FileMood

Download Machine Learning Specialization

Machine Learning Specialization

Name

Machine Learning Specialization

 DOWNLOAD Copy Link

Total Size

14.1 GB

Total Files

2880

Last Seen

2024-11-15 23:41

Hash

1C6FE20E31AFAB2F041D8BC4299B968B95EEE660

/.../04_conversations-with-andrew-optional/

01_andrew-ng-and-chris-manning-on-natural-language-processing.mp4

247.6 MB

01_andrew-ng-and-chris-manning-on-natural-language-processing.en.txt

40.8 KB

01_andrew-ng-and-chris-manning-on-natural-language-processing.en.srt

65.5 KB

/

TutsNode.net.txt

0.1 KB

[TGx]Downloaded from torrentgalaxy.to .txt

0.6 KB

/.../01_neural-networks-learning-very-non-linear-features/

01_slides-presented-in-this-module_LM-3dtexton.pdf

1.9 MB

01_slides-presented-in-this-module_eccv06.pdf

723.4 KB

02_searching-for-images-a-case-study-in-deep-learning.mp4

1.8 MB

01_slides-presented-in-this-module_instructions.html

3.2 KB

01_slides-presented-in-this-module_imagenet.pdf

1.4 MB

01_slides-presented-in-this-module_iccv99.pdf

577.9 KB

01_slides-presented-in-this-module_Dalal-cvpr05.pdf

456.1 KB

04_learning-very-non-linear-features-with-neural-networks.en.srt

13.9 KB

04_learning-very-non-linear-features-with-neural-networks.en.txt

8.6 KB

04_learning-very-non-linear-features-with-neural-networks.mp4

34.4 MB

03_what-is-a-visual-product-recommender.en.srt

5.8 KB

03_what-is-a-visual-product-recommender.en.txt

3.5 KB

02_searching-for-images-a-case-study-in-deep-learning.en.srt

0.6 KB

02_searching-for-images-a-case-study-in-deep-learning.en.txt

0.3 KB

03_what-is-a-visual-product-recommender.mp4

17.5 MB

01_slides-presented-in-this-module_deeplearning-annotated.pdf

11.4 MB

01_slides-presented-in-this-module_johnson_andrew_1997_3.pdf

9.3 MB

01_slides-presented-in-this-module_mikolajczyk_pami05.pdf

2.1 MB

/.../01_neural-networks-intuition/

01_welcome.en.txt

2.9 KB

03_demand-prediction.en.srt

27.2 KB

02_neurons-and-the-brain.en.srt

18.7 KB

03_demand-prediction.en.txt

14.3 KB

04_example-recognizing-images.en.srt

10.4 KB

02_neurons-and-the-brain.en.txt

9.7 KB

01_welcome.en.srt

5.5 KB

04_example-recognizing-images.en.txt

5.5 KB

02_neurons-and-the-brain.mp4

28.2 MB

03_demand-prediction.mp4

25.4 MB

04_example-recognizing-images.mp4

15.3 MB

01_welcome.mp4

11.2 MB

/.../06_programming-assignment/

01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy.argsort.html

39.8 KB

01_predicting-house-prices-using-k-nearest-neighbors-regression_home_data_small.sframe.zip

396.1 KB

01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_train.csv.zip

175.8 KB

01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small.csv.zip

275.7 KB

01_predicting-house-prices-using-k-nearest-neighbors-regression_instructions.html

20.4 KB

01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_test.csv.zip

56.5 KB

01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_validation.csv.zip

46.8 KB

01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy.ndarray.shape.html

7.4 KB

01_predicting-house-prices-using-k-nearest-neighbors-regression_REG06-NB01.ipynb.zip

5.4 KB

01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy-tutorial-py3.ipynb.zip

3.0 KB

/.../01_defining-how-we-assess-performance/

02_assessing-performance-intro.en.txt

0.5 KB

02_assessing-performance-intro.mp4

1.9 MB

02_assessing-performance-intro.en.srt

0.8 KB

03_what-do-we-mean-by-loss.en.srt

6.3 KB

03_what-do-we-mean-by-loss.en.txt

3.9 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

03_what-do-we-mean-by-loss.mp4

14.5 MB

01_slides-presented-in-this-module_week3_assessingperformance-annotated.pdf

7.2 MB

/.pad/

0

0.0 KB

1

0.0 KB

2

0.0 KB

3

0.0 KB

4

0.0 KB

5

0.0 KB

6

0.0 KB

7

0.0 KB

8

0.0 KB

9

0.0 KB

10

0.0 KB

11

0.0 KB

12

0.0 KB

13

0.1 KB

14

0.0 KB

15

0.0 KB

16

0.0 KB

17

0.0 KB

18

0.0 KB

19

0.0 KB

20

0.0 KB

21

0.0 KB

22

0.0 KB

23

0.0 KB

24

0.1 KB

25

0.1 KB

26

0.0 KB

27

0.0 KB

28

0.0 KB

29

0.0 KB

30

0.0 KB

31

0.0 KB

32

0.0 KB

33

0.0 KB

34

0.0 KB

35

0.0 KB

36

0.0 KB

37

0.0 KB

38

0.0 KB

39

0.0 KB

40

0.0 KB

41

0.0 KB

42

0.1 KB

43

0.2 KB

44

0.0 KB

45

0.0 KB

46

0.0 KB

47

0.1 KB

48

0.1 KB

49

0.0 KB

50

0.1 KB

51

0.0 KB

52

0.1 KB

53

0.0 KB

54

63.9 KB

55

2.0 MB

56

902.4 KB

57

1.0 MB

58

1.1 MB

59

1.2 MB

60

2.1 MB

61

33.5 KB

62

49.0 KB

63

97.9 KB

64

395.5 KB

65

459.1 KB

66

517.2 KB

67

719.4 KB

68

770.4 KB

69

985.6 KB

70

1.0 MB

71

1.0 MB

72

1.1 MB

73

1.4 MB

74

1.4 MB

75

1.7 MB

76

2.0 MB

77

2.1 MB

78

2.1 MB

79

752.0 KB

80

997.9 KB

81

1.1 MB

82

1.2 MB

83

1.4 MB

84

1.4 MB

85

1.4 MB

86

1.8 MB

87

2.0 MB

88

2.0 MB

89

16.7 KB

90

59.0 KB

91

468.7 KB

92

661.5 KB

93

683.5 KB

94

684.1 KB

95

684.1 KB

96

684.1 KB

97

767.5 KB

98

1.1 MB

99

1.2 MB

100

1.2 MB

101

1.4 MB

102

1.5 MB

103

1.7 MB

104

1.7 MB

105

1.7 MB

106

1.9 MB

107

2.0 MB

108

35.2 KB

109

102.5 KB

110

241.3 KB

111

535.0 KB

112

643.7 KB

113

756.0 KB

114

851.7 KB

115

1.0 MB

116

1.1 MB

117

1.2 MB

118

1.4 MB

119

1.5 MB

120

1.6 MB

121

1.7 MB

122

1.7 MB

123

1.7 MB

124

1.8 MB

125

1.9 MB

126

1.9 MB

127

1.9 MB

128

1.9 MB

129

1.9 MB

130

1.9 MB

131

2.0 MB

132

2.0 MB

133

2.1 MB

134

136.6 KB

135

248.3 KB

136

251.8 KB

137

333.8 KB

138

387.9 KB

139

438.3 KB

140

460.9 KB

141

472.9 KB

142

497.9 KB

143

540.9 KB

144

550.3 KB

145

607.9 KB

146

628.9 KB

147

701.8 KB

148

775.1 KB

149

794.2 KB

150

858.4 KB

151

929.0 KB

152

967.3 KB

153

995.4 KB

154

1.0 MB

155

1.1 MB

156

1.1 MB

157

1.2 MB

158

1.2 MB

159

1.2 MB

160

1.2 MB

161

1.3 MB

162

1.3 MB

163

1.3 MB

164

1.4 MB

165

1.6 MB

166

1.6 MB

167

1.7 MB

168

1.7 MB

169

1.8 MB

170

1.8 MB

171

1.8 MB

172

1.8 MB

173

1.9 MB

174

1.9 MB

175

1.9 MB

176

2.0 MB

177

2.0 MB

178

30.9 KB

179

95.8 KB

180

108.8 KB

181

132.7 KB

182

165.3 KB

183

167.8 KB

184

171.3 KB

185

202.9 KB

186

236.1 KB

187

250.2 KB

188

297.5 KB

189

490.5 KB

190

506.6 KB

191

550.6 KB

192

562.0 KB

193

576.6 KB

194

599.1 KB

195

634.8 KB

196

644.8 KB

197

648.2 KB

198

649.2 KB

199

649.2 KB

200

649.2 KB

201

649.2 KB

202

649.2 KB

203

651.4 KB

204

651.4 KB

205

651.4 KB

206

651.4 KB

207

651.4 KB

208

673.4 KB

209

724.3 KB

210

734.4 KB

211

764.3 KB

212

926.3 KB

213

967.9 KB

214

970.0 KB

215

1.0 MB

216

1.1 MB

217

1.1 MB

218

1.1 MB

219

1.1 MB

220

1.1 MB

221

1.2 MB

222

1.2 MB

223

1.2 MB

224

1.2 MB

225

1.3 MB

226

1.3 MB

227

1.4 MB

228

1.5 MB

229

1.5 MB

230

1.5 MB

231

1.5 MB

232

1.5 MB

233

1.5 MB

234

1.5 MB

235

1.5 MB

236

1.5 MB

237

1.6 MB

238

1.6 MB

239

1.7 MB

240

1.7 MB

241

1.7 MB

242

1.7 MB

243

1.7 MB

244

1.7 MB

245

1.8 MB

246

1.9 MB

247

2.0 MB

248

5.6 KB

249

16.3 KB

250

142.3 KB

251

157.6 KB

252

167.4 KB

253

180.9 KB

254

214.9 KB

255

253.9 KB

256

328.9 KB

257

333.1 KB

258

480.0 KB

259

502.7 KB

260

510.7 KB

261

553.8 KB

262

580.8 KB

263

645.1 KB

264

708.8 KB

265

754.0 KB

266

766.4 KB

267

778.8 KB

268

789.8 KB

269

819.7 KB

270

833.0 KB

271

859.8 KB

272

942.1 KB

273

990.9 KB

274

1.0 MB

275

1.0 MB

276

1.0 MB

277

1.0 MB

278

1.1 MB

279

1.1 MB

280

1.1 MB

281

1.1 MB

282

1.2 MB

283

1.2 MB

284

1.2 MB

285

1.2 MB

286

1.2 MB

287

1.3 MB

288

1.3 MB

289

1.4 MB

290

1.4 MB

291

1.4 MB

292

1.4 MB

293

1.4 MB

294

1.5 MB

295

1.5 MB

296

1.5 MB

297

1.6 MB

298

1.7 MB

299

1.7 MB

300

1.7 MB

301

1.8 MB

302

1.8 MB

303

1.8 MB

304

1.8 MB

305

1.9 MB

306

1.9 MB

307

1.9 MB

308

2.0 MB

309

2.0 MB

310

2.1 MB

311

35.1 KB

312

112.6 KB

313

119.0 KB

314

122.0 KB

315

194.4 KB

316

227.0 KB

317

278.1 KB

318

323.0 KB

319

359.2 KB

320

447.5 KB

321

456.0 KB

322

475.1 KB

323

482.2 KB

324

526.6 KB

325

554.9 KB

326

568.6 KB

327

583.1 KB

328

585.1 KB

329

647.5 KB

330

684.6 KB

331

778.5 KB

332

806.7 KB

333

867.9 KB

334

900.7 KB

335

931.1 KB

336

934.2 KB

337

993.2 KB

338

1.0 MB

339

1.1 MB

340

1.2 MB

341

1.2 MB

342

1.2 MB

343

1.3 MB

344

1.3 MB

345

1.3 MB

346

1.3 MB

347

1.3 MB

348

1.4 MB

349

1.4 MB

350

1.4 MB

351

1.5 MB

352

1.5 MB

353

1.5 MB

354

1.6 MB

355

1.6 MB

356

1.7 MB

357

1.7 MB

358

1.7 MB

359

1.7 MB

360

1.7 MB

361

1.7 MB

362

1.7 MB

363

1.7 MB

364

1.7 MB

365

1.7 MB

366

1.8 MB

367

1.9 MB

368

1.9 MB

369

1.9 MB

370

1.9 MB

371

2.0 MB

372

2.0 MB

373

2.0 MB

374

2.0 MB

375

2.1 MB

376

15.4 KB

377

62.0 KB

378

138.3 KB

379

142.5 KB

380

152.6 KB

381

197.3 KB

382

199.0 KB

383

223.5 KB

384

237.4 KB

385

259.7 KB

386

276.0 KB

387

350.4 KB

388

355.7 KB

389

362.8 KB

390

379.8 KB

391

405.4 KB

392

460.0 KB

393

463.3 KB

394

508.9 KB

395

515.5 KB

396

546.4 KB

397

573.2 KB

398

612.2 KB

399

649.8 KB

400

720.8 KB

401

738.8 KB

402

799.6 KB

403

801.6 KB

404

820.6 KB

405

833.7 KB

406

839.2 KB

407

840.0 KB

408

849.3 KB

409

887.5 KB

410

890.8 KB

411

966.8 KB

412

997.8 KB

413

1.1 MB

414

1.1 MB

415

1.1 MB

416

1.1 MB

417

1.1 MB

418

1.1 MB

419

1.1 MB

420

1.2 MB

421

1.2 MB

422

1.3 MB

423

1.3 MB

424

1.3 MB

425

1.3 MB

426

1.3 MB

427

1.3 MB

428

1.3 MB

429

1.3 MB

430

1.3 MB

431

1.3 MB

432

1.4 MB

433

1.4 MB

434

1.4 MB

435

1.5 MB

436

1.5 MB

437

1.5 MB

438

1.5 MB

439

1.6 MB

440

1.6 MB

441

1.6 MB

442

1.6 MB

443

1.7 MB

444

1.7 MB

445

1.7 MB

446

1.8 MB

447

1.8 MB

448

1.8 MB

449

1.8 MB

450

1.9 MB

451

2.0 MB

452

2.0 MB

453

2.0 MB

454

2.0 MB

455

2.0 MB

456

2.0 MB

457

2.1 MB

458

2.1 MB

459

9.0 KB

460

26.9 KB

461

45.4 KB

462

85.7 KB

463

116.4 KB

464

116.9 KB

465

136.7 KB

466

137.0 KB

467

140.8 KB

468

158.4 KB

469

234.6 KB

470

269.6 KB

471

274.6 KB

472

301.3 KB

473

342.3 KB

474

394.9 KB

475

440.0 KB

476

444.9 KB

477

457.8 KB

478

460.5 KB

479

487.6 KB

480

491.8 KB

481

530.0 KB

482

580.9 KB

483

597.0 KB

484

628.0 KB

485

633.6 KB

486

636.6 KB

487

704.5 KB

488

708.0 KB

489

725.6 KB

490

743.2 KB

491

767.2 KB

492

773.0 KB

493

777.2 KB

494

789.6 KB

495

849.8 KB

496

861.6 KB

497

865.0 KB

498

877.3 KB

499

886.7 KB

500

898.6 KB

501

941.2 KB

502

946.4 KB

503

972.5 KB

504

1.1 MB

505

1.1 MB

506

1.1 MB

507

1.2 MB

508

1.2 MB

509

1.2 MB

510

1.2 MB

511

1.2 MB

512

1.2 MB

513

1.3 MB

514

1.4 MB

515

1.4 MB

516

1.4 MB

517

1.5 MB

518

1.5 MB

519

1.5 MB

520

1.5 MB

521

1.5 MB

522

1.6 MB

523

1.7 MB

524

1.7 MB

525

1.7 MB

526

1.7 MB

527

1.9 MB

528

1.9 MB

529

1.9 MB

530

1.9 MB

531

1.9 MB

532

2.0 MB

533

2.0 MB

534

15.2 KB

535

58.5 KB

536

76.9 KB

537

89.0 KB

538

102.7 KB

539

105.4 KB

540

106.8 KB

541

165.6 KB

542

208.7 KB

543

252.8 KB

544

373.9 KB

545

422.7 KB

546

455.8 KB

547

546.4 KB

548

553.9 KB

549

563.6 KB

550

609.0 KB

551

625.7 KB

552

649.2 KB

553

651.7 KB

554

667.1 KB

555

677.3 KB

556

701.5 KB

557

702.5 KB

558

776.2 KB

559

880.5 KB

560

880.5 KB

561

880.5 KB

562

971.8 KB

563

987.5 KB

564

1.0 MB

565

1.1 MB

566

1.1 MB

567

1.2 MB

568

1.2 MB

569

1.2 MB

570

1.2 MB

571

1.3 MB

572

1.3 MB

573

1.3 MB

574

1.3 MB

575

1.4 MB

576

1.4 MB

577

1.4 MB

578

1.5 MB

579

1.5 MB

580

1.5 MB

581

1.6 MB

582

1.7 MB

583

1.7 MB

584

1.7 MB

585

1.8 MB

586

1.8 MB

587

1.9 MB

588

2.0 MB

589

2.1 MB

590

3.8 KB

591

65.6 KB

592

136.4 KB

593

140.9 KB

594

148.1 KB

595

160.0 KB

596

219.8 KB

597

264.3 KB

598

280.3 KB

599

313.9 KB

600

363.8 KB

601

370.0 KB

602

580.1 KB

603

609.6 KB

604

699.5 KB

605

706.3 KB

606

783.0 KB

607

857.7 KB

608

928.0 KB

609

950.7 KB

610

986.6 KB

611

1.0 MB

612

1.1 MB

613

1.1 MB

614

1.1 MB

615

1.2 MB

616

1.2 MB

617

1.2 MB

618

1.3 MB

619

1.3 MB

620

1.4 MB

621

1.4 MB

622

1.5 MB

623

1.7 MB

624

1.7 MB

625

1.8 MB

626

1.9 MB

627

1.9 MB

628

1.9 MB

629

1.9 MB

630

1.9 MB

631

1.9 MB

632

1.9 MB

633

2.0 MB

634

2.0 MB

635

2.1 MB

636

82.0 KB

637

90.1 KB

638

107.5 KB

639

110.6 KB

640

130.6 KB

641

201.2 KB

642

226.3 KB

643

237.2 KB

644

310.9 KB

645

675.9 KB

646

700.4 KB

647

802.0 KB

648

840.4 KB

649

936.9 KB

650

993.5 KB

651

1.0 MB

652

1.0 MB

653

1.0 MB

654

1.0 MB

655

1.0 MB

656

1.1 MB

657

1.2 MB

658

1.2 MB

659

1.2 MB

660

1.3 MB

661

1.4 MB

662

1.4 MB

663

1.4 MB

664

1.4 MB

665

1.4 MB

666

1.4 MB

667

1.5 MB

668

1.6 MB

669

1.6 MB

670

1.7 MB

671

1.7 MB

672

2.1 MB

673

96.0 KB

674

174.2 KB

675

179.5 KB

676

197.3 KB

677

278.1 KB

678

317.7 KB

679

317.7 KB

680

317.7 KB

681

317.7 KB

682

367.1 KB

683

597.4 KB

684

642.0 KB

685

716.9 KB

686

794.0 KB

687

943.7 KB

688

1.0 MB

689

1.0 MB

690

1.2 MB

691

1.2 MB

692

1.2 MB

693

1.3 MB

694

1.5 MB

695

1.6 MB

696

1.9 MB

697

2.0 MB

/.../05_conversations-with-andrew-optional/

01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.mp4

241.9 MB

01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.txt

32.5 KB

01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.srt

51.8 KB

/.../04_scaling-up-k-nn-search-using-kd-trees/

07_optional-a-worked-out-example-for-kd-trees_instructions.html

1.3 MB

02_kd-tree-representation.en.srt

10.4 KB

06_approximate-k-nn-search-using-kd-trees.en.srt

10.0 KB

03_nn-search-with-kd-trees.en.srt

8.7 KB

01_complexity-of-brute-force-search.en.srt

2.7 KB

01_complexity-of-brute-force-search.en.txt

1.7 KB

04_complexity-of-nn-search-with-kd-trees.en.srt

6.6 KB

02_kd-tree-representation.en.txt

6.5 KB

06_approximate-k-nn-search-using-kd-trees.en.txt

6.3 KB

05_visualizing-scaling-behavior-of-kd-trees.en.srt

5.5 KB

03_nn-search-with-kd-trees.en.txt

5.0 KB

04_complexity-of-nn-search-with-kd-trees.en.txt

4.1 KB

05_visualizing-scaling-behavior-of-kd-trees.en.txt

3.5 KB

06_approximate-k-nn-search-using-kd-trees.mp4

24.1 MB

02_kd-tree-representation.mp4

23.7 MB

03_nn-search-with-kd-trees.mp4

17.0 MB

04_complexity-of-nn-search-with-kd-trees.mp4

14.2 MB

05_visualizing-scaling-behavior-of-kd-trees.mp4

10.6 MB

01_complexity-of-brute-force-search.mp4

6.2 MB

/.../06_programming-assignment/

01_predicting-sentiment-from-product-reviews_sklearn.linear_model.LogisticRegression.html

84.6 KB

01_predicting-sentiment-from-product-reviews_amazon_baby.gl.zip

42.3 MB

01_predicting-sentiment-from-product-reviews_amazon_baby.sframe.zip

42.3 MB

01_predicting-sentiment-from-product-reviews_module-2-assignment-train-idx.json.zip

167.8 KB

01_predicting-sentiment-from-product-reviews_module-2-assignment-test-idx.json.zip

