Complete Machine Learning Data Science Bootcamp 2021 | 8415BF74D3A3147854E92873333B3D986FC0CD8D

Complete Machine Learning Data Science Bootcamp 2021

Name	Complete Machine Learning & Data Science Bootcamp 2021	DOWNLOAD Copy Link
Total Size	21.0 GB
Total Files	1163
Last Seen	2024-12-20 23:26
Hash	8415BF74D3A3147854E92873333B3D986FC0CD8D

/.../5. Data Science Environment Setup/
8. Windows Environment Setup 2.mp4	238.7 MB
10. Sharing your Conda Environment.html	2.5 KB
11.1 6-step-ml-framework.png	332.0 KB
3.2 conda-cheatsheet.pdf	216.4 KB
8. Windows Environment Setup 2.srt	32.4 KB
5. Mac Environment Setup.srt	24.5 KB
12. Jupyter Notebook Walkthrough 2.srt	23.0 KB
6. Mac Environment Setup 2.srt	21.2 KB
1. Section Overview.srt	2.2 KB
3.1 Getting started with Conda (documentation).html	0.1 KB
3.3 Getting your computer ready for machine learning How, what and why you should use Anaconda, Miniconda and Conda (blog post).html	0.2 KB
3.4 Conda documentation.html	0.1 KB
5.1 Miniconda download documentation.html	0.1 KB
7.1 Miniconda download documentation.html	0.1 KB
9. Linux Environment Setup.html	1.1 KB
10.1 Conda documentation on sharing an environment.html	0.2 KB
11.3 Dataquest Jupyter Notebook for Beginners Tutorial.html	0.1 KB
11.4 Jupyter Notebook documentation.html	0.1 KB
11. Jupyter Notebook Walkthrough.srt	15.5 KB
13. Jupyter Notebook Walkthrough 3.srt	11.8 KB
11.2 heart-disease.csv	11.3 KB
7. Windows Environment Setup.srt	7.8 KB
4. Conda Environments.srt	6.3 KB
2. Introducing Our Tools.srt	4.4 KB
3. What is Conda.srt	3.5 KB
5. Mac Environment Setup.mp4	151.4 MB
6. Mac Environment Setup 2.mp4	131.6 MB
12. Jupyter Notebook Walkthrough 2.mp4	108.9 MB
13. Jupyter Notebook Walkthrough 3.mp4	74.9 MB
11. Jupyter Notebook Walkthrough.mp4	70.6 MB
7. Windows Environment Setup.mp4	50.2 MB
4. Conda Environments.mp4	32.0 MB
2. Introducing Our Tools.mp4	20.2 MB
3. What is Conda.mp4	13.1 MB
1. Section Overview.mp4	6.