47.8 KB

01_predicting-sentiment-from-product-reviews_instructions.html

25.5 KB

01_predicting-sentiment-from-product-reviews_CLA02-NB01.ipynb.zip

6.6 KB

01_predicting-sentiment-from-product-reviews_amazon_baby.csv.zip

30.1 MB

/.../10_conversations-with-andrew-optional/

01_andrew-ng-and-fei-fei-li-on-human-centered-ai.mp4

225.1 MB

01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en.srt

61.1 KB

01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en.txt

31.4 KB

/.../03_summary-of-deep-learning/

02_deep-learning_exam.html

1.3 MB

01_deep-learning-ml-block-diagram.en.srt

4.1 KB

01_deep-learning-ml-block-diagram.en.txt

2.5 KB

01_deep-learning-ml-block-diagram.mp4

11.7 MB

/.../07_programming-assignment-2/

01_clustering-text-data-with-gaussian-mixtures_instructions.html

21.8 KB

01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.json.zip

854.3 KB

01_clustering-text-data-with-gaussian-mixtures_people_wiki.sframe.zip

59.0 MB

01_clustering-text-data-with-gaussian-mixtures_people_wiki.gl.zip

58.3 MB

01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.gl.zip

638.6 KB

01_clustering-text-data-with-gaussian-mixtures_people_wiki.csv.zip

41.8 MB

01_clustering-text-data-with-gaussian-mixtures_sklearn.feature_extraction.text.TfidfVectorizer.html

63.1 KB

01_clustering-text-data-with-gaussian-mixtures_CLU04-NB02.ipynb.zip

5.6 KB

01_clustering-text-data-with-gaussian-mixtures_em_utilities.py.zip

2.9 KB

01_clustering-text-data-with-gaussian-mixtures_4_tf_idf.npz.zip

3.4 MB

/.../07_programming-assignment/

01_recommending-songs-assignment_song_data.csv

156.4 MB

01_recommending-songs-assignment_song_data.sframe.zip

50.3 MB

01_recommending-songs-assignment_graphlab.SFrame.groupby.html

288.2 KB

01_recommending-songs-assignment_FND05-NB01.ipynb.zip

15.5 KB

01_recommending-songs-assignment_instructions.html

10.9 KB

02_recommending-songs_exam.html

3.4 KB

/.../07_programming-assignment-2/

01_implementing-binary-decision-trees_instructions.html

31.5 KB

01_implementing-binary-decision-trees_module-5-assignment-2-train-idx.json.zip

56.4 KB

01_implementing-binary-decision-trees_module-5-assignment-2-test-idx.json.zip

19.4 KB

01_implementing-binary-decision-trees_CLA05-NB02.ipynb.zip

8.4 KB

01_implementing-binary-decision-trees_module-5-decision-tree-assignment-2-blank.ipynb.zip

7.9 KB

01_implementing-binary-decision-trees_lending-club-data.gl.zip

20.3 MB

01_implementing-binary-decision-trees_lending-club-data.sframe.zip

20.3 MB

01_implementing-binary-decision-trees_lending-club-data.csv.zip

19.4 MB

/.../05_programming-assignment/

01_retrieving-wikipedia-articles-assignment_people_wiki.csv

116.2 MB

01_retrieving-wikipedia-articles-assignment_people_wiki.sframe.zip

59.0 MB

01_retrieving-wikipedia-articles-assignment_FND04-NB01.ipynb.zip

11.9 KB

01_retrieving-wikipedia-articles-assignment_turicreate.toolkits.distances.cosine.html

11.1 KB

01_retrieving-wikipedia-articles-assignment_instructions.html

9.3 KB

02_retrieving-wikipedia-articles_exam.html

8.5 KB

/.../04_predicting-house-prices-jupyter-notebook/

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_home_data.sframe.zip

930.0 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND02-NB01.ipynb.zip

68.9 KB

11_applying-learned-models-to-predict-price-of-two-fancy-houses.en.srt

8.4 KB

02_loading-exploring-house-sale-data.en.srt

8.1 KB

08_exploring-other-features-of-the-data.en.srt

7.6 KB

10_applying-learned-models-to-predict-price-of-an-average-house.en.srt

5.6 KB

06_visualizing-predictions-of-simple-model-with-matplotlib.en.srt

5.5 KB

02_loading-exploring-house-sale-data.en.txt

5.0 KB

11_applying-learned-models-to-predict-price-of-two-fancy-houses.en.txt

4.7 KB

08_exploring-other-features-of-the-data.en.txt

4.5 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html

2.6 KB

03_splitting-the-data-into-training-and-test-sets.en.srt

3.0 KB

03_splitting-the-data-into-training-and-test-sets.en.txt

1.8 KB

04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.en.srt

4.4 KB

04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.en.txt

2.6 KB

05_evaluating-error-rmse-of-the-simple-model.en.srt

2.7 KB

05_evaluating-error-rmse-of-the-simple-model.en.txt

1.5 KB

07_inspecting-the-model-coefficients-learned.en.srt

1.6 KB

07_inspecting-the-model-coefficients-learned.en.txt

0.9 KB

09_learning-a-model-to-predict-house-prices-from-more-features.en.txt

2.2 KB

09_learning-a-model-to-predict-house-prices-from-more-features.en.srt

3.7 KB

10_applying-learned-models-to-predict-price-of-an-average-house.en.txt

3.3 KB

06_visualizing-predictions-of-simple-model-with-matplotlib.en.txt

3.3 KB

11_applying-learned-models-to-predict-price-of-two-fancy-houses.mp4

23.5 MB

02_loading-exploring-house-sale-data.mp4

19.3 MB

08_exploring-other-features-of-the-data.mp4

14.3 MB

10_applying-learned-models-to-predict-price-of-an-average-house.mp4

13.3 MB

06_visualizing-predictions-of-simple-model-with-matplotlib.mp4

12.4 MB

04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.mp4

8.9 MB

09_learning-a-model-to-predict-house-prices-from-more-features.mp4

8.1 MB

03_splitting-the-data-into-training-and-test-sets.mp4

6.5 MB

05_evaluating-error-rmse-of-the-simple-model.mp4

6.5 MB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_house_images.zip

4.9 MB

07_inspecting-the-model-coefficients-learned.mp4

3.6 MB

/.../06_programming-assignment/

01_deep-features-for-image-retrieval-assignment_nearest_neighbors.html

249.1 KB

01_deep-features-for-image-retrieval-assignment_image_test_data.csv

108.0 MB

01_deep-features-for-image-retrieval-assignment_tabular-data.html

249.1 KB

01_deep-features-for-image-retrieval-assignment_instructions.html

358.8 KB

01_deep-features-for-image-retrieval-assignment_turicreate.Sketch.html

28.9 KB

01_deep-features-for-image-retrieval-assignment_image_train_data.csv

54.1 MB

01_deep-features-for-image-retrieval-assignment_nearest_neighbors.md

180.3 KB

01_deep-features-for-image-retrieval-assignment_image_test_data.zip

53.4 MB

02_deep-features-for-image-retrieval_exam.html

169.1 KB

01_deep-features-for-image-retrieval-assignment_image_train_data.zip

26.6 MB

/.../04_optional-advanced-material-formally-defining-and-deriving-the-3-sources-of-error/

02_formally-deriving-why-3-sources-of-error.en.srt

22.3 KB

02_formally-deriving-why-3-sources-of-error.mp4

49.0 MB

01_formally-defining-the-3-sources-of-error.mp4

47.4 MB

01_formally-defining-the-3-sources-of-error.en.srt

19.8 KB

02_formally-deriving-why-3-sources-of-error.en.txt

13.4 KB

01_formally-defining-the-3-sources-of-error.en.txt

12.4 KB

/.../06_discussion-and-summary-of-simple-linear-regression/

06_a-brief-recap.en.srt

1.6 KB

01_download-notebooks-to-follow-along_PhillyCrime.ipynb.zip

46.0 KB

03_influence-of-high-leverage-points-removing-center-city.en.srt

9.4 KB

03_influence-of-high-leverage-points-removing-center-city.en.txt

5.8 KB

02_influence-of-high-leverage-points-exploring-the-data.en.srt

5.8 KB

05_asymmetric-cost-functions.en.srt

4.5 KB

04_influence-of-high-leverage-points-removing-high-end-towns.en.srt

4.0 KB

02_influence-of-high-leverage-points-exploring-the-data.en.txt

3.7 KB

05_asymmetric-cost-functions.en.txt

2.7 KB

04_influence-of-high-leverage-points-removing-high-end-towns.en.txt

2.6 KB

01_download-notebooks-to-follow-along_Philadelphia_Crime_Rate_noNA.csv.zip

2.2 KB

01_download-notebooks-to-follow-along_instructions.html

1.8 KB

06_a-brief-recap.en.txt

1.0 KB

03_influence-of-high-leverage-points-removing-center-city.mp4

20.5 MB

02_influence-of-high-leverage-points-exploring-the-data.mp4

12.5 MB

05_asymmetric-cost-functions.mp4

9.9 MB

04_influence-of-high-leverage-points-removing-high-end-towns.mp4

9.8 MB

06_a-brief-recap.mp4

4.7 MB

/.../05_programming-assignment/

01_predicting-house-prices-assignment_home_data.sframe.zip

930.0 KB

01_predicting-house-prices-assignment_graphlab.SFrame.html

288.2 KB

01_predicting-house-prices-assignment_FND02-NB01.ipynb.zip

68.9 KB

01_predicting-house-prices-assignment_instructions.html

30.1 KB

02_predicting-house-prices_exam.html

4.3 KB

01_predicting-house-prices-assignment_house_images.zip

4.9 MB

01_predicting-house-prices-assignment_home_data.csv

2.6 MB

/.../03_summary-of-clustering-and-similarity/

02_clustering-and-similarity_exam.html

111.8 KB

01_clustering-and-similarity-ml-block-diagram.en.srt

8.9 KB

01_clustering-and-similarity-ml-block-diagram.en.txt

5.5 KB

01_clustering-and-similarity-ml-block-diagram.mp4

19.8 MB

/.../02_mixtures-of-gaussians-for-clustering/

03_scaling-mixtures-of-gaussians-for-document-clustering.en.srt

7.2 KB

01_mixture-of-gaussians.en.srt

9.0 KB

02_interpreting-the-mixture-of-gaussian-terms.en.srt

7.0 KB

01_mixture-of-gaussians.en.txt

5.7 KB

03_scaling-mixtures-of-gaussians-for-document-clustering.en.txt

4.7 KB

02_interpreting-the-mixture-of-gaussian-terms.en.txt

4.3 KB

01_mixture-of-gaussians.mp4

22.1 MB

03_scaling-mixtures-of-gaussians-for-document-clustering.mp4

18.0 MB

02_interpreting-the-mixture-of-gaussian-terms.mp4

15.1 MB

/.../05_programming-assignment/

01_modeling-text-topics-with-latent-dirichlet-allocation_topic_models.zip

91.9 MB

01_modeling-text-topics-with-latent-dirichlet-allocation_people_wiki.sframe.zip

59.0 MB

01_modeling-text-topics-with-latent-dirichlet-allocation_CLU05-NB01.ipynb.zip

9.2 KB

01_modeling-text-topics-with-latent-dirichlet-allocation_instructions.html

5.7 KB

/.../06_programming-assignment-2/

01_implementing-locality-sensitive-hashing-from-scratch_instructions.html

335.9 KB

01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.sframe.zip

59.0 MB

01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.gl.zip

58.3 MB

01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_tf_idf.npz.zip

53.4 MB

01_implementing-locality-sensitive-hashing-from-scratch_itertools.html

165.1 KB

01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.csv.zip

41.8 MB

01_implementing-locality-sensitive-hashing-from-scratch_sklearn.feature_extraction.text.TfidfVectorizer.html

63.1 KB

01_implementing-locality-sensitive-hashing-from-scratch_CLU02-NB02.ipynb.zip

10.3 KB

01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_map_index_to_word.json.zip

5.3 MB

01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_map_index_to_word.gl.zip

3.9 MB

/.../02_supervised-vs-unsupervised-machine-learning/

01_what-is-machine-learning.en.srt

9.7 KB

04_unsupervised-learning-part-1.en.srt

11.9 KB

02_supervised-learning-part-1.en.srt

11.0 KB

03_supervised-learning-part-2.en.srt

9.1 KB

04_unsupervised-learning-part-1.en.txt

7.5 KB

06_jupyter-notebooks.en.srt

7.5 KB

05_unsupervised-learning-part-2.en.srt

5.9 KB

02_supervised-learning-part-1.en.txt

5.8 KB

03_supervised-learning-part-2.en.txt

5.6 KB

01_what-is-machine-learning.en.txt

5.1 KB

06_jupyter-notebooks.en.txt

4.0 KB

05_unsupervised-learning-part-2.en.txt

3.2 KB

01_what-is-machine-learning.mp4

27.2 MB

06_jupyter-notebooks.mp4

20.9 MB

04_unsupervised-learning-part-1.mp4

19.6 MB

03_supervised-learning-part-2.mp4

15.1 MB

02_supervised-learning-part-1.mp4

14.5 MB

05_unsupervised-learning-part-2.mp4

8.6 MB

/.../05_programming-assignment/

01_analyzing-product-sentiment-assignment_amazon_baby.csv

89.4 MB

01_analyzing-product-sentiment-assignment_amazon_baby.sframe.zip

42.3 MB

01_analyzing-product-sentiment-assignment_datastructures.html

95.4 KB

01_analyzing-product-sentiment-assignment_instructions.html

17.7 KB

01_analyzing-product-sentiment-assignment_FND03-NB01.ipynb.zip

13.4 KB

01_analyzing-product-sentiment-assignment_turicreate.SArray.apply.html

13.0 KB

02_analyzing-product-sentiment_exam.html

12.5 KB

/.../03_content-based-filtering/

04_ethical-use-of-recommender-systems.en.txt

9.2 KB

04_ethical-use-of-recommender-systems.en.srt

17.3 KB

01_collaborative-filtering-vs-content-based-filtering.en.srt

14.7 KB

02_deep-learning-for-content-based-filtering.en.srt

14.5 KB

03_recommending-from-a-large-catalogue.en.srt

10.4 KB

01_collaborative-filtering-vs-content-based-filtering.en.txt

7.6 KB

02_deep-learning-for-content-based-filtering.en.txt

7.6 KB

05_tensorflow-implementation-of-content-based-filtering.en.srt

7.5 KB

03_recommending-from-a-large-catalogue.en.txt

6.5 KB

05_tensorflow-implementation-of-content-based-filtering.en.txt

4.0 KB

04_ethical-use-of-recommender-systems.mp4

26.0 MB

02_deep-learning-for-content-based-filtering.mp4

25.5 MB

01_collaborative-filtering-vs-content-based-filtering.mp4

20.9 MB

03_recommending-from-a-large-catalogue.mp4

18.9 MB

05_tensorflow-implementation-of-content-based-filtering.mp4

13.6 MB

/.../03_programming-assignment-1/

01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.sframe.zip

59.0 MB

01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.gl.zip

58.3 MB

01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_tf_idf.npz.zip

53.4 MB

01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.metrics.pairwise.euclidean_distances.html

22.3 KB

01_choosing-features-and-metrics-for-nearest-neighbor-search_stdtypes.html

305.1 KB

01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.CountVectorizer.html

58.9 KB

01_choosing-features-and-metrics-for-nearest-neighbor-search_graphlab.SFrame.join.html

288.2 KB

01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.csv.zip

41.8 MB

01_choosing-features-and-metrics-for-nearest-neighbor-search_instructions.html

126.5 KB

01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.TfidfVectorizer.html

63.1 KB

01_choosing-features-and-metrics-for-nearest-neighbor-search_CLU02-NB01.ipynb.zip

7.8 KB

01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_word_count.npz.zip

24.3 MB

01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.json.zip

5.3 MB

01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.gl.zip

3.9 MB

/.../04_programming-assignment-1/

01_exploring-ensemble-methods_sklearn.ensemble.GradientBoostingClassifier.html

85.4 KB

01_exploring-ensemble-methods_instructions.html

32.0 KB

01_exploring-ensemble-methods_boosted_trees_classifier.html

290.4 KB

01_exploring-ensemble-methods_graphlab.SFrame.to_numpy.html

290.4 KB

01_exploring-ensemble-methods_module-8-assignment-1-train-idx.json.zip

56.4 KB

01_exploring-ensemble-methods_module-8-assignment-1-validation-idx.json.zip

19.4 KB

01_exploring-ensemble-methods_CLA08-NB01.ipynb.zip

7.3 KB

01_exploring-ensemble-methods_lending-club-data.gl.zip

20.3 MB

01_exploring-ensemble-methods_lending-club-data.sframe.zip

20.3 MB

01_exploring-ensemble-methods_lending-club-data.csv.zip

19.4 MB

/.../01_basic-strategies-for-handling-missing-data/

01_slides-presented-in-this-module_decision-trees-missing-values-annotated.pdf

1.6 MB

01_slides-presented-in-this-module_instructions.html

1.2 KB

04_strategy-2-purification-by-imputing-missing-data.en.srt

6.8 KB

02_challenge-of-missing-data.en.srt

5.7 KB

03_strategy-1-purification-by-skipping-missing-data.en.srt

5.4 KB

04_strategy-2-purification-by-imputing-missing-data.en.txt

4.2 KB

02_challenge-of-missing-data.en.txt

3.6 KB

03_strategy-1-purification-by-skipping-missing-data.en.txt

3.4 KB

04_strategy-2-purification-by-imputing-missing-data.mp4

16.7 MB

03_strategy-1-purification-by-skipping-missing-data.mp4

13.5 MB

02_challenge-of-missing-data.mp4

13.2 MB

/.../01_characteristics-of-overfit-models/

03_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip

203.2 KB

04_overfitting-demo.en.srt

8.7 KB

04_overfitting-demo.en.txt

5.5 KB

05_overfitting-for-more-general-multiple-regression-models.en.srt

5.2 KB

05_overfitting-for-more-general-multiple-regression-models.en.txt

3.2 KB

02_symptoms-of-overfitting-in-polynomial-regression.en.srt

3.1 KB

02_symptoms-of-overfitting-in-polynomial-regression.en.txt

2.0 KB

03_download-the-notebook-and-follow-along_instructions.html

1.5 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

04_overfitting-demo.mp4

17.8 MB

05_overfitting-for-more-general-multiple-regression-models.mp4

11.9 MB

02_symptoms-of-overfitting-in-polynomial-regression.mp4

7.2 MB

01_slides-presented-in-this-module_week4_ridgeregression-annotated.pdf

3.2 MB

/.../05_summarizing-overfitting-regularization-in-logistic-regression/

01_recap-of-overfitting-regularization-in-logistic-regression.en.srt

1.3 KB

01_recap-of-overfitting-regularization-in-logistic-regression.en.txt

0.8 KB

01_recap-of-overfitting-regularization-in-logistic-regression.mp4

4.0 MB

/.../03_programming-assignment/

01_clustering-text-data-with-k-means_people_wiki.sframe.zip

59.0 MB

01_clustering-text-data-with-k-means_people_wiki.gl.zip

58.3 MB

01_clustering-text-data-with-k-means_instructions.html

80.7 KB

01_clustering-text-data-with-k-means_sklearn.preprocessing.normalize.html

18.6 KB

01_clustering-text-data-with-k-means_people_wiki_tf_idf.npz.zip

53.4 MB

01_clustering-text-data-with-k-means_kmeans-arrays.npz.zip

49.9 MB

01_clustering-text-data-with-k-means_people_wiki.csv.zip

41.8 MB

01_clustering-text-data-with-k-means_sklearn.feature_extraction.text.TfidfVectorizer.html

63.1 KB

01_clustering-text-data-with-k-means_sklearn.cluster.KMeans.html

62.2 KB

01_clustering-text-data-with-k-means_CLU03-NB01.ipynb.zip

14.3 KB

01_clustering-text-data-with-k-means_numpy.mean.html

12.5 KB

01_clustering-text-data-with-k-means_numpy.argmin.html

9.5 KB

01_clustering-text-data-with-k-means_pyplot_api.html

0.3 KB

01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.json.zip

5.3 MB

01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.gl.zip

3.9 MB

/.../03_k-nearest-neighbors-and-weighted-k-nearest-neighbors/

02_k-nearest-neighbors-in-practice.en.txt

3.2 KB

01_k-nearest-neighbors-regression.en.srt

9.5 KB

01_k-nearest-neighbors-regression.en.txt

6.0 KB

03_weighted-k-nearest-neighbors.en.srt

5.7 KB

02_k-nearest-neighbors-in-practice.en.srt

5.0 KB

03_weighted-k-nearest-neighbors.en.txt

3.7 KB

01_k-nearest-neighbors-regression.mp4

20.2 MB

03_weighted-k-nearest-neighbors.mp4

14.5 MB

02_k-nearest-neighbors-in-practice.mp4

12.2 MB

/.../01_multiple-features-of-one-input/

02_multiple-regression-intro.mp4

1.3 MB

04_modeling-seasonality.en.srt

10.9 KB

02_multiple-regression-intro.en.srt

0.6 KB

04_modeling-seasonality.en.txt

6.9 KB

03_polynomial-regression.en.srt

5.4 KB

05_where-we-see-seasonality.en.srt

4.9 KB

03_polynomial-regression.en.txt

3.4 KB

06_regression-with-general-features-of-1-input.en.srt

3.2 KB

05_where-we-see-seasonality.en.txt

3.2 KB

06_regression-with-general-features-of-1-input.en.txt

2.0 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_multiple-regression-intro.en.txt

0.4 KB

04_modeling-seasonality.mp4

31.5 MB

03_polynomial-regression.mp4

12.1 MB

05_where-we-see-seasonality.mp4

11.8 MB

01_slides-presented-in-this-module_week2_multipleregression-annotated.pdf

10.6 MB

06_regression-with-general-features-of-1-input.mp4

9.0 MB

/.../03_getting-started-with-the-tools-for-the-course/

01_getting-started-with-python-jupyter-notebook-turi-create_Turi_Getting_Started_with_SFrames.ipynb.zip

1.8 KB

01_getting-started-with-python-jupyter-notebook-turi-create_people-example.csv

0.2 KB

02_where-should-my-files-go_instructions.html

775.5 KB

01_getting-started-with-python-jupyter-notebook-turi-create_instructions.html

13.5 KB

01_getting-started-with-python-jupyter-notebook-turi-create_Getting_started_with_Jupyter_Notebook.ipynb.zip

2.0 KB

03_important-changes-from-previous-courses_instructions.html

2.7 KB

/.../06_programming-assignment/

01_logistic-regression-with-l2-regularization_module-4-assignment-numpy-arrays.npz.zip

1.2 MB

01_logistic-regression-with-l2-regularization_instructions.html

92.6 KB

01_logistic-regression-with-l2-regularization_module-4-assignment-train-idx.json.zip

48.3 KB

01_logistic-regression-with-l2-regularization_module-4-assignment-validation-idx.json.zip

17.3 KB

01_logistic-regression-with-l2-regularization_important_words.json.zip

0.9 KB

01_logistic-regression-with-l2-regularization_CLA04-NB01.ipynb.zip

7.6 KB

01_logistic-regression-with-l2-regularization_module-4-linear-classifier-regularization-assignment-blank.ipynb.zip

7.2 KB

01_logistic-regression-with-l2-regularization_amazon_baby_subset.sframe.zip

13.3 MB

01_logistic-regression-with-l2-regularization_amazon_baby_subset.gl.zip

13.3 MB

01_logistic-regression-with-l2-regularization_amazon_baby_subset.csv.zip

9.6 MB

/.../05_programming-assignment-1/

01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_train_valid_shuffled.csv.zip

624.6 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_train_data.csv.zip

363.2 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_test_data.csv.zip

83.7 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_valid_data.csv.zip

358.4 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_home_data.sframe.zip

930.0 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_kc_house_data.csv.zip

799.4 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_3_data.csv.zip

162.5 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_1_data.csv.zip

162.5 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_4_data.csv.zip

162.1 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_2_data.csv.zip

161.4 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_instructions.html

20.2 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_REG04-NB01.ipynb.zip

6.0 KB

01_observing-effects-of-l2-penalty-in-polynomial-regression_numpy-tutorial-py3.ipynb.zip

3.0 KB

/.../04_tying-up-the-loose-ends/

04_a-brief-recap.en.txt

1.5 KB

03_how-to-handle-the-intercept.en.srt

7.8 KB

02_k-fold-cross-validation.en.srt

7.5 KB

01_selecting-tuning-parameters-via-cross-validation.en.srt

5.3 KB

03_how-to-handle-the-intercept.en.txt

4.8 KB

02_k-fold-cross-validation.en.txt

4.8 KB

01_selecting-tuning-parameters-via-cross-validation.en.txt

3.3 KB

04_a-brief-recap.en.srt

2.4 KB

03_how-to-handle-the-intercept.mp4

19.5 MB

02_k-fold-cross-validation.mp4

17.8 MB

01_selecting-tuning-parameters-via-cross-validation.mp4

13.0 MB

04_a-brief-recap.mp4

5.5 MB

/.../03_programming-assignment/

01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.sframe.zip

59.0 MB

01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.gl.zip

58.3 MB

01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_tf_idf.npz.zip

53.4 MB

01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.csv.zip

41.8 MB

01_modeling-text-data-with-a-hierarchy-of-clusters_sklearn.feature_extraction.text.TfidfVectorizer.html

63.1 KB

01_modeling-text-data-with-a-hierarchy-of-clusters_instructions.html

21.0 KB

01_modeling-text-data-with-a-hierarchy-of-clusters_CLU06-NB01.ipynb.zip

5.6 KB

01_modeling-text-data-with-a-hierarchy-of-clusters_em_utilities.py.zip

2.9 KB

01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_map_index_to_word.json.zip

5.3 MB

01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_map_index_to_word.gl.zip

3.9 MB

/.../07_programming-assignment/

01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-assignment-numpy-arrays.npz.zip

1.2 MB

01_training-logistic-regression-via-stochastic-gradient-ascent_instructions.html

129.4 KB

01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-assignment-train-idx.json.zip

54.3 KB

01_training-logistic-regression-via-stochastic-gradient-ascent_CLA10-NB01.ipynb.zip

13.0 KB

01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-assignment-validation-idx.json.zip

10.5 KB

01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-online-learning-assignment-blank.ipynb.zip

9.0 KB

01_training-logistic-regression-via-stochastic-gradient-ascent_important_words.json.zip

0.9 KB

01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.sframe.zip

13.3 MB

01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.gl.zip

13.3 MB

01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.csv.zip

9.6 MB

/.../02_course-overview-and-details/

06_reading-software-tools-you-ll-need_quickstart.html

170.3 KB

02_outline-of-first-half-of-course.en.txt

5.5 KB

05_lets-get-started.en.srt

1.1 KB

05_lets-get-started.en.txt

0.6 KB

06_reading-software-tools-you-ll-need_instructions.html

13.2 KB

03_outline-of-second-half-of-course.en.srt

9.3 KB

02_outline-of-first-half-of-course.en.srt

8.6 KB

03_outline-of-second-half-of-course.en.txt

5.9 KB

04_assumed-background.en.srt

5.6 KB

01_course-overview.en.srt

5.0 KB

04_assumed-background.en.txt

3.5 KB

01_course-overview.en.txt

3.1 KB

03_outline-of-second-half-of-course.mp4

21.1 MB

02_outline-of-first-half-of-course.mp4

20.3 MB

04_assumed-background.mp4

12.6 MB

01_course-overview.mp4

12.0 MB

05_lets-get-started.mp4

2.9 MB

/.../01_welcome-to-the-course/

05_impact-of-classification.en.srt

1.5 KB

01_important-update-regarding-the-machine-learning-specialization_instructions.html

1.8 KB

02_slides-presented-in-this-module_instructions.html

1.1 KB

03_welcome-to-the-classification-course-a-part-of-the-machine-learning.en.srt

1.8 KB

03_welcome-to-the-classification-course-a-part-of-the-machine-learning.en.txt

1.1 KB

05_impact-of-classification.en.txt

0.9 KB

04_what-is-this-course-about.en.srt

9.6 KB

04_what-is-this-course-about.en.txt

6.0 KB

04_what-is-this-course-about.mp4

23.1 MB

02_slides-presented-in-this-module_intro.pdf

6.5 MB

03_welcome-to-the-classification-course-a-part-of-the-machine-learning.mp4

4.8 MB

05_impact-of-classification.mp4

4.2 MB

/.../03_multiclass-classification/

01_multiclass.en.txt

2.8 KB

02_softmax.en.srt

15.7 KB

04_improved-implementation-of-softmax.en.srt

13.7 KB

03_neural-network-with-softmax-output.en.srt

9.3 KB

02_softmax.en.txt

8.1 KB

04_improved-implementation-of-softmax.en.txt

7.2 KB

05_classification-with-multiple-outputs-optional.en.srt

6.9 KB

03_neural-network-with-softmax-output.en.txt

5.9 KB

01_multiclass.en.srt

4.4 KB

05_classification-with-multiple-outputs-optional.en.txt

3.7 KB

02_softmax.mp4

21.7 MB

04_improved-implementation-of-softmax.mp4

15.8 MB

03_neural-network-with-softmax-output.mp4

15.8 MB

05_classification-with-multiple-outputs-optional.mp4

11.9 MB

01_multiclass.mp4

8.8 MB

/.../01_why-use-precision-recall-as-quality-metrics/

01_slides-presented-in-this-module_precision-recall.pdf

2.0 MB

01_slides-presented-in-this-module_instructions.html

1.1 KB

02_case-study-where-accuracy-is-not-best-metric-for-classification.en.srt

5.7 KB

03_what-is-good-performance-for-a-classifier.en.srt

5.6 KB

02_case-study-where-accuracy-is-not-best-metric-for-classification.en.txt

3.6 KB

03_what-is-good-performance-for-a-classifier.en.txt

3.4 KB

03_what-is-good-performance-for-a-classifier.mp4

14.5 MB

02_case-study-where-accuracy-is-not-best-metric-for-classification.mp4

13.2 MB

/.../02_practice-quiz-multiple-linear-regression/

01_practice-quiz-multiple-linear-regression_exam.html

71.4 KB

/.../06_more-sframes-practice/

01_download-wiki-people-data_people_wiki.sframe.zip

59.0 MB

01_download-wiki-people-data_instructions.html

1.2 KB

02_sframes_exam.html

3.5 KB

/.../06_programming-assignment/

01_implementing-logistic-regression-from-scratch_module-3-assignment-numpy-arrays.npz.zip

1.2 MB

01_implementing-logistic-regression-from-scratch_instructions.html

31.5 KB

01_implementing-logistic-regression-from-scratch_important_words.json.zip

0.9 KB

01_implementing-logistic-regression-from-scratch_CLA03-NB01.ipynb.zip

7.1 KB

01_implementing-logistic-regression-from-scratch_module-3-linear-classifier-learning-assignment-blank.ipynb.zip

6.6 KB

01_implementing-logistic-regression-from-scratch_amazon_baby_subset.sframe.zip

13.3 MB

01_implementing-logistic-regression-from-scratch_amazon_baby_subset.gl.zip

13.3 MB

01_implementing-logistic-regression-from-scratch_amazon_baby_subset.csv.zip

9.6 MB

/.../06_programming-assignment/

01_polynomial-regression_wk3_kc_house_train_data.csv.zip

363.2 KB

01_polynomial-regression_wk3_kc_house_test_data.csv.zip

83.7 KB

01_polynomial-regression_wk3_kc_house_valid_data.csv.zip

358.4 KB

01_polynomial-regression_home_data.sframe.zip

930.0 KB

01_polynomial-regression_kc_house_data.csv.zip

799.4 KB

01_polynomial-regression_wk3_kc_house_set_3_data.csv.zip

162.5 KB

01_polynomial-regression_wk3_kc_house_set_1_data.csv.zip

162.5 KB

01_polynomial-regression_wk3_kc_house_set_4_data.csv.zip

162.1 KB

01_polynomial-regression_wk3_kc_house_set_2_data.csv.zip

161.4 KB

01_polynomial-regression_instructions.html

14.8 KB

01_polynomial-regression_REG03-NB01.ipynb.zip

4.3 KB

01_polynomial-regression_numpy-tutorial-py3.ipynb.zip

3.0 KB

/.../01_collaborative-filtering/

03_collaborative-filtering-algorithm.en.srt

19.2 KB

02_using-per-item-features.en.srt

12.9 KB

04_binary-labels-favs-likes-and-clicks.en.srt

10.9 KB

03_collaborative-filtering-algorithm.en.txt

10.2 KB

02_using-per-item-features.en.txt

7.9 KB

01_making-recommendations.en.srt

7.4 KB

04_binary-labels-favs-likes-and-clicks.en.txt

6.8 KB

01_making-recommendations.en.txt

4.7 KB

03_collaborative-filtering-algorithm.mp4

32.5 MB

02_using-per-item-features.mp4

24.6 MB

01_making-recommendations.mp4

21.4 MB

04_binary-labels-favs-likes-and-clicks.mp4

20.8 MB

/.../04_document-retrieval-jupyter-notebook/

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_people_wiki.sframe.zip

59.0 MB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND04-NB01.ipynb.zip

11.9 KB

04_computing-exploring-tf-idfs.en.srt

8.5 KB

03_exploring-word-counts.en.srt

7.4 KB

05_computing-distances-between-wikipedia-articles.en.srt

5.8 KB

02_loading-exploring-wikipedia-data.en.srt

5.5 KB

04_computing-exploring-tf-idfs.en.txt

5.0 KB

07_examples-of-document-retrieval-in-action.en.srt

4.9 KB

03_exploring-word-counts.en.txt

4.4 KB

06_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles.en.srt

3.8 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html

2.0 KB

06_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles.en.txt

2.3 KB

07_examples-of-document-retrieval-in-action.en.txt

2.9 KB

05_computing-distances-between-wikipedia-articles.en.txt

3.4 KB

02_loading-exploring-wikipedia-data.en.txt

3.2 KB

03_exploring-word-counts.mp4

19.7 MB

04_computing-exploring-tf-idfs.mp4

19.2 MB

02_loading-exploring-wikipedia-data.mp4

18.4 MB

05_computing-distances-between-wikipedia-articles.mp4

16.1 MB

07_examples-of-document-retrieval-in-action.mp4

13.6 MB

06_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles.mp4

9.8 MB

/.../07_programming-assignment/

01_fitting-a-simple-linear-regression-model-on-housing-data_home_data.sframe.zip

930.0 KB

01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_data.csv.zip

799.4 KB

01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_train_data.csv.zip

643.5 KB

01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_test_data.csv.zip

158.5 KB

01_fitting-a-simple-linear-regression-model-on-housing-data_instructions.html

9.7 KB

01_fitting-a-simple-linear-regression-model-on-housing-data_REG01-NB01.ipynb.zip

4.2 KB

/.../05_summary-of-recommender-systems/

02_recommender-systems_exam.html

808.1 KB

01_recommender-systems-ml-block-diagram.en.txt

3.7 KB

01_recommender-systems-ml-block-diagram.en.srt

5.9 KB

01_recommender-systems-ml-block-diagram.mp4

12.9 MB

/.../07_programming-assignment-2/

01_boosting-a-decision-stump_instructions.html

105.8 KB

01_boosting-a-decision-stump_module-8-assignment-2-train-idx.json.zip

56.4 KB

01_boosting-a-decision-stump_module-8-assignment-2-test-idx.json.zip

19.4 KB

01_boosting-a-decision-stump_CLA08-NB02.ipynb.zip

10.4 KB

01_boosting-a-decision-stump_module-8-boosting-assignment-2-blank.ipynb.zip

10.0 KB

01_boosting-a-decision-stump_lending-club-data.gl.zip

20.3 MB

01_boosting-a-decision-stump_lending-club-data.sframe.zip

20.3 MB

01_boosting-a-decision-stump_lending-club-data.csv.zip

19.4 MB

/.../02_class-probabilities/

04_using-probabilities-in-classification.en.txt

2.2 KB

01_predicting-class-probabilities.en.srt

2.3 KB

01_predicting-class-probabilities.en.txt

1.4 KB

03_review-of-basics-of-conditional-probabilities.en.srt

9.4 KB

02_review-of-basics-of-probabilities.en.srt

7.5 KB

03_review-of-basics-of-conditional-probabilities.en.txt

5.7 KB

02_review-of-basics-of-probabilities.en.txt

4.5 KB

04_using-probabilities-in-classification.en.srt

3.4 KB

03_review-of-basics-of-conditional-probabilities.mp4

20.9 MB

02_review-of-basics-of-probabilities.mp4

16.3 MB

04_using-probabilities-in-classification.mp4

10.4 MB

01_predicting-class-probabilities.mp4

6.0 MB

/.../06_programming-assignment-1/

01_exploring-different-multiple-regression-models-for-house-price-prediction_home_data.sframe.zip

930.0 KB

01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_data.csv.zip

799.4 KB

01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_train_data.csv.zip

643.5 KB

01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_test_data.csv.zip

158.5 KB

01_exploring-different-multiple-regression-models-for-house-price-prediction_instructions.html

10.3 KB

01_exploring-different-multiple-regression-models-for-house-price-prediction_REG02-NB01.ipynb.zip

3.5 KB

/.../06_programming-assignment-1/

01_implementing-em-for-gaussian-mixtures_instructions.html

805.5 KB

01_implementing-em-for-gaussian-mixtures_chosen_images.png

361.4 KB

01_implementing-em-for-gaussian-mixtures_LinearAlgebraReview.html

135.0 KB

01_implementing-em-for-gaussian-mixtures_scipy.stats.multivariate_normal.html

13.2 KB

01_implementing-em-for-gaussian-mixtures_CLU04-NB01.ipynb.zip

11.4 KB

01_implementing-em-for-gaussian-mixtures_images.zip

14.5 MB

01_implementing-em-for-gaussian-mixtures_images.sf.zip

11.7 MB

/.../06_practice-quiz-gradient-descent-for-logistic-regression/

01_practice-quiz-gradient-descent-for-logistic-regression_exam.html

104.5 KB

/.../02_3-measures-of-loss-and-their-trends-with-model-complexity/

03_test-error-what-we-can-actually-compute.en.srt

6.0 KB

02_generalization-error-what-we-really-want.en.srt

10.5 KB

01_training-error-assessing-loss-on-the-training-set.en.srt

10.5 KB

02_generalization-error-what-we-really-want.en.txt

6.7 KB

01_training-error-assessing-loss-on-the-training-set.en.txt

6.7 KB

03_test-error-what-we-can-actually-compute.en.txt

3.8 KB

04_defining-overfitting.en.srt

2.6 KB

05_training-test-split.en.srt

2.6 KB

05_training-test-split.en.txt

1.6 KB

04_defining-overfitting.en.txt

1.5 KB

02_generalization-error-what-we-really-want.mp4

22.8 MB

01_training-error-assessing-loss-on-the-training-set.mp4

21.4 MB

03_test-error-what-we-can-actually-compute.mp4

13.6 MB

04_defining-overfitting.mp4

6.4 MB

05_training-test-split.mp4

6.4 MB

/.../03_using-the-learned-decision-tree/

02_multiclass-classification-with-decision-trees.en.srt

3.7 KB

01_making-predictions-with-decision-trees.en.srt

1.9 KB

01_making-predictions-with-decision-trees.en.txt

1.1 KB

02_multiclass-classification-with-decision-trees.en.txt

2.4 KB

02_multiclass-classification-with-decision-trees.mp4

7.3 MB

01_making-predictions-with-decision-trees.mp4

5.1 MB

/.../08_programming-assignment-1/

01_using-lasso-to-select-features_wk3_kc_house_train_data.csv.zip

363.2 KB

01_using-lasso-to-select-features_wk3_kc_house_test_data.csv.zip

83.7 KB

01_using-lasso-to-select-features_home_data.sframe.zip

930.0 KB

01_using-lasso-to-select-features_wk3_kc_house_valid_data.csv.zip

358.4 KB

01_using-lasso-to-select-features_kc_house_data.csv.zip

799.4 KB

01_using-lasso-to-select-features_instructions.html

14.1 KB

01_using-lasso-to-select-features_REG05-NB01.ipynb.zip

4.0 KB

01_using-lasso-to-select-features_numpy-tutorial-py3.ipynb.zip

3.0 KB

/.../01_motivating-and-setting-the-foundation-for-mixture-models/

02_motiving-probabilistic-clustering-models.en.srt

11.0 KB

05_bivariate-and-multivariate-gaussians.en.srt

9.3 KB

03_aggregating-over-unknown-classes-in-an-image-dataset.en.srt

8.7 KB

02_motiving-probabilistic-clustering-models.en.txt

7.0 KB

05_bivariate-and-multivariate-gaussians.en.txt

5.8 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

04_univariate-gaussian-distributions.en.txt

2.5 KB

03_aggregating-over-unknown-classes-in-an-image-dataset.en.txt

5.6 KB

04_univariate-gaussian-distributions.en.srt

3.9 KB

02_motiving-probabilistic-clustering-models.mp4

23.4 MB

03_aggregating-over-unknown-classes-in-an-image-dataset.mp4

22.6 MB

05_bivariate-and-multivariate-gaussians.mp4

22.3 MB

01_slides-presented-in-this-module_mixmodel-EM-annotated.pdf

18.5 MB

04_univariate-gaussian-distributions.mp4

9.5 MB

/.../08_practice-quiz-the-problem-of-overfitting/

01_practice-quiz-the-problem-of-overfitting_exam.html

91.9 KB

/.../05_locality-sensitive-hashing-for-approximate-nn-search/

07_optional-improving-efficiency-through-multiple-tables.mp4

56.8 MB

07_optional-improving-efficiency-through-multiple-tables.en.srt

26.6 KB

04_defining-more-bins.en.srt

4.3 KB

07_optional-improving-efficiency-through-multiple-tables.en.txt

16.3 KB

05_searching-neighboring-bins.en.srt

10.8 KB

03_using-random-lines-to-partition-points.en.srt

8.0 KB

05_searching-neighboring-bins.en.txt

7.0 KB

04_defining-more-bins.en.txt

2.7 KB

02_lsh-as-an-alternative-to-kd-trees.en.srt

6.2 KB

01_limitations-of-kd-trees.en.srt

5.3 KB

06_lsh-in-higher-dimensions.en.srt

5.3 KB

03_using-random-lines-to-partition-points.en.txt

5.1 KB

02_lsh-as-an-alternative-to-kd-trees.en.txt

4.0 KB

01_limitations-of-kd-trees.en.txt

3.4 KB

06_lsh-in-higher-dimensions.en.txt

3.3 KB

05_searching-neighboring-bins.mp4

23.1 MB

03_using-random-lines-to-partition-points.mp4

17.9 MB

02_lsh-as-an-alternative-to-kd-trees.mp4

13.5 MB

01_limitations-of-kd-trees.mp4

12.1 MB

04_defining-more-bins.mp4

11.1 MB

06_lsh-in-higher-dimensions.mp4

10.4 MB

/.../01_deploying-machine-learning-as-a-service/

02_you-ve-made-it.en.srt

1.1 KB

04_what-happens-after-deployment.en.srt

11.9 KB

04_what-happens-after-deployment.en.txt

7.5 KB

03_deploying-an-ml-service.en.srt

5.4 KB

03_deploying-an-ml-service.en.txt

3.4 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_you-ve-made-it.en.txt

0.6 KB

04_what-happens-after-deployment.mp4

31.4 MB

01_slides-presented-in-this-module_closing.pdf

20.4 MB

03_deploying-an-ml-service.mp4

15.8 MB

02_you-ve-made-it.mp4

3.0 MB

/.../04_practice-quiz-cost-function-for-logistic-regression/

01_practice-quiz-cost-function-for-logistic-regression_exam.html

78.8 KB

/.../02_the-simple-linear-regression-model-its-use-and-interpretation/

03_using-the-fitted-line.en.txt

4.0 KB

02_the-cost-of-using-a-given-line.en.srt

8.1 KB

03_using-the-fitted-line.en.srt

6.9 KB

04_interpreting-the-fitted-line.en.srt

6.7 KB

02_the-cost-of-using-a-given-line.en.txt

5.0 KB

04_interpreting-the-fitted-line.en.txt

4.0 KB

01_the-simple-linear-regression-model.en.srt

2.7 KB

01_the-simple-linear-regression-model.en.txt

1.7 KB

02_the-cost-of-using-a-given-line.mp4

18.3 MB

03_using-the-fitted-line.mp4

17.4 MB

04_interpreting-the-fitted-line.mp4

15.9 MB

01_the-simple-linear-regression-model.mp4

8.0 MB

/.../07_programming-assignment-2/

02_implementing-gradient-descent-for-multiple-regression_home_data.sframe.zip

930.0 KB

02_implementing-gradient-descent-for-multiple-regression_kc_house_data.csv.zip

799.4 KB

02_implementing-gradient-descent-for-multiple-regression_kc_house_train_data.csv.zip

643.5 KB

01_numpy-tutorial_quickstart.html

166.8 KB

02_implementing-gradient-descent-for-multiple-regression_kc_house_test_data.csv.zip

158.5 KB

02_implementing-gradient-descent-for-multiple-regression_instructions.html

15.3 KB

02_implementing-gradient-descent-for-multiple-regression_REG02-NB02.ipynb.zip

5.8 KB

01_numpy-tutorial_numpy-tutorial.ipynb.zip

3.0 KB

02_implementing-gradient-descent-for-multiple-regression_numpy-tutorial-py3.ipynb.zip.ipynb

3.0 KB

01_numpy-tutorial_instructions.html

2.3 KB

/.../01_regression-fundamentals/

02_a-case-study-in-predicting-house-prices.en.txt

0.9 KB

03_regression-fundamentals-data-model.en.srt

10.0 KB

03_regression-fundamentals-data-model.en.txt

6.1 KB

05_regression-ml-block-diagram.en.srt

6.0 KB

05_regression-ml-block-diagram.en.txt

3.6 KB

04_regression-fundamentals-the-task.en.srt

3.2 KB

04_regression-fundamentals-the-task.en.txt

2.0 KB

02_a-case-study-in-predicting-house-prices.en.srt

1.5 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

03_regression-fundamentals-data-model.mp4

23.4 MB

05_regression-ml-block-diagram.mp4

12.3 MB

01_slides-presented-in-this-module_week1_simpleregression-annotated.pdf

8.3 MB

04_regression-fundamentals-the-task.mp4

7.7 MB

02_a-case-study-in-predicting-house-prices.mp4

4.0 MB

/.../01_what-is-this-course-about/

07_software-tools-you-ll-need-for-this-course_quickstart.html

170.3 KB

07_software-tools-you-ll-need-for-this-course_instructions.html

13.9 KB

05_module-by-module-topics-covered.en.srt

13.8 KB

06_assumed-background.en.srt

10.5 KB

03_welcome-and-introduction-to-clustering-and-retrieval-tasks.en.srt

10.1 KB

05_module-by-module-topics-covered.en.txt

8.6 KB

01_important-update-regarding-the-machine-learning-specialization_instructions.html

1.8 KB

02_slides-presented-in-this-module_instructions.html

1.1 KB

06_assumed-background.en.txt

6.7 KB

03_welcome-and-introduction-to-clustering-and-retrieval-tasks.en.txt

6.3 KB

04_course-overview.en.srt

5.4 KB

04_course-overview.en.txt

3.4 KB

08_a-big-week-ahead_instructions.html

1.2 KB

05_module-by-module-topics-covered.mp4

30.4 MB

03_welcome-and-introduction-to-clustering-and-retrieval-tasks.mp4

20.8 MB

06_assumed-background.mp4

20.4 MB

02_slides-presented-in-this-module_intro.pdf

11.3 MB

04_course-overview.mp4

11.1 MB

/.../02_summary-and-whats-ahead-in-the-specialization/

02_thank-you.en.txt

1.4 KB

01_what-we-covered-and-what-we-didn-t-cover.en.srt

8.7 KB

01_what-we-covered-and-what-we-didn-t-cover.en.txt

5.5 KB

02_thank-you.en.srt

2.4 KB

01_what-we-covered-and-what-we-didn-t-cover.mp4

17.3 MB

02_thank-you.mp4

6.1 MB

/.../06_programming-assignment-2/

01_implementing-ridge-regression-via-gradient-descent_home_data.sframe.zip

930.0 KB

01_implementing-ridge-regression-via-gradient-descent_kc_house_data.csv.zip

799.4 KB

01_implementing-ridge-regression-via-gradient-descent_kc_house_train_data.csv.zip

643.5 KB

01_implementing-ridge-regression-via-gradient-descent_kc_house_test_data.csv.zip

158.5 KB

01_implementing-ridge-regression-via-gradient-descent_instructions.html

19.0 KB

01_implementing-ridge-regression-via-gradient-descent_REG04-NB02.ipynb.zip

4.8 KB

01_implementing-ridge-regression-via-gradient-descent_numpy-tutorial-py3.ipynb.zip

3.0 KB

/.../01_what-is-this-course-about/

08_reading-software-tools-you-ll-need_quickstart.html

170.3 KB

08_reading-software-tools-you-ll-need_instructions.html

13.3 KB

06_outlining-the-second-half-of-the-course.en.srt

8.0 KB

05_outlining-the-first-half-of-the-course.en.srt

7.4 KB

07_assumed-background.en.srt

6.0 KB

04_what-is-the-course-about.en.srt

5.6 KB

06_outlining-the-second-half-of-the-course.en.txt

5.2 KB

05_outlining-the-first-half-of-the-course.en.txt

4.8 KB

07_assumed-background.en.txt

3.9 KB

04_what-is-the-course-about.en.txt

3.5 KB

03_welcome.en.srt

2.9 KB

03_welcome.en.txt

1.9 KB

01_important-update-regarding-the-machine-learning-specialization_instructions.html

1.8 KB

02_slides-presented-in-this-module_instructions.html

1.2 KB

02_slides-presented-in-this-module_intro.pdf

21.8 MB

06_outlining-the-second-half-of-the-course.mp4

16.9 MB

05_outlining-the-first-half-of-the-course.mp4

15.6 MB

07_assumed-background.mp4

12.4 MB

04_what-is-the-course-about.mp4

11.7 MB

03_welcome.mp4

5.1 MB

/.../05_back-propagation-optional/

02_computation-graph-optional.en.srt

27.0 KB

01_what-is-a-derivative-optional.en.txt

15.6 KB

01_what-is-a-derivative-optional.mp4

40.2 MB

01_what-is-a-derivative-optional.en.srt

30.4 KB

02_computation-graph-optional.en.txt

13.9 KB

03_larger-neural-network-example-optional.en.srt

12.2 KB

03_larger-neural-network-example-optional.en.txt

7.5 KB

02_computation-graph-optional.mp4

31.4 MB

03_larger-neural-network-example-optional.mp4

27.4 MB

/.../04_getting-started-with-python-and-the-jupyter-notebook/

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html

1.7 KB

04_conditional-statements-and-loops-in-python.en.srt

9.6 KB

03_creating-variables-in-python.en.srt

8.4 KB

02_starting-a-jupyter-notebook.en.srt

7.8 KB

04_conditional-statements-and-loops-in-python.en.txt

5.4 KB

03_creating-variables-in-python.en.txt

4.9 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_Getting_started_with_Jupyter_Notebook.ipynb.zip

2.0 KB

05_creating-functions-and-lambdas-in-python.en.txt

2.5 KB

02_starting-a-jupyter-notebook.en.txt

4.5 KB

05_creating-functions-and-lambdas-in-python.en.srt

4.3 KB

04_conditional-statements-and-loops-in-python.mp4

21.1 MB

03_creating-variables-in-python.mp4

18.5 MB

02_starting-a-jupyter-notebook.mp4

14.4 MB

05_creating-functions-and-lambdas-in-python.mp4

9.5 MB

/.../04_deep-features-for-image-classification-jupyter-notebook/

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_test_data.zip

53.4 MB

04_training-evaluating-a-classifier-using-deep-features.en.srt

9.5 KB

03_training-evaluating-a-classifier-using-raw-image-pixels.en.srt

7.0 KB

04_training-evaluating-a-classifier-using-deep-features.en.txt

5.5 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND06-NB01.ipynb.zip

5.1 KB

02_loading-image-data.en.srt

4.2 KB

03_training-evaluating-a-classifier-using-raw-image-pixels.en.txt

4.0 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html

2.3 KB

02_loading-image-data.en.txt

2.4 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_train_data.zip

26.6 MB

04_training-evaluating-a-classifier-using-deep-features.mp4

18.7 MB

03_training-evaluating-a-classifier-using-raw-image-pixels.mp4

15.7 MB

02_loading-image-data.mp4

9.9 MB

/.../02_the-ridge-objective/

04_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip

203.2 KB

05_ridge-regression-demo.en.srt

12.3 KB

01_balancing-fit-and-magnitude-of-coefficients.en.srt

7.8 KB

05_ridge-regression-demo.en.txt

7.7 KB

02_the-resulting-ridge-objective-and-its-extreme-solutions.en.srt

6.5 KB

01_balancing-fit-and-magnitude-of-coefficients.en.txt

4.8 KB

06_the-ridge-coefficient-path.en.srt

4.7 KB

02_the-resulting-ridge-objective-and-its-extreme-solutions.en.txt

3.9 KB

06_the-ridge-coefficient-path.en.txt

3.0 KB

03_how-ridge-regression-balances-bias-and-variance.en.srt

2.0 KB

04_download-the-notebook-and-follow-along_instructions.html

1.7 KB

03_how-ridge-regression-balances-bias-and-variance.en.txt

1.3 KB

05_ridge-regression-demo.mp4

26.2 MB

01_balancing-fit-and-magnitude-of-coefficients.mp4

19.0 MB

02_the-resulting-ridge-objective-and-its-extreme-solutions.mp4

14.3 MB

06_the-ridge-coefficient-path.mp4

10.0 MB

03_how-ridge-regression-balances-bias-and-variance.mp4

5.6 MB

/.../02_who-this-specialization-is-for-and-what-you-will-be-able-to-do/

03_what-you-ll-be-able-to-do.en.txt

0.9 KB

04_the-capstone-and-an-example-intelligent-application.en.srt

10.3 KB

04_the-capstone-and-an-example-intelligent-application.en.txt

6.4 KB

02_who-is-this-specialization-for.en.srt

5.7 KB

01_how-we-got-into-ml.en.srt

5.7 KB

03_what-you-ll-be-able-to-do.en.srt

1.5 KB

05_the-future-of-intelligent-applications.en.txt

2.3 KB

05_the-future-of-intelligent-applications.en.srt

4.0 KB

02_who-is-this-specialization-for.en.txt

3.6 KB

01_how-we-got-into-ml.en.txt

3.4 KB

04_the-capstone-and-an-example-intelligent-application.mp4

23.2 MB

01_how-we-got-into-ml.mp4

20.0 MB

02_who-is-this-specialization-for.mp4

15.1 MB

05_the-future-of-intelligent-applications.mp4

13.4 MB

03_what-you-ll-be-able-to-do.mp4

4.7 MB

/.../05_deep-features-for-image-retrieval-jupyter-notebook/

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_test_data.zip

53.4 MB

05_querying-for-the-most-similar-images-for-car-image.en.srt

2.2 KB

04_querying-the-nearest-neighbors-model-to-retrieve-images.en.srt

6.0 KB

06_displaying-other-example-image-retrievals-with-a-python-lambda.en.srt

4.8 KB

04_querying-the-nearest-neighbors-model-to-retrieve-images.en.txt

3.4 KB

02_loading-image-data.en.srt

3.3 KB

06_displaying-other-example-image-retrievals-with-a-python-lambda.en.txt

2.8 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND06-NB02.ipynb.zip

2.0 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html

2.5 KB

02_loading-image-data.en.txt

2.0 KB

03_creating-a-nearest-neighbors-model-for-image-retrieval.en.srt

1.8 KB

03_creating-a-nearest-neighbors-model-for-image-retrieval.en.txt

1.0 KB

05_querying-for-the-most-similar-images-for-car-image.en.txt

1.2 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_train_data.zip

26.6 MB

04_querying-the-nearest-neighbors-model-to-retrieve-images.mp4

13.9 MB

06_displaying-other-example-image-retrievals-with-a-python-lambda.mp4

10.4 MB

02_loading-image-data.mp4

7.8 MB

03_creating-a-nearest-neighbors-model-for-image-retrieval.mp4

5.0 MB

05_querying-for-the-most-similar-images-for-car-image.mp4

4.9 MB

/.../09_programming-assignment-2/

01_implementing-lasso-using-coordinate-descent_home_data.sframe.zip

930.0 KB

01_implementing-lasso-using-coordinate-descent_kc_house_data.csv.zip

799.4 KB

01_implementing-lasso-using-coordinate-descent_kc_house_train_data.csv.zip

643.5 KB

01_implementing-lasso-using-coordinate-descent_kc_house_test_data.csv.zip

158.5 KB

01_implementing-lasso-using-coordinate-descent_instructions.html

20.1 KB

01_implementing-lasso-using-coordinate-descent_REG05-NB02.ipynb.zip

5.8 KB

01_implementing-lasso-using-coordinate-descent_numpy-tutorial-py3.ipynb.zip

3.0 KB

/.../03_geometric-intuition-for-sparsity-of-lasso-solutions/

04_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip

203.2 KB

01_visualizing-the-ridge-cost.en.srt

8.4 KB

03_visualizing-the-lasso-cost-and-solution.en.srt

8.3 KB

02_visualizing-the-ridge-solution.en.srt

7.0 KB

05_lasso-demo.en.srt

6.6 KB

01_visualizing-the-ridge-cost.en.txt

5.0 KB

03_visualizing-the-lasso-cost-and-solution.en.txt

5.0 KB

05_lasso-demo.en.txt

4.2 KB

02_visualizing-the-ridge-solution.en.txt

4.1 KB

04_download-the-notebook-and-follow-along_instructions.html

1.6 KB

03_visualizing-the-lasso-cost-and-solution.mp4

19.3 MB

01_visualizing-the-ridge-cost.mp4

18.7 MB

02_visualizing-the-ridge-solution.mp4

15.4 MB

05_lasso-demo.mp4

12.8 MB

/.../01_classification-modeling/

02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.en.srt

0.9 KB

04_examples-of-classification-tasks.en.srt

7.4 KB

05_linear-classifiers.en.srt

7.3 KB

03_what-is-an-intelligent-restaurant-review-system.en.srt

6.4 KB

06_decision-boundaries.en.srt

5.6 KB

04_examples-of-classification-tasks.en.txt

4.6 KB

05_linear-classifiers.en.txt

4.5 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.en.txt

0.5 KB

03_what-is-an-intelligent-restaurant-review-system.en.txt

4.0 KB

06_decision-boundaries.en.txt

3.4 KB

04_examples-of-classification-tasks.mp4

24.1 MB

05_linear-classifiers.mp4

19.4 MB

03_what-is-an-intelligent-restaurant-review-system.mp4

19.4 MB

06_decision-boundaries.mp4

16.8 MB

01_slides-presented-in-this-module_classification-annotated.pdf

7.2 MB

02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.mp4

3.0 MB

/.../01_linear-classifiers/

06_effect-of-coefficient-values-on-decision-boundary.en.srt

3.0 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_linear-classifiers-a-motivating-example.en.txt

2.4 KB

04_decision-boundaries.en.txt

2.8 KB

06_effect-of-coefficient-values-on-decision-boundary.en.txt

1.9 KB

07_using-features-of-the-inputs.en.srt

3.0 KB

07_using-features-of-the-inputs.en.txt

1.9 KB

05_linear-classifier-model.en.srt

7.0 KB

03_intuition-behind-linear-classifiers.en.srt

5.3 KB

04_decision-boundaries.en.srt

4.6 KB

05_linear-classifier-model.en.txt

4.3 KB

02_linear-classifiers-a-motivating-example.en.srt

4.0 KB

03_intuition-behind-linear-classifiers.en.txt

3.2 KB

05_linear-classifier-model.mp4

20.0 MB

03_intuition-behind-linear-classifiers.mp4

12.9 MB

01_slides-presented-in-this-module_logistic-regression-model-annotated.pdf

12.1 MB

04_decision-boundaries.mp4

11.6 MB

02_linear-classifiers-a-motivating-example.mp4

10.4 MB

07_using-features-of-the-inputs.mp4

9.1 MB

06_effect-of-coefficient-values-on-decision-boundary.mp4

8.0 MB

/.../06_song-recommender-jupyter-notebook/

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_song_data.sframe.zip

50.3 MB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND05-NB01.ipynb.zip

15.5 KB

04_creating-evaluating-a-personalized-song-recommender.en.srt

6.5 KB

02_loading-and-exploring-song-data.en.srt

6.3 KB

03_creating-evaluating-a-popularity-based-song-recommender.en.srt

5.6 KB

05_using-precision-recall-to-compare-recommender-models.en.srt

4.5 KB

04_creating-evaluating-a-personalized-song-recommender.en.txt

3.8 KB

02_loading-and-exploring-song-data.en.txt

3.7 KB

03_creating-evaluating-a-popularity-based-song-recommender.en.txt

3.3 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html

2.0 KB

05_using-precision-recall-to-compare-recommender-models.en.txt

2.7 KB

04_creating-evaluating-a-personalized-song-recommender.mp4

17.2 MB

02_loading-and-exploring-song-data.mp4

15.5 MB

03_creating-evaluating-a-popularity-based-song-recommender.mp4

13.8 MB

05_using-precision-recall-to-compare-recommender-models.mp4

11.8 MB

/ml-classification/08_boosting/02_adaboost/

03_computing-coefficient-of-each-ensemble-component.en.txt

3.8 KB

01_adaboost-overview.en.txt

2.4 KB

05_normalizing-weights.en.srt

2.8 KB

05_normalizing-weights.en.txt

1.7 KB

02_weighted-error.en.srt

6.9 KB

04_reweighing-data-to-focus-on-mistakes.en.srt

6.6 KB

03_computing-coefficient-of-each-ensemble-component.en.srt

6.2 KB

02_weighted-error.en.txt

4.2 KB

04_reweighing-data-to-focus-on-mistakes.en.txt

4.2 KB

01_adaboost-overview.en.srt

4.1 KB

02_weighted-error.mp4

13.8 MB

03_computing-coefficient-of-each-ensemble-component.mp4

12.7 MB

04_reweighing-data-to-focus-on-mistakes.mp4

12.3 MB

01_adaboost-overview.mp4

8.1 MB

05_normalizing-weights.mp4

7.1 MB

/.../01_linear-regression-modeling/

04_linear-regression-a-model-based-approach.en.txt

4.2 KB

04_linear-regression-a-model-based-approach.en.srt

7.0 KB

05_adding-higher-order-effects.en.srt

6.2 KB

03_what-is-the-goal-and-how-might-you-naively-address-it.en.srt

5.1 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_predicting-house-prices-a-case-study-in-regression.en.srt

2.1 KB

02_predicting-house-prices-a-case-study-in-regression.en.txt

1.3 KB

05_adding-higher-order-effects.en.txt

3.6 KB

03_what-is-the-goal-and-how-might-you-naively-address-it.en.txt

3.2 KB

01_slides-presented-in-this-module_regression-intro-annotated.pdf

22.6 MB

04_linear-regression-a-model-based-approach.mp4

13.3 MB

05_adding-higher-order-effects.mp4

10.9 MB

03_what-is-the-goal-and-how-might-you-naively-address-it.mp4

10.5 MB

02_predicting-house-prices-a-case-study-in-regression.mp4

4.0 MB

/.../03_collapsed-gibbs-sampling-for-lda/

03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.srt

9.8 KB

03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.txt

6.2 KB

04_using-the-output-of-collapsed-gibbs-sampling.en.srt

5.8 KB

01_what-is-collapsed-gibbs-sampling.en.txt

2.9 KB

02_a-worked-example-for-lda-initial-setup.en.srt

5.0 KB

01_what-is-collapsed-gibbs-sampling.en.srt

4.5 KB

04_using-the-output-of-collapsed-gibbs-sampling.en.txt

3.6 KB

02_a-worked-example-for-lda-initial-setup.en.txt

3.1 KB

03_a-worked-example-for-lda-deriving-the-resampling-distribution.mp4

17.5 MB

04_using-the-output-of-collapsed-gibbs-sampling.mp4

16.0 MB

01_what-is-collapsed-gibbs-sampling.mp4

12.3 MB

02_a-worked-example-for-lda-initial-setup.mp4

9.2 MB

/.../04_principal-component-analysis/

01_reducing-the-number-of-features-optional.en.srt

18.5 KB

02_pca-algorithm-optional.en.srt

25.0 KB

03_pca-in-code-optional.en.srt

17.1 KB

02_pca-algorithm-optional.en.txt

13.1 KB

01_reducing-the-number-of-features-optional.en.txt

9.6 KB

03_pca-in-code-optional.en.txt

8.8 KB

02_pca-algorithm-optional.mp4

29.4 MB

01_reducing-the-number-of-features-optional.mp4

28.0 MB

03_pca-in-code-optional.mp4

18.7 MB

/.../06_optional-advanced-material-deriving-the-lasso-coordinate-descent-update/

01_deriving-the-lasso-coordinate-descent-update.mp4

44.8 MB

01_deriving-the-lasso-coordinate-descent-update.en.srt

20.7 KB

01_deriving-the-lasso-coordinate-descent-update.en.txt

12.4 KB

/.../04_the-em-algorithm/

04_optional-a-worked-out-example-for-em_instructions.html

215.9 KB

02_convergence-initialization-and-overfitting-of-em.en.srt

12.8 KB

02_convergence-initialization-and-overfitting-of-em.en.txt

8.2 KB

01_em-iterates-in-equations-and-pictures.en.srt

7.8 KB

03_relationship-to-k-means.en.txt

2.7 KB

01_em-iterates-in-equations-and-pictures.en.txt

4.9 KB

03_relationship-to-k-means.en.srt

4.2 KB

02_convergence-initialization-and-overfitting-of-em.mp4

31.4 MB

01_em-iterates-in-equations-and-pictures.mp4

17.5 MB

03_relationship-to-k-means.mp4

10.9 MB

/.../01_overview-of-machine-learning/

01_welcome-to-machine-learning.en.txt

2.5 KB

02_applications-of-machine-learning.mp4

35.1 MB

02_applications-of-machine-learning.en.srt

7.7 KB

02_applications-of-machine-learning.en.txt

4.0 KB

01_welcome-to-machine-learning.en.srt

4.0 KB

01_welcome-to-machine-learning.mp4

23.3 MB

/.../01_why-you-should-learn-machine-learning-with-us/

03_welcome-to-this-course-and-specialization.en.txt

0.6 KB

06_why-a-case-study-approach.en.srt

11.2 KB

07_specialization-overview.en.srt

9.5 KB

04_who-we-are.en.srt

9.0 KB

06_why-a-case-study-approach.en.txt

7.0 KB

07_specialization-overview.en.txt

6.1 KB

05_machine-learning-is-changing-the-world.en.srt

5.5 KB

04_who-we-are.en.txt

5.3 KB

01_important-update-regarding-the-machine-learning-specialization_instructions.html

1.8 KB

02_slides-presented-in-this-module_instructions.html

1.2 KB

03_welcome-to-this-course-and-specialization.en.srt

1.2 KB

05_machine-learning-is-changing-the-world.en.txt

3.5 KB

06_why-a-case-study-approach.mp4

30.5 MB

04_who-we-are.mp4

29.8 MB

07_specialization-overview.mp4

28.6 MB

05_machine-learning-is-changing-the-world.mp4

17.1 MB

02_slides-presented-in-this-module_intro.pdf

6.5 MB

03_welcome-to-this-course-and-specialization.mp4

3.5 MB

/.../02_recommender-systems-implementation-detail/

01_mean-normalization.en.srt

11.0 KB

02_tensorflow-implementation-of-collaborative-filtering.mp4

37.6 MB

02_tensorflow-implementation-of-collaborative-filtering.en.srt

15.8 KB

03_finding-related-items.en.srt

10.5 KB

02_tensorflow-implementation-of-collaborative-filtering.en.txt

9.8 KB

01_mean-normalization.en.txt

6.9 KB

03_finding-related-items.en.txt

5.5 KB

01_mean-normalization.mp4

19.8 MB

03_finding-related-items.mp4

17.4 MB

/.../05_programming-assignment/

01_exploring-precision-and-recall_amazon_baby.gl.zip

42.3 MB

01_exploring-precision-and-recall_amazon_baby.sframe.zip

42.3 MB

01_exploring-precision-and-recall_instructions.html

72.6 KB

01_exploring-precision-and-recall_module-9-assignment-train-idx.json.zip

167.8 KB

01_exploring-precision-and-recall_module-9-assignment-test-idx.json.zip

47.8 KB

01_exploring-precision-and-recall_CLA09-NB01.ipynb.zip

6.5 KB

01_exploring-precision-and-recall_amazon_baby.csv.zip

30.1 MB

/.../05_getting-started-with-sframes-for-data-engineering-and-analysis/

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_people-example.csv

0.2 KB

05_using-apply-for-data-transformation.en.srt

6.5 KB

02_starting-turi-create-loading-an-sframe.en.srt

5.5 KB

04_interacting-with-columns-of-an-sframe.en.srt

5.3 KB

03_canvas-for-data-visualization.en.srt

5.3 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html

2.3 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_Turi_Getting_Started_with_SFrames.ipynb.zip

1.8 KB

04_interacting-with-columns-of-an-sframe.en.txt

3.0 KB

05_using-apply-for-data-transformation.en.txt

3.8 KB

02_starting-turi-create-loading-an-sframe.en.txt

3.3 KB

03_canvas-for-data-visualization.en.txt

3.2 KB

05_using-apply-for-data-transformation.mp4

13.1 MB

02_starting-turi-create-loading-an-sframe.mp4

12.1 MB

04_interacting-with-columns-of-an-sframe.mp4

11.0 MB

03_canvas-for-data-visualization.mp4

10.2 MB

/.../03_summary-of-regression/

02_regression_exam.html

693.2 KB

01_regression-ml-block-diagram.en.srt

8.0 KB

01_regression-ml-block-diagram.en.txt

5.0 KB

01_regression-ml-block-diagram.mp4

16.2 MB

/.../06_programming-assignment-1/

01_identifying-safe-loans-with-decision-trees_sklearn.tree.DecisionTreeClassifier.html

76.7 KB

01_identifying-safe-loans-with-decision-trees_graphlab.SFrame.to_numpy.html

288.2 KB

01_identifying-safe-loans-with-decision-trees_module-5-assignment-1-train-idx.json.zip

56.4 KB

01_identifying-safe-loans-with-decision-trees_instructions.html

25.6 KB

01_identifying-safe-loans-with-decision-trees_module-5-assignment-1-validation-idx.json.zip

19.4 KB

01_identifying-safe-loans-with-decision-trees_CLA05-NB01.ipynb.zip

6.4 KB

01_identifying-safe-loans-with-decision-trees_lending-club-data.gl.zip

20.3 MB

01_identifying-safe-loans-with-decision-trees_lending-club-data.sframe.zip

20.3 MB

01_identifying-safe-loans-with-decision-trees_lending-club-data.csv.zip

19.4 MB

/.../02_bayesian-inference-via-gibbs-sampling/

03_a-standard-gibbs-sampler-for-lda.en.srt

11.7 KB

02_gibbs-sampling-from-10-000-feet.en.srt

7.6 KB

03_a-standard-gibbs-sampler-for-lda.en.txt

7.3 KB

01_the-need-for-bayesian-inference.en.srt

6.9 KB

02_gibbs-sampling-from-10-000-feet.en.txt

4.9 KB

01_the-need-for-bayesian-inference.en.txt

4.4 KB

03_a-standard-gibbs-sampler-for-lda.mp4

30.7 MB

02_gibbs-sampling-from-10-000-feet.mp4

19.3 MB

01_the-need-for-bayesian-inference.mp4

17.8 MB

/.../03_practice-quiz-supervised-vs-unsupervised-learning/

01_practice-quiz-supervised-vs-unsupervised-learning_exam.html

2.0 KB

/.../04_practice-quiz-gradient-descent-in-practice/

01_practice-quiz-gradient-descent-in-practice_exam.html

168.8 KB

/.../01_the-amazing-idea-of-boosting-a-classifier/

02_the-boosting-question.en.srt

6.0 KB

04_boosting.en.srt

9.3 KB

01_slides-presented-in-this-module_instructions.html

1.1 KB

03_ensemble-classifiers.en.srt

7.9 KB

04_boosting.en.txt

5.9 KB

03_ensemble-classifiers.en.txt

5.0 KB

02_the-boosting-question.en.txt

3.8 KB

04_boosting.mp4

22.5 MB

03_ensemble-classifiers.mp4

18.4 MB

02_the-boosting-question.mp4

13.9 MB

01_slides-presented-in-this-module_boosting-annotated.pdf

2.3 MB

/.../04_analyzing-sentiment-jupyter-notebook/

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_amazon_baby.sframe.zip

42.3 MB

03_creating-the-word-count-vector.en.txt

1.3 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND03-NB01.ipynb.zip

13.4 KB

09_exploring-the-most-positive-negative-aspects-of-a-product.en.srt

6.0 KB

07_evaluating-a-classifier-the-roc-curve.en.srt

5.8 KB

04_exploring-the-most-popular-product.en.srt

5.7 KB

08_applying-model-to-find-most-positive-negative-reviews-for-a-product.en.srt

5.3 KB

05_defining-which-reviews-have-positive-or-negative-sentiment.en.srt

5.1 KB

01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html

2.0 KB

02_loading-exploring-product-review-data.en.txt

1.9 KB

03_creating-the-word-count-vector.en.srt

2.2 KB

05_defining-which-reviews-have-positive-or-negative-sentiment.en.txt

3.0 KB

06_training-a-sentiment-classifier.en.txt

2.3 KB

06_training-a-sentiment-classifier.en.srt

3.9 KB

09_exploring-the-most-positive-negative-aspects-of-a-product.en.txt

3.5 KB

07_evaluating-a-classifier-the-roc-curve.en.txt

3.5 KB

04_exploring-the-most-popular-product.en.txt

3.4 KB

02_loading-exploring-product-review-data.en.srt

3.4 KB

08_applying-model-to-find-most-positive-negative-reviews-for-a-product.en.txt

3.1 KB

09_exploring-the-most-positive-negative-aspects-of-a-product.mp4

16.6 MB

08_applying-model-to-find-most-positive-negative-reviews-for-a-product.mp4

13.8 MB

04_exploring-the-most-popular-product.mp4

12.7 MB

07_evaluating-a-classifier-the-roc-curve.mp4

12.5 MB

05_defining-which-reviews-have-positive-or-negative-sentiment.mp4

12.2 MB

06_training-a-sentiment-classifier.mp4

9.5 MB

02_loading-exploring-product-review-data.mp4

8.4 MB

03_creating-the-word-count-vector.mp4

6.5 MB

/.../05_practice-quiz-regression-model/

01_practice-quiz-regression_exam.html

2.7 KB

/.../04_computing-the-least-squares-d-dimensional-curve/

01_computing-the-gradient-of-rss.en.srt

3.8 KB

05_feature-by-feature-update.en.srt

8.9 KB

03_discussing-the-closed-form-solution.en.srt

5.4 KB

05_feature-by-feature-update.en.txt

5.2 KB

06_algorithmic-summary-of-gradient-descent-approach.en.srt

5.2 KB

02_approach-1-closed-form-solution.en.srt

4.5 KB

03_discussing-the-closed-form-solution.en.txt

3.4 KB

06_algorithmic-summary-of-gradient-descent-approach.en.txt

3.2 KB

02_approach-1-closed-form-solution.en.txt

2.7 KB

01_computing-the-gradient-of-rss.en.txt

2.3 KB

04_approach-2-gradient-descent.en.srt

2.1 KB

04_approach-2-gradient-descent.en.txt

1.3 KB

05_feature-by-feature-update.mp4

19.9 MB

06_algorithmic-summary-of-gradient-descent-approach.mp4

11.5 MB

03_discussing-the-closed-form-solution.mp4

11.4 MB

02_approach-1-closed-form-solution.mp4

10.5 MB

01_computing-the-gradient-of-rss.mp4

7.6 MB

04_approach-2-gradient-descent.mp4

5.6 MB

/.../01_introduction-to-nearest-neighbor-search-and-algorithms/

02_retrieval-as-k-nearest-neighbor-search.en.srt

4.5 KB

04_k-nn-algorithm.en.srt

8.3 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_retrieval-as-k-nearest-neighbor-search.en.txt

2.8 KB

03_1-nn-algorithm.en.txt

2.4 KB

04_k-nn-algorithm.en.txt

5.2 KB

03_1-nn-algorithm.en.srt

3.7 KB

04_k-nn-algorithm.mp4

18.2 MB

01_slides-presented-in-this-module_retrieval-intro-annotated.pdf

14.2 MB

02_retrieval-as-k-nearest-neighbor-search.mp4

9.8 MB

03_1-nn-algorithm.mp4

7.1 MB

/.../01_discussion-forums-and-mentors/

01__MentorProgramInformation.pdf

64.3 KB

01__resources.html

4.1 KB

/.../06_train-the-model-with-gradient-descent/

01_gradient-descent.en.srt

12.5 KB

02_implementing-gradient-descent.en.txt

7.7 KB

02_implementing-gradient-descent.en.srt

14.6 KB

04_learning-rate.en.srt

11.5 KB

03_gradient-descent-intuition.en.srt

10.2 KB

05_gradient-descent-for-linear-regression.en.srt

9.3 KB

06_running-gradient-descent.en.srt

7.5 KB

04_learning-rate.en.txt

7.0 KB

01_gradient-descent.en.txt

6.6 KB

03_gradient-descent-intuition.en.txt

5.4 KB

05_gradient-descent-for-linear-regression.en.txt

5.0 KB

06_running-gradient-descent.en.txt

4.7 KB

01_gradient-descent.mp4

23.6 MB

02_implementing-gradient-descent.mp4

21.9 MB

06_running-gradient-descent.mp4

19.3 MB

04_learning-rate.mp4

17.8 MB

05_gradient-descent-for-linear-regression.mp4

17.2 MB

03_gradient-descent-intuition.mp4

13.8 MB

/.../01_reinforcement-learning-introduction/

03_the-return-in-reinforcement-learning.en.srt

16.0 KB

01_what-is-reinforcement-learning.en.srt

12.8 KB

02_mars-rover-example.en.srt

10.6 KB

05_review-of-key-concepts.en.srt

8.7 KB

03_the-return-in-reinforcement-learning.en.txt

8.4 KB

01_what-is-reinforcement-learning.en.txt

8.2 KB

02_mars-rover-example.en.txt

5.5 KB

05_review-of-key-concepts.en.txt

4.6 KB

04_making-decisions-policies-in-reinforcement-learning.en.srt

3.8 KB

04_making-decisions-policies-in-reinforcement-learning.en.txt

2.0 KB

01_what-is-reinforcement-learning.mp4

32.5 MB

03_the-return-in-reinforcement-learning.mp4

30.4 MB

02_mars-rover-example.mp4

13.3 MB

05_review-of-key-concepts.mp4

11.9 MB

04_making-decisions-policies-in-reinforcement-learning.mp4

6.1 MB

/.../03_gradient-descent-in-practice/

05_feature-engineering.en.txt

2.6 KB

02_feature-scaling-part-2.en.srt

11.1 KB

04_choosing-the-learning-rate.en.srt

9.8 KB

06_polynomial-regression.en.srt

9.7 KB

03_checking-gradient-descent-for-convergence.en.srt

8.5 KB

01_feature-scaling-part-1.en.srt

7.7 KB

02_feature-scaling-part-2.en.txt

5.7 KB

04_choosing-the-learning-rate.en.txt

5.2 KB

06_polynomial-regression.en.txt

5.1 KB

05_feature-engineering.en.srt

5.0 KB

01_feature-scaling-part-1.en.txt

4.9 KB

03_checking-gradient-descent-for-convergence.en.txt

4.4 KB

06_polynomial-regression.mp4

23.9 MB

04_choosing-the-learning-rate.mp4

17.1 MB

02_feature-scaling-part-2.mp4

15.1 MB

01_feature-scaling-part-1.mp4

14.3 MB

03_checking-gradient-descent-for-convergence.mp4

11.5 MB

05_feature-engineering.mp4

8.2 MB

/.../05_week-2-practice-lab-linear-regression/

01_week-2-practice-lab-linear-regression_instructions.html

2.6 KB

/.../05_programming-assignment/

01_decision-trees-in-practice_module-6-assignment-train-idx.json.zip

56.4 KB

01_decision-trees-in-practice_instructions.html

39.6 KB

01_decision-trees-in-practice_module-6-assignment-validation-idx.json.zip

19.4 KB

01_decision-trees-in-practice_CLA06-NB01.ipynb.zip

8.8 KB

01_decision-trees-in-practice_module-6-decision-tree-practical-assignment-blank.zip

8.4 KB

01_decision-trees-in-practice_lending-club-data.gl.zip

20.3 MB

01_decision-trees-in-practice_lending-club-data.sframe.zip

20.3 MB

01_decision-trees-in-practice_lending-club-data.csv.zip

19.4 MB

/.../02_practice-quiz-classification-with-logistic-regression/

01_practice-quiz-classification-with-logistic-regression_exam.html

52.7 KB

/.../03_summary-of-classification/

02_classification_exam.html

36.4 KB

01_classification-ml-block-diagram.en.srt

4.1 KB

01_classification-ml-block-diagram.en.txt

2.6 KB

01_classification-ml-block-diagram.mp4

11.2 MB

/.../02_machine-learning-challenges-and-future-directions/

02_where-is-ml-going.mp4

39.6 MB

01_open-challenges-in-ml.en.srt

12.3 KB

02_where-is-ml-going.en.srt

11.4 KB

01_open-challenges-in-ml.mp4

34.9 MB

03_whats-ahead-in-the-specialization.en.srt

8.4 KB

01_open-challenges-in-ml.en.txt

7.8 KB

02_where-is-ml-going.en.txt

6.7 KB

03_whats-ahead-in-the-specialization.en.txt

5.4 KB

04_thank-you.en.srt

2.4 KB

04_thank-you.en.txt

1.4 KB

03_whats-ahead-in-the-specialization.mp4

23.3 MB

04_thank-you.mp4

8.6 MB

/.../05_finding-the-least-squares-line/

05_optional-reading-worked-out-example-for-gradient-descent_instructions.html

30.2 KB

03_optional-reading-worked-out-example-for-closed-form-solution_instructions.html

12.8 KB

01_computing-the-gradient-of-rss.en.srt

8.5 KB

04_approach-2-gradient-descent.en.srt

7.1 KB

02_approach-1-closed-form-solution.en.srt

5.6 KB

01_computing-the-gradient-of-rss.en.txt

5.0 KB

04_approach-2-gradient-descent.en.txt

4.0 KB

02_approach-1-closed-form-solution.en.txt

3.3 KB

06_comparing-the-approaches.en.srt

2.2 KB

06_comparing-the-approaches.en.txt

1.4 KB

04_approach-2-gradient-descent.mp4

19.3 MB

01_computing-the-gradient-of-rss.mp4

17.9 MB

02_approach-1-closed-form-solution.mp4

14.8 MB

06_comparing-the-approaches.mp4

6.5 MB

/.../01_algorithms-for-retrieval-and-measuring-similarity-of-documents/

05_prioritizing-important-words-with-tf-idf.en.srt

5.1 KB

04_word-count-representation-for-measuring-similarity.en.srt

9.9 KB

04_word-count-representation-for-measuring-similarity.en.txt

6.1 KB

06_calculating-tf-idf-vectors.en.srt

6.1 KB

03_what-is-the-document-retrieval-task.en.srt

2.2 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity.en.srt

0.9 KB

02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity.en.txt

0.5 KB

03_what-is-the-document-retrieval-task.en.txt

1.4 KB

07_retrieving-similar-documents-using-nearest-neighbor-search.en.txt

2.3 KB

06_calculating-tf-idf-vectors.en.txt

3.8 KB

07_retrieving-similar-documents-using-nearest-neighbor-search.en.srt

3.6 KB

05_prioritizing-important-words-with-tf-idf.en.txt

3.2 KB

04_word-count-representation-for-measuring-similarity.mp4

23.4 MB

06_calculating-tf-idf-vectors.mp4

15.2 MB

01_slides-presented-in-this-module_clustering-intro-annotated.pdf

14.8 MB

05_prioritizing-important-words-with-tf-idf.mp4

14.2 MB

07_retrieving-similar-documents-using-nearest-neighbor-search.mp4

9.2 MB

03_what-is-the-document-retrieval-task.mp4

6.7 MB

02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity.mp4

2.2 MB

/.../03_continuous-state-spaces/

03_learning-the-state-value-function.en.srt

25.8 KB

06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.srt

18.1 KB

05_algorithm-refinement-greedy-policy.en.srt

14.6 KB

03_learning-the-state-value-function.en.txt

13.2 KB

01_example-of-continuous-state-space-applications.en.srt

9.9 KB

06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.txt

9.7 KB

02_lunar-lander.en.srt

7.9 KB

05_algorithm-refinement-greedy-policy.en.txt

7.7 KB

01_example-of-continuous-state-space-applications.en.txt

5.2 KB

02_lunar-lander.en.txt

5.0 KB

04_algorithm-refinement-improved-neural-network-architecture.en.srt

4.8 KB

07_the-state-of-reinforcement-learning.en.srt

4.1 KB

07_the-state-of-reinforcement-learning.en.txt

2.7 KB

04_algorithm-refinement-improved-neural-network-architecture.en.txt

2.5 KB

03_learning-the-state-value-function.mp4

32.7 MB

01_example-of-continuous-state-space-applications.mp4

28.4 MB

06_algorithm-refinement-mini-batch-and-soft-updates-optional.mp4

26.8 MB

05_algorithm-refinement-greedy-policy.mp4

26.5 MB

02_lunar-lander.mp4

10.9 MB

07_the-state-of-reinforcement-learning.mp4

8.2 MB

04_algorithm-refinement-improved-neural-network-architecture.mp4

8.2 MB

/.../07_practice-quiz-train-the-model-with-gradient-descent/

01_practice-quiz-train-the-model-with-gradient-descent_exam.html

21.9 KB

/.../01_advice-for-applying-machine-learning/

03_model-selection-and-training-cross-validation-test-sets.en.srt

21.8 KB

02_evaluating-a-model.en.srt

13.1 KB

03_model-selection-and-training-cross-validation-test-sets.en.txt

11.5 KB

02_evaluating-a-model.en.txt

8.3 KB

01_deciding-what-to-try-next.en.srt

6.9 KB

01_deciding-what-to-try-next.en.txt

3.6 KB

03_model-selection-and-training-cross-validation-test-sets.mp4

31.1 MB

02_evaluating-a-model.mp4

20.4 MB

01_deciding-what-to-try-next.mp4

12.0 MB

/.../01_neural-network-training/

02_training-details.en.srt

21.4 KB

02_training-details.en.txt

11.2 KB

01_tensorflow-implementation.en.srt

6.1 KB

01_tensorflow-implementation.en.txt

3.2 KB

02_training-details.mp4

25.3 MB

01_tensorflow-implementation.mp4

11.9 MB

/.../09_week-3-practice-lab-logistic-regression/

01_week-3-practice-lab-logistic-regression_instructions.html

1.1 KB

/.../04_regression-model/

04_cost-function-intuition.en.srt

20.7 KB

01_linear-regression-model-part-1.en.srt

15.1 KB

05_visualizing-the-cost-function.en.srt

12.6 KB

03_cost-function-formula.en.srt

12.2 KB

04_cost-function-intuition.en.txt

10.4 KB

02_linear-regression-model-part-2.en.srt

9.8 KB

06_visualization-examples.en.srt

8.9 KB

01_linear-regression-model-part-1.en.txt

7.8 KB

05_visualizing-the-cost-function.en.txt

6.5 KB

03_cost-function-formula.en.txt

6.4 KB

02_linear-regression-model-part-2.en.txt

5.2 KB

06_visualization-examples.en.txt

4.7 KB

04_cost-function-intuition.mp4

31.0 MB

01_linear-regression-model-part-1.mp4

21.2 MB

05_visualizing-the-cost-function.mp4

18.2 MB

06_visualization-examples.mp4

18.0 MB

03_cost-function-formula.mp4

17.5 MB

02_linear-regression-model-part-2.mp4

17.0 MB

/.../03_machine-learning-development-process/

04_transfer-learning-using-data-from-a-different-task.en.srt

20.7 KB

03_adding-data.en.srt

19.4 KB

05_full-cycle-of-a-machine-learning-project.en.srt

14.9 KB

06_fairness-bias-and-ethics.en.srt

13.7 KB

02_error-analysis.en.srt

13.5 KB

01_iterative-loop-of-ml-development.en.srt

12.4 KB

03_adding-data.en.txt

12.3 KB

04_transfer-learning-using-data-from-a-different-task.en.txt

10.8 KB

06_fairness-bias-and-ethics.en.txt

8.9 KB

03_adding-data.mp4

34.5 MB

05_full-cycle-of-a-machine-learning-project.en.txt

7.9 KB

02_error-analysis.en.txt

7.2 KB

01_iterative-loop-of-ml-development.en.txt

6.6 KB

06_fairness-bias-and-ethics.mp4

26.6 MB

04_transfer-learning-using-data-from-a-different-task.mp4

19.9 MB

02_error-analysis.mp4

18.4 MB

05_full-cycle-of-a-machine-learning-project.mp4

17.1 MB

01_iterative-loop-of-ml-development.mp4

15.5 MB

/.../02_bias-and-variance/

04_learning-curves.en.srt

20.5 KB

01_diagnosing-bias-and-variance.en.srt

18.2 KB

02_regularization-and-bias-variance.en.srt

16.7 KB

03_establishing-a-baseline-level-of-performance.en.srt

16.2 KB

06_bias-variance-and-neural-networks.en.srt

14.9 KB

05_deciding-what-to-try-next-revisited.en.srt

14.9 KB

04_learning-curves.en.txt

10.7 KB

06_bias-variance-and-neural-networks.en.txt

9.7 KB

01_diagnosing-bias-and-variance.en.txt

9.5 KB

02_regularization-and-bias-variance.en.txt

8.5 KB

03_establishing-a-baseline-level-of-performance.en.txt

8.5 KB

05_deciding-what-to-try-next-revisited.en.txt

7.9 KB

05_deciding-what-to-try-next-revisited.mp4

29.4 MB

06_bias-variance-and-neural-networks.mp4

28.2 MB

04_learning-curves.mp4

24.4 MB

02_regularization-and-bias-variance.mp4

22.1 MB

01_diagnosing-bias-and-variance.mp4

21.3 MB

03_establishing-a-baseline-level-of-performance.mp4

20.3 MB

/.../03_anomaly-detection/

06_choosing-what-features-to-use.en.srt

19.1 KB

04_developing-and-evaluating-an-anomaly-detection-system.en.srt

17.9 KB

02_gaussian-normal-distribution.en.srt

15.3 KB

01_finding-unusual-events.en.srt

14.9 KB

03_anomaly-detection-algorithm.en.srt

13.7 KB

06_choosing-what-features-to-use.en.txt

12.2 KB

05_anomaly-detection-vs-supervised-learning.en.srt

11.4 KB

01_finding-unusual-events.en.txt

9.6 KB

04_developing-and-evaluating-an-anomaly-detection-system.en.txt

9.5 KB

03_anomaly-detection-algorithm.en.txt

8.6 KB

02_gaussian-normal-distribution.en.txt

8.0 KB

05_anomaly-detection-vs-supervised-learning.en.txt

7.3 KB

06_choosing-what-features-to-use.mp4

32.4 MB

01_finding-unusual-events.mp4

27.6 MB

04_developing-and-evaluating-an-anomaly-detection-system.mp4

25.1 MB

02_gaussian-normal-distribution.mp4

21.9 MB

03_anomaly-detection-algorithm.mp4

21.3 MB

05_anomaly-detection-vs-supervised-learning.mp4

21.3 MB

/.../07_the-problem-of-overfitting/

01_the-problem-of-overfitting.en.srt

18.8 KB

02_addressing-overfitting.en.srt

13.2 KB

04_regularized-linear-regression.en.srt

12.3 KB

03_cost-function-with-regularization.en.srt

11.6 KB

01_the-problem-of-overfitting.en.txt

9.8 KB

05_regularized-logistic-regression.en.srt

8.9 KB

03_cost-function-with-regularization.en.txt

7.3 KB

02_addressing-overfitting.en.txt

7.0 KB

04_regularized-linear-regression.en.txt

6.5 KB

05_regularized-logistic-regression.en.txt

4.8 KB

01_the-problem-of-overfitting.mp4

25.1 MB

05_regularized-logistic-regression.mp4

21.9 MB

04_regularized-linear-regression.mp4

20.8 MB

03_cost-function-with-regularization.mp4

17.9 MB

02_addressing-overfitting.mp4

16.5 MB

/.../04_skewed-datasets-optional/

02_trading-off-precision-and-recall.en.srt

18.7 KB

01_error-metrics-for-skewed-datasets.en.srt

17.4 KB

02_trading-off-precision-and-recall.en.txt

9.8 KB

01_error-metrics-for-skewed-datasets.en.txt

9.1 KB

02_trading-off-precision-and-recall.mp4

23.2 MB

01_error-metrics-for-skewed-datasets.mp4

19.9 MB

/.../01_decision-trees/

02_learning-process.en.srt

18.5 KB

01_decision-tree-model.en.srt

11.1 KB

02_learning-process.en.txt

9.6 KB

01_decision-tree-model.en.txt

5.8 KB

02_learning-process.mp4

30.4 MB

01_decision-tree-model.mp4

15.5 MB

/.../02_state-action-value-function/

03_bellman-equation.en.srt

18.2 KB

01_state-action-value-function-definition.en.srt

13.8 KB

04_random-stochastic-environment-optional.en.srt

13.4 KB

03_bellman-equation.en.txt

9.4 KB

01_state-action-value-function-definition.en.txt

8.5 KB

04_random-stochastic-environment-optional.en.txt

7.1 KB

02_state-action-value-function-example.en.srt

6.9 KB

02_state-action-value-function-example.en.txt

4.4 KB

03_bellman-equation.mp4

28.0 MB

01_state-action-value-function-definition.mp4

20.8 MB

04_random-stochastic-environment-optional.mp4

20.2 MB

02_state-action-value-function-example.mp4

15.3 MB

/.../02_decision-tree-learning/

02_choosing-a-split-information-gain.en.srt

17.8 KB

03_putting-it-together.en.srt

14.9 KB

06_regression-trees-optional.en.srt

12.5 KB

01_measuring-purity.en.srt

10.4 KB

02_choosing-a-split-information-gain.en.txt

9.2 KB

05_continuous-valued-features.en.srt

8.8 KB

03_putting-it-together.en.txt

7.9 KB

06_regression-trees-optional.en.txt

7.9 KB

04_using-one-hot-encoding-of-categorical-features.en.srt

6.8 KB

05_continuous-valued-features.en.txt

5.6 KB

01_measuring-purity.en.txt

5.5 KB

04_using-one-hot-encoding-of-categorical-features.en.txt

4.4 KB

02_choosing-a-split-information-gain.mp4

24.9 MB

06_regression-trees-optional.mp4

19.8 MB

03_putting-it-together.mp4

19.3 MB

01_measuring-purity.mp4

16.7 MB

05_continuous-valued-features.mp4

16.7 MB

04_using-one-hot-encoding-of-categorical-features.mp4

14.9 MB

/.../03_cost-function-for-logistic-regression/

01_cost-function-for-logistic-regression.en.srt

17.5 KB

01_cost-function-for-logistic-regression.en.txt

9.1 KB

02_simplified-cost-function-for-logistic-regression.en.srt

7.8 KB

02_simplified-cost-function-for-logistic-regression.en.txt

4.0 KB

01_cost-function-for-logistic-regression.mp4

25.8 MB

02_simplified-cost-function-for-logistic-regression.mp4

12.3 MB

/.../05_speculations-on-artificial-general-intelligence-agi/

01_is-there-a-path-to-agi.en.srt

16.8 KB

01_is-there-a-path-to-agi.en.txt

8.8 KB

01_is-there-a-path-to-agi.mp4

29.5 MB

/.../01_what-we-ve-learned/

02_module-1-recap.en.srt

15.9 KB

05_module-4-recap.en.srt

10.7 KB

02_module-1-recap.en.txt

10.2 KB

04_module-3-recap.en.srt

8.8 KB

05_module-4-recap.en.txt

6.9 KB

04_module-3-recap.en.txt

5.8 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

03_module-2-recap.en.srt

4.8 KB

03_module-2-recap.en.txt

3.1 KB

02_module-1-recap.mp4

30.7 MB

01_slides-presented-in-this-module_closing-annotated.pdf

27.3 MB

05_module-4-recap.mp4

27.2 MB

04_module-3-recap.mp4

20.7 MB

03_module-2-recap.mp4

11.1 MB

/.../01_unsupervised-learning/02_clustering/

03_k-means-algorithm.en.srt

14.6 KB

04_optimization-objective.en.srt

14.0 KB

06_choosing-the-number-of-clusters.en.srt

11.4 KB

05_initializing-k-means.en.srt

11.0 KB

02_k-means-intuition.en.srt

9.2 KB

04_optimization-objective.en.txt

8.8 KB

03_k-means-algorithm.en.txt

7.6 KB

05_initializing-k-means.en.txt

7.1 KB

01_what-is-clustering.en.srt

6.0 KB

06_choosing-the-number-of-clusters.en.txt

6.0 KB

02_k-means-intuition.en.txt

5.8 KB

01_what-is-clustering.en.txt

3.2 KB

04_optimization-objective.mp4

30.9 MB

03_k-means-algorithm.mp4

20.7 MB

05_initializing-k-means.mp4

18.7 MB

06_choosing-the-number-of-clusters.mp4

17.7 MB

02_k-means-intuition.mp4

13.0 MB

01_what-is-clustering.mp4

9.2 MB

/.../01_classification-with-logistic-regression/

03_decision-boundary.en.srt

14.5 KB

02_logistic-regression.en.srt

13.7 KB

01_motivations.en.srt

13.0 KB

01_motivations.en.txt

8.0 KB

03_decision-boundary.en.txt

7.5 KB

02_logistic-regression.en.txt

7.2 KB

02_logistic-regression.mp4

22.5 MB

01_motivations.mp4

22.0 MB

03_decision-boundary.mp4

19.9 MB

/.../02_activation-functions/

02_choosing-activation-functions.en.srt

14.1 KB

03_why-do-we-need-activation-functions.en.srt

7.8 KB

02_choosing-activation-functions.en.txt

7.4 KB

01_alternatives-to-the-sigmoid-activation.en.srt

7.0 KB

01_alternatives-to-the-sigmoid-activation.en.txt

4.4 KB

03_why-do-we-need-activation-functions.en.txt

4.1 KB

02_choosing-activation-functions.mp4

24.5 MB

03_why-do-we-need-activation-functions.mp4

13.6 MB

01_alternatives-to-the-sigmoid-activation.mp4

12.5 MB

/.../03_logistic-regression/

03_logistic-regression-model.en.txt

2.8 KB

05_overview-of-learning-logistic-regression-models.en.txt

2.1 KB

04_effect-of-coefficient-values-on-predicted-probabilities.en.srt

8.4 KB

01_predicting-class-probabilities-with-generalized-linear-models.en.srt

7.0 KB

02_the-sigmoid-or-logistic-link-function.en.srt

5.7 KB

04_effect-of-coefficient-values-on-predicted-probabilities.en.txt

5.1 KB

03_logistic-regression-model.en.srt

4.9 KB

01_predicting-class-probabilities-with-generalized-linear-models.en.txt

4.4 KB

02_the-sigmoid-or-logistic-link-function.en.txt

3.3 KB

05_overview-of-learning-logistic-regression-models.en.srt

3.2 KB

01_predicting-class-probabilities-with-generalized-linear-models.mp4

20.8 MB

04_effect-of-coefficient-values-on-predicted-probabilities.mp4

17.8 MB

02_the-sigmoid-or-logistic-link-function.mp4

12.6 MB

03_logistic-regression-model.mp4

12.0 MB

05_overview-of-learning-logistic-regression-models.mp4

8.2 MB

/.../02_hierarchical-clustering-and-clustering-for-time-series-segmentation/

06_hidden-markov-models.en.srt

13.9 KB

05_agglomerative-clustering-details.en.srt

10.3 KB

06_hidden-markov-models.en.txt

8.9 KB

04_the-dendrogram.en.srt

7.1 KB

05_agglomerative-clustering-details.en.txt

6.7 KB

02_divisive-clustering.en.srt

6.4 KB

01_why-hierarchical-clustering.en.txt

2.4 KB

03_agglomerative-clustering.en.txt

2.6 KB

04_the-dendrogram.en.txt

4.6 KB

03_agglomerative-clustering.en.srt

4.1 KB

02_divisive-clustering.en.txt

4.1 KB

01_why-hierarchical-clustering.en.srt

3.8 KB

06_hidden-markov-models.mp4

33.7 MB

05_agglomerative-clustering-details.mp4

21.2 MB

04_the-dendrogram.mp4

16.1 MB

02_divisive-clustering.mp4

13.7 MB

03_agglomerative-clustering.mp4

9.1 MB

01_why-hierarchical-clustering.mp4

8.1 MB

/.../01_multiple-linear-regression/

01_multiple-features.en.srt

13.8 KB

04_gradient-descent-for-multiple-linear-regression.en.srt

11.4 KB

03_vectorization-part-2.en.srt

10.3 KB

02_vectorization-part-1.en.srt

9.9 KB

01_multiple-features.en.txt

7.1 KB

04_gradient-descent-for-multiple-linear-regression.en.txt

6.0 KB

03_vectorization-part-2.en.txt

5.4 KB

02_vectorization-part-1.en.txt

5.2 KB

04_gradient-descent-for-multiple-linear-regression.mp4

20.3 MB

01_multiple-features.mp4

19.8 MB

02_vectorization-part-1.mp4

18.1 MB

03_vectorization-part-2.mp4

18.1 MB

/.../05_summarizing-linear-classifiers-logistic-regression/

01_recap-of-logistic-regression-classifier.en.srt

1.9 KB

01_recap-of-logistic-regression-classifier.en.txt

1.2 KB

01_recap-of-logistic-regression-classifier.mp4

5.5 MB

/.../03_tensorflow-implementation/

02_data-in-tensorflow.en.srt

13.5 KB

03_building-a-neural-network.en.srt

11.0 KB

01_inference-in-code.en.srt

10.3 KB

02_data-in-tensorflow.en.txt

8.5 KB

03_building-a-neural-network.en.txt

7.0 KB

01_inference-in-code.en.txt

5.4 KB

02_data-in-tensorflow.mp4

26.0 MB

03_building-a-neural-network.mp4

25.6 MB

01_inference-in-code.mp4

17.6 MB

/.../01_maximum-likelihood-estimation/

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_goal-learning-parameters-of-logistic-regression.en.srt

3.0 KB

02_goal-learning-parameters-of-logistic-regression.en.txt

1.9 KB

05_finding-best-linear-classifier-with-gradient-ascent.en.txt

2.8 KB

04_data-likelihood.en.srt

10.2 KB

04_data-likelihood.en.txt

6.1 KB

03_intuition-behind-maximum-likelihood-estimation.en.srt

5.9 KB

05_finding-best-linear-classifier-with-gradient-ascent.en.srt

4.4 KB

03_intuition-behind-maximum-likelihood-estimation.en.txt

3.7 KB

04_data-likelihood.mp4

19.2 MB

03_intuition-behind-maximum-likelihood-estimation.mp4

11.9 MB

05_finding-best-linear-classifier-with-gradient-ascent.mp4

10.4 MB

02_goal-learning-parameters-of-logistic-regression.mp4

9.3 MB

01_slides-presented-in-this-module_logistic-regression-learning-annotated.pdf

3.2 MB

/.../02_neural-network-model/

01_neural-network-layer.en.srt

13.3 KB

02_more-complex-neural-networks.en.srt

12.0 KB

03_inference-making-predictions-forward-propagation.en.srt

7.1 KB

01_neural-network-layer.en.txt

7.0 KB

02_more-complex-neural-networks.en.txt

6.2 KB

03_inference-making-predictions-forward-propagation.en.txt

4.4 KB

01_neural-network-layer.mp4

21.4 MB

02_more-complex-neural-networks.mp4

17.9 MB

03_inference-making-predictions-forward-propagation.mp4

13.2 MB

/.../02_gradient-ascent-algorithm-for-learning-logistic-regression-classifier/

02_learning-algorithm-for-logistic-regression.en.txt

2.6 KB

05_summary-of-gradient-ascent-for-logistic-regression.en.srt

2.7 KB

05_summary-of-gradient-ascent-for-logistic-regression.en.txt

1.6 KB

01_review-of-gradient-ascent.en.srt

7.9 KB

03_example-of-computing-derivative-for-logistic-regression.en.srt

7.3 KB

04_interpreting-derivative-for-logistic-regression.en.srt

6.7 KB

01_review-of-gradient-ascent.en.txt

4.9 KB

03_example-of-computing-derivative-for-logistic-regression.en.txt

4.4 KB

02_learning-algorithm-for-logistic-regression.en.srt

4.2 KB

04_interpreting-derivative-for-logistic-regression.en.txt

4.0 KB

01_review-of-gradient-ascent.mp4

16.2 MB

03_example-of-computing-derivative-for-logistic-regression.mp4

14.4 MB

04_interpreting-derivative-for-logistic-regression.mp4

14.3 MB

02_learning-algorithm-for-logistic-regression.mp4

8.4 MB

05_summary-of-gradient-ascent-for-logistic-regression.mp4

6.5 MB

/.../06_vectorization-optional/

02_matrix-multiplication.en.srt

12.6 KB

03_matrix-multiplication-rules.en.srt

11.7 KB

04_matrix-multiplication-code.en.srt

9.0 KB

03_matrix-multiplication-rules.en.txt

7.2 KB

02_matrix-multiplication.en.txt

6.5 KB

01_how-neural-networks-are-implemented-efficiently.en.srt

6.3 KB

04_matrix-multiplication-code.en.txt

4.7 KB

01_how-neural-networks-are-implemented-efficiently.en.txt

3.2 KB

03_matrix-multiplication-rules.mp4

16.9 MB

02_matrix-multiplication.mp4

16.7 MB

04_matrix-multiplication-code.mp4

14.0 MB

01_how-neural-networks-are-implemented-efficiently.mp4

12.8 MB

/.../03_choosing-step-size-for-gradient-ascent-descent/

03_rule-of-thumb-for-choosing-step-size.en.txt

2.7 KB

01_choosing-step-size.en.srt

7.8 KB

02_careful-with-step-sizes-that-are-too-large.en.srt

5.4 KB

01_choosing-step-size.en.txt

4.8 KB

03_rule-of-thumb-for-choosing-step-size.en.srt

4.4 KB

02_careful-with-step-sizes-that-are-too-large.en.txt

3.3 KB

01_choosing-step-size.mp4

15.1 MB

02_careful-with-step-sizes-that-are-too-large.mp4

11.7 MB

03_rule-of-thumb-for-choosing-step-size.mp4

9.8 MB

/.../04_neural-network-implementation-in-python/

02_general-implementation-of-forward-propagation.en.srt

11.9 KB

01_forward-prop-in-a-single-layer.en.srt

6.2 KB

02_general-implementation-of-forward-propagation.en.txt

6.2 KB

01_forward-prop-in-a-single-layer.en.txt

3.9 KB

02_general-implementation-of-forward-propagation.mp4

22.4 MB

01_forward-prop-in-a-single-layer.mp4

13.0 MB

/.../04_very-optional-lesson-deriving-gradient-of-logistic-regression/

02_very-optional-expressing-the-log-likelihood.en.txt

2.3 KB

03_very-optional-deriving-probability-y-1-given-x.en.srt

2.2 KB

03_very-optional-deriving-probability-y-1-given-x.en.txt

1.2 KB

05_very-optional-deriving-gradient-of-log-likelihood.en.srt

8.7 KB

04_very-optional-rewriting-the-log-likelihood-into-a-simpler-form.en.srt

8.2 KB

01_very-optional-deriving-gradient-of-logistic-regression-log-trick.en.srt

5.7 KB

05_very-optional-deriving-gradient-of-log-likelihood.en.txt

5.2 KB

04_very-optional-rewriting-the-log-likelihood-into-a-simpler-form.en.txt

4.8 KB

02_very-optional-expressing-the-log-likelihood.en.srt

3.8 KB

01_very-optional-deriving-gradient-of-logistic-regression-log-trick.en.txt

3.5 KB

04_very-optional-rewriting-the-log-likelihood-into-a-simpler-form.mp4

18.9 MB

05_very-optional-deriving-gradient-of-log-likelihood.mp4

18.7 MB

01_very-optional-deriving-gradient-of-logistic-regression-log-trick.mp4

13.5 MB

02_very-optional-expressing-the-log-likelihood.mp4

7.4 MB

03_very-optional-deriving-probability-y-1-given-x.mp4

5.2 MB

/.../04_additional-neural-network-concepts/

02_additional-layer-types.en.srt

11.6 KB

01_advanced-optimization.en.srt

10.8 KB

02_additional-layer-types.en.txt

7.4 KB

01_advanced-optimization.en.txt

5.7 KB

02_additional-layer-types.mp4

20.5 MB

01_advanced-optimization.mp4

16.3 MB

/.../05_summarizing-learning-linear-classifiers/

01_recap-of-learning-logistic-regression-classifiers.en.srt

2.6 KB

01_recap-of-learning-logistic-regression-classifiers.en.txt

1.6 KB

01_recap-of-learning-logistic-regression-classifiers.mp4

7.3 MB

/.../02_clustering-via-k-means/

04_assessing-the-quality-and-choosing-the-number-of-clusters.en.srt

11.3 KB

02_k-means-as-coordinate-descent.en.srt

8.6 KB

01_the-k-means-algorithm.en.srt

8.1 KB

04_assessing-the-quality-and-choosing-the-number-of-clusters.en.txt

6.9 KB

02_k-means-as-coordinate-descent.en.txt

5.5 KB

03_smart-initialization-via-k-means.en.srt

5.4 KB

01_the-k-means-algorithm.en.txt

5.0 KB

03_smart-initialization-via-k-means.en.txt

3.3 KB

04_assessing-the-quality-and-choosing-the-number-of-clusters.mp4

22.2 MB

02_k-means-as-coordinate-descent.mp4

18.6 MB

01_the-k-means-algorithm.mp4

18.1 MB

03_smart-initialization-via-k-means.mp4

12.7 MB

/.../02_overconfident-predictions-due-to-overfitting/

03_optional-another-perspecting-on-overfitting-in-logistic-regression.en.srt

11.2 KB

03_optional-another-perspecting-on-overfitting-in-logistic-regression.en.txt

7.0 KB

01_overfitting-in-classifiers-leads-to-overconfident-predictions.en.srt

6.7 KB

02_visualizing-overconfident-predictions.en.srt

5.7 KB

01_overfitting-in-classifiers-leads-to-overconfident-predictions.en.txt

4.1 KB

02_visualizing-overconfident-predictions.en.txt

3.6 KB

03_optional-another-perspecting-on-overfitting-in-logistic-regression.mp4

22.7 MB

01_overfitting-in-classifiers-leads-to-overconfident-predictions.mp4

14.8 MB

02_visualizing-overconfident-predictions.mp4

10.7 MB

/.../01_overfitting-in-classification/

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_evaluating-a-classifier.en.txt

2.9 KB

03_review-of-overfitting-in-regression.en.txt

3.0 KB

04_overfitting-in-classification.en.srt

6.6 KB

05_visualizing-overfitting-with-high-degree-polynomial-features.en.srt

5.1 KB

03_review-of-overfitting-in-regression.en.srt

4.8 KB

02_evaluating-a-classifier.en.srt

4.7 KB

04_overfitting-in-classification.en.txt

4.1 KB

05_visualizing-overfitting-with-high-degree-polynomial-features.en.txt

3.2 KB

04_overfitting-in-classification.mp4

14.9 MB

05_visualizing-overfitting-with-high-degree-polynomial-features.mp4

10.4 MB

03_review-of-overfitting-in-regression.mp4

10.1 MB

02_evaluating-a-classifier.mp4

9.7 MB

01_slides-presented-in-this-module_logistic-regression-overfitting-annotated.pdf

2.7 MB

/.../03_tree-ensembles/

05_when-to-use-decision-trees.en.srt

10.7 KB

04_xgboost.en.srt

9.6 KB

03_random-forest-algorithm.en.srt

9.0 KB

02_sampling-with-replacement.en.srt

6.7 KB

01_using-multiple-decision-trees.en.srt

6.6 KB

04_xgboost.en.txt

6.2 KB

05_when-to-use-decision-trees.en.txt

5.7 KB

03_random-forest-algorithm.en.txt

5.7 KB

02_sampling-with-replacement.en.txt

3.5 KB

01_using-multiple-decision-trees.en.txt

3.5 KB

04_xgboost.mp4

22.1 MB

05_when-to-use-decision-trees.mp4

18.3 MB

02_sampling-with-replacement.mp4

15.0 MB

03_random-forest-algorithm.mp4

13.3 MB

01_using-multiple-decision-trees.mp4

13.1 MB

/.../03_optional-lesson-pruning-decision-trees/

01_optional-motivating-pruning.en.srt

10.6 KB

02_optional-pruning-decision-trees-to-avoid-overfitting.en.srt

7.7 KB

01_optional-motivating-pruning.en.txt

6.5 KB

03_optional-tree-pruning-algorithm.en.srt

4.8 KB

02_optional-pruning-decision-trees-to-avoid-overfitting.en.txt

4.8 KB

03_optional-tree-pruning-algorithm.en.txt

3.1 KB

01_optional-motivating-pruning.mp4

22.7 MB

02_optional-pruning-decision-trees-to-avoid-overfitting.mp4

17.5 MB

03_optional-tree-pruning-algorithm.mp4

12.5 MB

/.../05_putting-the-pieces-together/

01_training-validation-test-split-for-model-selection-fitting-and-assessment.en.srt

10.5 KB

01_training-validation-test-split-for-model-selection-fitting-and-assessment.en.txt

6.6 KB

02_a-brief-recap.en.srt

2.0 KB

02_a-brief-recap.en.txt

1.2 KB

01_training-validation-test-split-for-model-selection-fitting-and-assessment.mp4

26.6 MB

02_a-brief-recap.mp4

5.3 MB

/.../04_mapreduce-for-scaling-k-means/

01_motivating-mapreduce.en.srt

10.5 KB

04_mapreduce-for-k-means.en.srt

8.2 KB

03_mapreduce-execution-overview-and-combiners.en.srt

6.8 KB

01_motivating-mapreduce.en.txt

6.4 KB

02_the-general-mapreduce-abstraction.en.srt

5.7 KB

04_mapreduce-for-k-means.en.txt

4.9 KB

03_mapreduce-execution-overview-and-combiners.en.txt

4.1 KB

02_the-general-mapreduce-abstraction.en.txt

3.4 KB

01_motivating-mapreduce.mp4

21.4 MB

04_mapreduce-for-k-means.mp4

19.4 MB

03_mapreduce-execution-overview-and-combiners.mp4

15.1 MB

02_the-general-mapreduce-abstraction.mp4

12.7 MB

/.../01_what-we-ve-learned/

03_assessing-performance-and-ridge-regression.en.srt

10.4 KB

03_assessing-performance-and-ridge-regression.en.txt

6.7 KB

02_simple-and-multiple-regression.en.srt

6.7 KB

04_feature-selection-lasso-and-nearest-neighbor-regression.en.srt

6.1 KB

02_simple-and-multiple-regression.en.txt

4.3 KB

04_feature-selection-lasso-and-nearest-neighbor-regression.en.txt

4.0 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

03_assessing-performance-and-ridge-regression.mp4

21.7 MB

02_simple-and-multiple-regression.mp4

13.7 MB

04_feature-selection-lasso-and-nearest-neighbor-regression.mp4

12.6 MB

01_slides-presented-in-this-module_closing.pdf

4.6 MB

/.../03_setting-the-stage-for-computing-the-least-squares-fit/

04_computing-the-cost-of-a-d-dimensional-curve.en.srt

10.4 KB

02_rewriting-the-single-observation-model-in-vector-notation.en.srt

8.0 KB

04_computing-the-cost-of-a-d-dimensional-curve.en.txt

6.2 KB

03_rewriting-the-model-for-all-observations-in-matrix-notation.en.srt

4.9 KB

02_rewriting-the-single-observation-model-in-vector-notation.en.txt

4.9 KB

03_rewriting-the-model-for-all-observations-in-matrix-notation.en.txt

3.0 KB

01_optional-reading-review-of-matrix-algebra_instructions.html

1.1 KB

04_computing-the-cost-of-a-d-dimensional-curve.mp4

22.3 MB

02_rewriting-the-single-observation-model-in-vector-notation.mp4

16.2 MB

03_rewriting-the-model-for-all-observations-in-matrix-notation.mp4

10.7 MB

/.../02_incorporating-multiple-inputs/

04_interpreting-the-multiple-regression-fit.en.srt

10.3 KB

04_interpreting-the-multiple-regression-fit.en.txt

6.6 KB

01_motivating-the-use-of-multiple-inputs.en.srt

6.1 KB

03_regression-with-features-of-multiple-inputs.en.srt

5.0 KB

02_defining-notation.en.srt

4.4 KB

01_motivating-the-use-of-multiple-inputs.en.txt

4.0 KB

03_regression-with-features-of-multiple-inputs.en.txt

3.1 KB

02_defining-notation.en.txt

2.8 KB

04_interpreting-the-multiple-regression-fit.mp4

25.9 MB

01_motivating-the-use-of-multiple-inputs.mp4

14.9 MB

03_regression-with-features-of-multiple-inputs.mp4

13.2 MB

02_defining-notation.mp4

12.4 MB

/.../03_optimizing-the-ridge-objective/

04_approach-2-gradient-descent.en.srt

10.3 KB

02_approach-1-closed-form-solution.en.srt

6.7 KB

03_discussing-the-closed-form-solution.en.srt

6.3 KB

04_approach-2-gradient-descent.en.txt

6.1 KB

01_computing-the-gradient-of-the-ridge-objective.en.srt

5.9 KB

02_approach-1-closed-form-solution.en.txt

4.0 KB

03_discussing-the-closed-form-solution.en.txt

3.8 KB

01_computing-the-gradient-of-the-ridge-objective.en.txt

3.7 KB

04_approach-2-gradient-descent.mp4

23.2 MB

01_computing-the-gradient-of-the-ridge-objective.mp4

15.5 MB

03_discussing-the-closed-form-solution.mp4

14.1 MB

02_approach-1-closed-form-solution.mp4

14.0 MB

/.../05_k-nn-and-kernel-regression-wrapup/

01_performance-of-nn-as-amount-of-data-grows.en.srt

10.2 KB

01_performance-of-nn-as-amount-of-data-grows.en.txt

6.5 KB

02_issues-with-high-dimensions-data-scarcity-and-computational-complexity.en.srt

4.6 KB

02_issues-with-high-dimensions-data-scarcity-and-computational-complexity.en.txt

2.9 KB

03_k-nn-for-classification.en.srt

2.6 KB

04_a-brief-recap.en.srt

2.1 KB

03_k-nn-for-classification.en.txt

1.6 KB

04_a-brief-recap.en.txt

1.3 KB

01_performance-of-nn-as-amount-of-data-grows.mp4

22.9 MB

02_issues-with-high-dimensions-data-scarcity-and-computational-complexity.mp4

10.3 MB

03_k-nn-for-classification.mp4

6.9 MB

04_a-brief-recap.mp4

4.9 MB

/.../01_feature-selection-via-explicit-model-enumeration/

05_greedy-algorithms.en.srt

10.2 KB

03_all-subsets.en.srt

7.4 KB

05_greedy-algorithms.en.txt

6.3 KB

02_the-feature-selection-task.en.srt

5.8 KB

03_all-subsets.en.txt

4.5 KB

04_complexity-of-all-subsets.en.srt

4.1 KB

06_complexity-of-the-greedy-forward-stepwise-algorithm.en.srt

3.8 KB

02_the-feature-selection-task.en.txt

3.6 KB

04_complexity-of-all-subsets.en.txt

2.5 KB

06_complexity-of-the-greedy-forward-stepwise-algorithm.en.txt

2.3 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

05_greedy-algorithms.mp4

19.6 MB

03_all-subsets.mp4

14.3 MB

02_the-feature-selection-task.mp4

12.1 MB

01_slides-presented-in-this-module_week5_lassoregression-annotated.pdf

9.2 MB

06_complexity-of-the-greedy-forward-stepwise-algorithm.mp4

8.8 MB

04_complexity-of-all-subsets.mp4

7.2 MB

/.../02_the-importance-of-data-representations-and-distance-metrics/

05_to-normalize-or-not-and-other-distance-considerations.en.srt

10.2 KB

04_distance-metrics-cosine-similarity.en.srt

10.1 KB

02_distance-metrics-euclidean-and-scaled-euclidean.en.srt

9.5 KB

01_document-representation.en.srt

7.7 KB

03_writing-scaled-euclidean-distance-using-weighted-inner-products.en.txt

2.6 KB

05_to-normalize-or-not-and-other-distance-considerations.en.txt

6.6 KB

04_distance-metrics-cosine-similarity.en.txt

6.3 KB

02_distance-metrics-euclidean-and-scaled-euclidean.en.txt

6.2 KB

01_document-representation.en.txt

4.8 KB

03_writing-scaled-euclidean-distance-using-weighted-inner-products.en.srt

4.5 KB

05_to-normalize-or-not-and-other-distance-considerations.mp4

22.6 MB

02_distance-metrics-euclidean-and-scaled-euclidean.mp4

22.1 MB

04_distance-metrics-cosine-similarity.mp4

21.1 MB

01_document-representation.mp4

14.8 MB

03_writing-scaled-euclidean-distance-using-weighted-inner-products.mp4

9.3 MB

/.../01_intuition-behind-decision-trees/

01_slides-presented-in-this-module_instructions.html

1.2 KB

03_intuition-behind-decision-trees.en.srt

2.2 KB

03_intuition-behind-decision-trees.en.txt

1.4 KB

04_task-of-learning-decision-trees-from-data.en.txt

3.0 KB

02_predicting-loan-defaults-with-decision-trees.en.srt

6.3 KB

04_task-of-learning-decision-trees-from-data.en.srt

4.8 KB

02_predicting-loan-defaults-with-decision-trees.en.txt

4.0 KB

02_predicting-loan-defaults-with-decision-trees.mp4

14.4 MB

04_task-of-learning-decision-trees-from-data.mp4

11.5 MB

03_intuition-behind-decision-trees.mp4

6.2 MB

01_slides-presented-in-this-module_decision-trees-annotated.pdf

3.0 MB

/.../04_setting-the-stage-for-solving-the-lasso/

04_coordinate-descent-for-least-squares-regression-normalized-features.en.srt

10.0 KB

02_coordinate-descent.en.srt

7.2 KB

04_coordinate-descent-for-least-squares-regression-normalized-features.en.txt

6.1 KB

03_normalizing-features.en.srt

4.5 KB

02_coordinate-descent.en.txt

4.4 KB

01_what-makes-the-lasso-objective-different.en.srt

4.1 KB

03_normalizing-features.en.txt

2.9 KB

01_what-makes-the-lasso-objective-different.en.txt

2.5 KB

04_coordinate-descent-for-least-squares-regression-normalized-features.mp4

21.8 MB

02_coordinate-descent.mp4

15.8 MB

03_normalizing-features.mp4

9.8 MB

01_what-makes-the-lasso-objective-different.mp4

8.8 MB

/.../04_practical-issues-for-classification/

02_multiclass-classification-with-1-versus-all.en.srt

10.0 KB

01_encoding-categorical-inputs.en.srt

6.2 KB

02_multiclass-classification-with-1-versus-all.en.txt

6.2 KB

01_encoding-categorical-inputs.en.txt

3.9 KB

02_multiclass-classification-with-1-versus-all.mp4

23.8 MB

01_encoding-categorical-inputs.mp4

15.3 MB

/.../01_recommender-systems/

03_where-we-see-recommender-systems-in-action.en.srt

10.0 KB

03_where-we-see-recommender-systems-in-action.en.txt

6.3 KB

04_building-a-recommender-system-via-classification.en.srt

6.2 KB

04_building-a-recommender-system-via-classification.en.txt

4.0 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_recommender-systems-overview.en.srt

2.1 KB

02_recommender-systems-overview.en.txt

1.3 KB

03_where-we-see-recommender-systems-in-action.mp4

28.0 MB

04_building-a-recommender-system-via-classification.mp4

14.1 MB

01_slides-presented-in-this-module_recommenders-intro-annotated.pdf

12.9 MB

02_recommender-systems-overview.mp4

4.8 MB

/.../02_nearest-neighbor-regression/

01_1-nearest-neighbor-regression-approach.en.srt

10.0 KB

01_1-nearest-neighbor-regression-approach.en.txt

6.2 KB

02_distance-metrics.en.srt

5.8 KB

03_1-nearest-neighbor-algorithm.en.srt

4.9 KB

02_distance-metrics.en.txt

3.8 KB

03_1-nearest-neighbor-algorithm.en.txt

3.1 KB

01_1-nearest-neighbor-regression-approach.mp4

22.5 MB

02_distance-metrics.mp4

14.9 MB

03_1-nearest-neighbor-algorithm.mp4

11.4 MB

/.../05_summarizing-clustering-with-k-means/

01_other-applications-of-clustering.en.srt

9.9 KB

01_other-applications-of-clustering.en.txt

6.3 KB

02_a-brief-recap.en.srt

1.9 KB

02_a-brief-recap.en.txt

1.2 KB

01_other-applications-of-clustering.mp4

22.6 MB

02_a-brief-recap.mp4

3.9 MB

/.../05_gradient-descent-for-logistic-regression/

01_gradient-descent-implementation.en.srt

9.8 KB

01_gradient-descent-implementation.en.txt

5.2 KB

01_gradient-descent-implementation.mp4

13.4 MB

/.../03_expectation-maximization-em-building-blocks/

04_estimating-cluster-parameters-from-soft-assignments.en.srt

9.7 KB

01_computing-soft-assignments-from-known-cluster-parameters.en.srt

9.6 KB

03_estimating-cluster-parameters-from-known-cluster-assignments.en.srt

9.4 KB

02_optional-responsibilities-as-bayes-rule.en.srt

6.3 KB

04_estimating-cluster-parameters-from-soft-assignments.en.txt

6.2 KB

01_computing-soft-assignments-from-known-cluster-parameters.en.txt

6.1 KB

03_estimating-cluster-parameters-from-known-cluster-assignments.en.txt

5.8 KB

02_optional-responsibilities-as-bayes-rule.en.txt

3.9 KB

01_computing-soft-assignments-from-known-cluster-parameters.mp4

22.8 MB

04_estimating-cluster-parameters-from-soft-assignments.mp4

22.4 MB

03_estimating-cluster-parameters-from-known-cluster-assignments.mp4

19.7 MB

02_optional-responsibilities-as-bayes-rule.mp4

15.1 MB

/.../04_learning-decision-trees-with-continuous-inputs/

02_optional-picking-the-best-threshold-to-split-on.en.txt

2.6 KB

01_threshold-splits-for-continuous-inputs.en.srt

8.1 KB

03_visualizing-decision-boundaries.en.srt

7.5 KB

01_threshold-splits-for-continuous-inputs.en.txt

5.0 KB

03_visualizing-decision-boundaries.en.txt

4.7 KB

02_optional-picking-the-best-threshold-to-split-on.en.srt

4.2 KB

01_threshold-splits-for-continuous-inputs.mp4

18.1 MB

03_visualizing-decision-boundaries.mp4

13.8 MB

02_optional-picking-the-best-threshold-to-split-on.mp4

9.9 MB

/.../05_summarizing-decision-trees/

01_recap-of-decision-trees.en.srt

1.3 KB

01_recap-of-decision-trees.en.txt

0.8 KB

01_recap-of-decision-trees.mp4

3.6 MB

/.../02_early-stopping-to-avoid-overfitting/

02_early-stopping-in-learning-decision-trees.en.srt

9.6 KB

01_principle-of-occams-razor-learning-simpler-decision-trees.en.srt

6.8 KB

02_early-stopping-in-learning-decision-trees.en.txt

6.0 KB

01_principle-of-occams-razor-learning-simpler-decision-trees.en.txt

4.3 KB

02_early-stopping-in-learning-decision-trees.mp4

23.3 MB

01_principle-of-occams-razor-learning-simpler-decision-trees.mp4

15.2 MB

/.../03_l2-regularized-logistic-regression/

04_learning-l2-regularized-logistic-regression-with-gradient-ascent.en.srt

9.5 KB

01_penalizing-large-coefficients-to-mitigate-overfitting.en.srt

7.2 KB

03_visualizing-effect-of-l2-regularization-in-logistic-regression.en.srt

7.0 KB

02_l2-regularized-logistic-regression.en.srt

6.1 KB

04_learning-l2-regularized-logistic-regression-with-gradient-ascent.en.txt

5.8 KB

01_penalizing-large-coefficients-to-mitigate-overfitting.en.txt

4.5 KB

03_visualizing-effect-of-l2-regularization-in-logistic-regression.en.txt

4.5 KB

02_l2-regularized-logistic-regression.en.txt

3.8 KB

04_learning-l2-regularized-logistic-regression-with-gradient-ascent.mp4

20.0 MB

01_penalizing-large-coefficients-to-mitigate-overfitting.mp4

15.5 MB

03_visualizing-effect-of-l2-regularization-in-logistic-regression.mp4

14.2 MB

02_l2-regularized-logistic-regression.mp4

13.1 MB

/.../04_sparse-logistic-regression/

01_sparse-logistic-regression-with-l1-regularization.en.srt

9.4 KB

01_sparse-logistic-regression-with-l1-regularization.en.txt

5.7 KB

01_sparse-logistic-regression-with-l1-regularization.mp4

21.8 MB

/.../01_overfitting-in-decision-trees/

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_a-review-of-overfitting.en.txt

2.3 KB

03_overfitting-in-decision-trees.en.srt

7.5 KB

03_overfitting-in-decision-trees.en.txt

4.6 KB

02_a-review-of-overfitting.en.srt

3.5 KB

03_overfitting-in-decision-trees.mp4

17.6 MB

02_a-review-of-overfitting.mp4

7.5 MB

01_slides-presented-in-this-module_decision-trees-overfitting-annotated.pdf

3.3 MB

/.../02_deep-learning-deep-features/

06_deep-features.en.srt

9.1 KB

01_application-of-deep-learning-to-computer-vision.en.srt

8.0 KB

06_deep-features.en.txt

5.8 KB

01_application-of-deep-learning-to-computer-vision.en.txt

5.0 KB

03_demo-of-deep-learning-model-on-imagenet-data.en.srt

4.4 KB

02_deep-learning-performance.en.srt

4.1 KB

05_challenges-of-deep-learning.en.srt

3.4 KB

02_deep-learning-performance.en.txt

2.5 KB

03_demo-of-deep-learning-model-on-imagenet-data.en.txt

2.7 KB

04_other-examples-of-deep-learning-in-computer-vision.en.srt

2.1 KB

04_other-examples-of-deep-learning-in-computer-vision.en.txt

1.2 KB

05_challenges-of-deep-learning.en.txt

2.2 KB

06_deep-features.mp4

25.6 MB

01_application-of-deep-learning-to-computer-vision.mp4

20.2 MB

02_deep-learning-performance.mp4

13.9 MB

05_challenges-of-deep-learning.mp4

11.6 MB

03_demo-of-deep-learning-model-on-imagenet-data.mp4

8.3 MB

04_other-examples-of-deep-learning-in-computer-vision.mp4

7.0 MB

/.../04_kernel-regression/

01_from-weighted-k-nn-to-kernel-regression.en.srt

9.1 KB

02_global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.en.srt

8.7 KB

01_from-weighted-k-nn-to-kernel-regression.en.txt

5.8 KB

02_global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.en.txt

5.5 KB

02_global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.mp4

21.1 MB

01_from-weighted-k-nn-to-kernel-regression.mp4

19.5 MB

/.../03_3-sources-of-error-and-the-bias-variance-tradeoff/

01_irreducible-error-and-bias.en.srt

9.0 KB

02_variance-and-the-bias-variance-tradeoff.en.srt

8.5 KB

03_error-vs-amount-of-data.en.srt

6.9 KB

01_irreducible-error-and-bias.en.txt

5.6 KB

02_variance-and-the-bias-variance-tradeoff.en.txt

5.1 KB

03_error-vs-amount-of-data.en.txt

4.1 KB

01_irreducible-error-and-bias.mp4

21.9 MB

02_variance-and-the-bias-variance-tradeoff.mp4

19.9 MB

03_error-vs-amount-of-data.mp4

15.4 MB

/.../04_summarizing-preventing-overfitting-in-decision-trees/

01_recap-of-overfitting-and-regularization-in-decision-trees.en.srt

1.8 KB

01_recap-of-overfitting-and-regularization-in-decision-trees.en.txt

1.0 KB

01_recap-of-overfitting-and-regularization-in-decision-trees.mp4

5.1 MB

/.../02_evaluating-regression-models/

01_evaluating-overfitting-via-training-test-split.en.srt

8.9 KB

02_training-test-curves.en.srt

5.6 KB

01_evaluating-overfitting-via-training-test-split.en.txt

5.6 KB

04_other-regression-examples.en.srt

5.1 KB

03_adding-other-features.en.txt

2.4 KB

03_adding-other-features.en.srt

3.9 KB

02_training-test-curves.en.txt

3.4 KB

04_other-regression-examples.en.txt

3.3 KB

01_evaluating-overfitting-via-training-test-split.mp4

16.2 MB

04_other-regression-examples.mp4

14.1 MB

02_training-test-curves.mp4

9.9 MB

03_adding-other-features.mp4

7.0 MB

/.../03_matrix-factorization/

04_discovering-hidden-structure-by-matrix-factorization.en.srt

8.7 KB

02_recommendations-from-known-user-item-features.en.srt

6.7 KB

01_the-matrix-completion-task.en.srt

6.6 KB

04_discovering-hidden-structure-by-matrix-factorization.en.txt

5.3 KB

05_bringing-it-all-together-featurized-matrix-factorization.en.srt

4.3 KB

01_the-matrix-completion-task.en.txt

4.1 KB

02_recommendations-from-known-user-item-features.en.txt

3.9 KB

03_predictions-in-matrix-form.en.srt

3.9 KB

03_predictions-in-matrix-form.en.txt

2.4 KB

05_bringing-it-all-together-featurized-matrix-factorization.en.txt

2.7 KB

04_discovering-hidden-structure-by-matrix-factorization.mp4

18.1 MB

01_the-matrix-completion-task.mp4

15.5 MB

02_recommendations-from-known-user-item-features.mp4

14.9 MB

05_bringing-it-all-together-featurized-matrix-factorization.mp4

11.8 MB

03_predictions-in-matrix-form.mp4

7.8 MB

/.../02_evaluating-classification-models/

03_false-positives-false-negatives-and-confusion-matrices.en.srt

8.7 KB

04_learning-curves.en.srt

7.3 KB

01_training-and-evaluating-a-classifier.en.srt

6.1 KB

03_false-positives-false-negatives-and-confusion-matrices.en.txt

5.3 KB

04_learning-curves.en.txt

4.5 KB

02_whats-a-good-accuracy.en.srt

4.4 KB

02_whats-a-good-accuracy.en.txt

2.8 KB

05_class-probabilities.en.srt

2.6 KB

05_class-probabilities.en.txt

1.6 KB

01_training-and-evaluating-a-classifier.en.txt

3.7 KB

04_learning-curves.mp4

23.3 MB

03_false-positives-false-negatives-and-confusion-matrices.mp4

17.7 MB

02_whats-a-good-accuracy.mp4

16.6 MB

01_training-and-evaluating-a-classifier.mp4

13.6 MB

05_class-probabilities.mp4

9.1 MB

/.../02_learning-decision-trees/

03_selecting-best-feature-to-split-on.en.srt

8.7 KB

04_when-to-stop-recursing.en.srt

6.3 KB

01_recursive-greedy-algorithm.en.srt

5.9 KB

03_selecting-best-feature-to-split-on.en.txt

5.2 KB

02_learning-a-decision-stump.en.srt

5.2 KB

04_when-to-stop-recursing.en.txt

4.0 KB

01_recursive-greedy-algorithm.en.txt

3.8 KB

02_learning-a-decision-stump.en.txt

3.3 KB

04_when-to-stop-recursing.mp4

16.3 MB

03_selecting-best-feature-to-split-on.mp4

15.9 MB

02_learning-a-decision-stump.mp4

13.5 MB

01_recursive-greedy-algorithm.mp4

11.7 MB

/.../03_summarizing-handling-missing-data/

01_recap-of-handling-missing-data.en.srt

2.3 KB

01_recap-of-handling-missing-data.en.txt

1.4 KB

01_recap-of-handling-missing-data.mp4

6.5 MB

/.../04_performance-metrics-for-recommender-systems/

03_precision-recall-curves.en.srt

8.4 KB

01_a-performance-metric-for-recommender-systems.en.srt

7.2 KB

03_precision-recall-curves.en.txt

5.3 KB

01_a-performance-metric-for-recommender-systems.en.txt

4.5 KB

02_optimal-recommenders.en.srt

2.7 KB

02_optimal-recommenders.en.txt

1.6 KB

01_a-performance-metric-for-recommender-systems.mp4

17.3 MB

03_precision-recall-curves.mp4

17.1 MB

02_optimal-recommenders.mp4

6.2 MB

/.../02_co-occurrence-matrices-for-collaborative-filtering/

03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories.en.srt

8.3 KB

01_collaborative-filtering-people-who-bought-this-also-bought.en.srt

7.0 KB

03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories.en.txt

5.2 KB

01_collaborative-filtering-people-who-bought-this-also-bought.en.txt

4.3 KB

02_effect-of-popular-items.en.srt

3.9 KB

02_effect-of-popular-items.en.txt

2.3 KB

03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories.mp4

20.4 MB

01_collaborative-filtering-people-who-bought-this-also-bought.mp4

16.0 MB

02_effect-of-popular-items.mp4

8.4 MB

/.../03_an-aside-on-optimization-one-dimensional-objectives/

04_finding-the-max-via-hill-climbing.en.srt

8.1 KB

06_choosing-stepsize-and-convergence-criteria.en.srt

7.9 KB

02_finding-maxima-or-minima-analytically.en.srt

7.4 KB

06_choosing-stepsize-and-convergence-criteria.en.txt

4.8 KB

04_finding-the-max-via-hill-climbing.en.txt

4.7 KB

02_finding-maxima-or-minima-analytically.en.txt

4.4 KB

01_defining-our-least-squares-optimization-objective.en.srt

3.9 KB

05_finding-the-min-via-hill-descent.en.srt

3.8 KB

03_maximizing-a-1d-function-a-worked-example.en.srt

3.6 KB

01_defining-our-least-squares-optimization-objective.en.txt

2.5 KB

05_finding-the-min-via-hill-descent.en.txt

2.3 KB

03_maximizing-a-1d-function-a-worked-example.en.txt

2.0 KB

02_finding-maxima-or-minima-analytically.mp4

17.7 MB

04_finding-the-max-via-hill-climbing.mp4

16.5 MB

06_choosing-stepsize-and-convergence-criteria.mp4

15.4 MB

01_defining-our-least-squares-optimization-objective.mp4

10.7 MB

05_finding-the-min-via-hill-descent.mp4

9.0 MB

03_maximizing-a-1d-function-a-worked-example.mp4

7.7 MB

/.../03_the-precision-recall-tradeoff/

03_precision-recall-curve.en.srt

8.0 KB

01_precision-recall-extremes.en.txt

2.3 KB

02_trading-off-precision-and-recall.en.srt

6.1 KB

03_precision-recall-curve.en.txt

5.0 KB

02_trading-off-precision-and-recall.en.txt

3.8 KB

01_precision-recall-extremes.en.srt

3.7 KB

03_precision-recall-curve.mp4

16.4 MB

02_trading-off-precision-and-recall.mp4

13.8 MB

01_precision-recall-extremes.mp4

9.1 MB

/.../02_feature-selection-implicitly-via-regularized-regression/

03_the-lasso-objective-and-its-coefficient-path.en.srt

7.9 KB

02_thresholding-ridge-coefficients.en.srt

7.0 KB

01_can-we-use-regularization-for-feature-selection.en.srt

5.3 KB

03_the-lasso-objective-and-its-coefficient-path.en.txt

4.8 KB

02_thresholding-ridge-coefficients.en.txt

4.4 KB

01_can-we-use-regularization-for-feature-selection.en.txt

3.4 KB

03_the-lasso-objective-and-its-coefficient-path.mp4

16.3 MB

02_thresholding-ridge-coefficients.mp4

14.5 MB

01_can-we-use-regularization-for-feature-selection.mp4

12.1 MB

/.../01_introduction-to-clustering/

03_an-unsupervised-task.en.srt

7.8 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

04_hope-for-unsupervised-learning-and-some-challenge-cases.en.srt

5.5 KB

02_the-goal-of-clustering.en.srt

5.3 KB

03_an-unsupervised-task.en.txt

4.9 KB

04_hope-for-unsupervised-learning-and-some-challenge-cases.en.txt

3.5 KB

02_the-goal-of-clustering.en.txt

3.4 KB

03_an-unsupervised-task.mp4

17.3 MB

04_hope-for-unsupervised-learning-and-some-challenge-cases.mp4

12.1 MB

01_slides-presented-in-this-module_kmeans-annotated.pdf

11.4 MB

02_the-goal-of-clustering.mp4

11.2 MB

/.../02_precision-recall-explained/

02_recall-fraction-of-positive-data-predicted-to-be-positive.en.txt

2.7 KB

01_precision-fraction-of-positive-predictions-that-are-actually-positive.en.srt

7.7 KB

01_precision-fraction-of-positive-predictions-that-are-actually-positive.en.txt

4.7 KB

02_recall-fraction-of-positive-data-predicted-to-be-positive.en.srt

4.4 KB

01_precision-fraction-of-positive-predictions-that-are-actually-positive.mp4

16.3 MB

02_recall-fraction-of-positive-data-predicted-to-be-positive.mp4

11.1 MB

/.../02_clustering-models-and-algorithms/

04_other-examples-of-clustering.en.srt

7.7 KB

02_clustering-documents-an-unsupervised-learning-task.en.srt

5.7 KB

03_k-means-a-clustering-algorithm.en.srt

5.0 KB

04_other-examples-of-clustering.en.txt

4.9 KB

01_clustering-documents-task-overview.en.txt

2.2 KB

02_clustering-documents-an-unsupervised-learning-task.en.txt

3.5 KB

01_clustering-documents-task-overview.en.srt

3.5 KB

03_k-means-a-clustering-algorithm.en.txt

3.1 KB

04_other-examples-of-clustering.mp4

20.7 MB

02_clustering-documents-an-unsupervised-learning-task.mp4

11.8 MB

01_clustering-documents-task-overview.mp4

10.3 MB

03_k-means-a-clustering-algorithm.mp4

9.9 MB

/.../04_summarizing-precision-recall/

01_recap-of-precision-recall.en.srt

2.1 KB

01_recap-of-precision-recall.en.txt

1.3 KB

01_recap-of-precision-recall.mp4

5.4 MB

/.../01_scaling-ml-to-huge-datasets/

01_slides-presented-in-this-module_instructions.html

1.2 KB

03_timeline-of-scalable-machine-learning-stochastic-gradient.en.srt

6.4 KB

02_gradient-ascent-won-t-scale-to-todays-huge-datasets.en.srt

5.2 KB

03_timeline-of-scalable-machine-learning-stochastic-gradient.en.txt

4.1 KB

02_gradient-ascent-won-t-scale-to-todays-huge-datasets.en.txt

3.3 KB

03_timeline-of-scalable-machine-learning-stochastic-gradient.mp4

12.7 MB

02_gradient-ascent-won-t-scale-to-todays-huge-datasets.mp4

12.6 MB

01_slides-presented-in-this-module_online-learning-annotated.pdf

3.8 MB

/.../02_scaling-ml-with-stochastic-gradient/

01_why-gradient-ascent-won-t-scale.en.txt

2.9 KB

02_stochastic-gradient-learning-one-data-point-at-a-time.en.txt

2.8 KB

03_comparing-gradient-to-stochastic-gradient.en.srt

5.8 KB

01_why-gradient-ascent-won-t-scale.en.srt

4.9 KB

02_stochastic-gradient-learning-one-data-point-at-a-time.en.srt

4.5 KB

03_comparing-gradient-to-stochastic-gradient.en.txt

3.6 KB

03_comparing-gradient-to-stochastic-gradient.mp4

11.6 MB

01_why-gradient-ascent-won-t-scale.mp4

9.3 MB

02_stochastic-gradient-learning-one-data-point-at-a-time.mp4

9.2 MB

/.../07_tying-up-loose-ends/

01_choosing-the-penalty-strength-and-other-practical-issues-with-lasso.en.srt

7.4 KB

02_a-brief-recap.en.srt

4.9 KB

01_choosing-the-penalty-strength-and-other-practical-issues-with-lasso.en.txt

4.8 KB

02_a-brief-recap.en.txt

3.1 KB

01_choosing-the-penalty-strength-and-other-practical-issues-with-lasso.mp4

20.3 MB

02_a-brief-recap.mp4

11.7 MB

/.../03_understanding-why-stochastic-gradient-works/

02_convergence-paths.en.srt

2.8 KB

02_convergence-paths.en.txt

1.8 KB

01_why-would-stochastic-gradient-ever-work.en.srt

5.2 KB

01_why-would-stochastic-gradient-ever-work.en.txt

3.1 KB

01_why-would-stochastic-gradient-ever-work.mp4

10.9 MB

02_convergence-paths.mp4

7.4 MB

/.../04_stochastic-gradient-practical-tricks/

01_shuffle-data-before-running-stochastic-gradient.en.srt

3.1 KB

01_shuffle-data-before-running-stochastic-gradient.en.txt

1.9 KB

02_choosing-step-size.en.txt

2.8 KB

03_don-t-trust-last-coefficients.en.srt

2.2 KB

03_don-t-trust-last-coefficients.en.txt

1.4 KB

06_optional-adding-regularization.en.txt

2.6 KB

05_optional-measuring-convergence.en.srt

5.5 KB

04_optional-learning-from-batches-of-data.en.srt

5.1 KB

02_choosing-step-size.en.srt

4.5 KB

06_optional-adding-regularization.en.srt

4.1 KB

05_optional-measuring-convergence.en.txt

3.5 KB

04_optional-learning-from-batches-of-data.en.txt

3.2 KB

05_optional-measuring-convergence.mp4

13.1 MB

04_optional-learning-from-batches-of-data.mp4

12.6 MB

06_optional-adding-regularization.mp4

11.4 MB

02_choosing-step-size.mp4

10.1 MB

03_don-t-trust-last-coefficients.mp4

6.3 MB

01_shuffle-data-before-running-stochastic-gradient.mp4

6.2 MB

/.../02_strategy-3-modify-learning-algorithm-to-explicitly-handle-missing-data/

02_feature-split-selection-with-missing-data.en.srt

7.3 KB

01_modifying-decision-trees-to-handle-missing-data.en.srt

6.6 KB

02_feature-split-selection-with-missing-data.en.txt

4.6 KB

01_modifying-decision-trees-to-handle-missing-data.en.txt

4.1 KB

02_feature-split-selection-with-missing-data.mp4

16.8 MB

01_modifying-decision-trees-to-handle-missing-data.mp4

15.1 MB

/.../06_summarizing-scaling-to-huge-datasets-online-learning/

01_scaling-to-huge-datasets-through-parallelization-module-recap.en.srt

2.1 KB

01_scaling-to-huge-datasets-through-parallelization-module-recap.en.txt

1.4 KB

01_scaling-to-huge-datasets-through-parallelization-module-recap.mp4

6.1 MB

/.../05_convergence-and-overfitting-in-boosting/

02_overfitting-in-boosting.en.srt

7.1 KB

01_the-boosting-theorem.en.srt

5.6 KB

02_overfitting-in-boosting.en.txt

4.4 KB

01_the-boosting-theorem.en.txt

3.5 KB

02_overfitting-in-boosting.mp4

15.3 MB

01_the-boosting-theorem.mp4

11.4 MB

/.../03_applying-adaboost/

01_example-of-adaboost-in-action.en.srt

7.0 KB

02_learning-boosted-decision-stumps-with-adaboost.en.srt

6.4 KB

01_example-of-adaboost-in-action.en.txt

4.4 KB

02_learning-boosted-decision-stumps-with-adaboost.en.txt

4.0 KB

02_learning-boosted-decision-stumps-with-adaboost.mp4

14.7 MB

01_example-of-adaboost-in-action.mp4

12.4 MB

/.../01_introduction-to-latent-dirichlet-allocation/

05_goal-of-lda-inference.en.srt

6.9 KB

03_an-alternative-document-clustering-model.en.srt

6.2 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

04_components-of-latent-dirichlet-allocation-model.en.txt

2.0 KB

02_mixed-membership-models-for-documents.en.srt

4.9 KB

05_goal-of-lda-inference.en.txt

4.5 KB

03_an-alternative-document-clustering-model.en.txt

4.0 KB

04_components-of-latent-dirichlet-allocation-model.en.srt

3.4 KB

02_mixed-membership-models-for-documents.en.txt

3.1 KB

05_goal-of-lda-inference.mp4

19.1 MB

03_an-alternative-document-clustering-model.mp4

17.4 MB

02_mixed-membership-models-for-documents.mp4

13.9 MB

04_components-of-latent-dirichlet-allocation-model.mp4

11.1 MB

01_slides-presented-in-this-module_LDA-annotated.pdf

7.3 MB

/.../06_summarizing-boosting/

01_ensemble-methods-impact-of-boosting-quick-recap.en.srt

6.9 KB

01_ensemble-methods-impact-of-boosting-quick-recap.en.txt

4.3 KB

01_ensemble-methods-impact-of-boosting-quick-recap.mp4

17.0 MB

/.../04_an-aside-on-optimization-multidimensional-objectives/

01_gradients-derivatives-in-multiple-dimensions.en.srt

6.5 KB

02_gradient-descent-multidimensional-hill-descent.en.srt

6.4 KB

01_gradients-derivatives-in-multiple-dimensions.en.txt

3.8 KB

02_gradient-descent-multidimensional-hill-descent.en.txt

3.8 KB

02_gradient-descent-multidimensional-hill-descent.mp4

17.7 MB

01_gradients-derivatives-in-multiple-dimensions.mp4

14.7 MB

/.../07_summarizing-nearest-neighbor-search/

01_a-brief-recap.en.txt

2.5 KB

01_a-brief-recap.en.srt

4.0 KB

01_a-brief-recap.mp4

6.7 MB

/.../05_online-learning-fitting-models-from-streaming-data/

02_using-stochastic-gradient-for-online-learning.en.srt

5.9 KB

01_the-online-learning-task.en.srt

5.3 KB

02_using-stochastic-gradient-for-online-learning.en.txt

3.7 KB

01_the-online-learning-task.en.txt

3.2 KB

02_using-stochastic-gradient-for-online-learning.mp4

15.1 MB

01_the-online-learning-task.mp4

10.7 MB

/.../05_optimizing-the-lasso-objective/

01_coordinate-descent-for-lasso-normalized-features.en.srt

5.8 KB

02_assessing-convergence-and-other-lasso-solvers.en.srt

3.9 KB

01_coordinate-descent-for-lasso-normalized-features.en.txt

3.6 KB

02_assessing-convergence-and-other-lasso-solvers.en.txt

2.5 KB

03_coordinate-descent-for-lasso-unnormalized-features.en.srt

2.5 KB

03_coordinate-descent-for-lasso-unnormalized-features.en.txt

1.5 KB

01_coordinate-descent-for-lasso-normalized-features.mp4

13.2 MB

02_assessing-convergence-and-other-lasso-solvers.mp4

9.8 MB

03_coordinate-descent-for-lasso-unnormalized-features.mp4

6.2 MB

/.../05_summarizing-mixture-models/

01_a-brief-recap.en.srt

2.4 KB

01_a-brief-recap.en.txt

1.5 KB

01_a-brief-recap.mp4

4.6 MB

/.../01_welcome-to-the-course/

01_welcome.en.srt

5.6 KB

01_welcome.en.txt

3.0 KB

01_welcome.mp4

8.7 MB

/.../03_reinforcement-learning/06_acknowledgments/

01_acknowledgments_instructions.html

5.4 KB

02_optional-opportunity-to-mentor-other-learners_instructions.html

1.7 KB

/.../04_decision-trees/05_acknowledgments/

01_acknowledgements_instructions.html

5.4 KB

/.../03_week-3-classification/11_acknowledgments/

01_acknowledgments_instructions.html

5.4 KB

/.../04_summary-and-thank-you/

01_summary-and-thank-you.en.srt

5.4 KB

01_summary-and-thank-you.en.txt

2.9 KB

01_summary-and-thank-you.mp4

14.6 MB

/.../01_motivating-local-fits/

02_limitations-of-parametric-regression.en.srt

5.4 KB

02_limitations-of-parametric-regression.en.txt

3.4 KB

01_slides-presented-in-this-module_instructions.html

1.2 KB

02_limitations-of-parametric-regression.mp4

11.8 MB

01_slides-presented-in-this-module_week6_NNkernelregression-annotated.pdf

4.8 MB

/.../04_summarizing-latent-dirichlet-allocation/

01_a-brief-recap.en.srt

2.4 KB

01_a-brief-recap.en.txt

1.5 KB

01_a-brief-recap.mp4

5.3 MB

/.../04_summary-and-whats-ahead-in-the-specialization/

01_what-we-didn-t-cover.en.txt

2.6 KB

02_thank-you.en.srt

2.6 KB

02_thank-you.en.txt

1.4 KB

01_what-we-didn-t-cover.en.srt

4.1 KB

01_what-we-didn-t-cover.mp4

9.0 MB

02_thank-you.mp4

7.5 MB

/.../05_summarizing-multiple-regression/

01_a-brief-recap.en.srt

1.6 KB

01_a-brief-recap.en.txt

1.0 KB

01_a-brief-recap.mp4

4.1 MB

 

Total files 2880


Copyright © 2024 FileMood.com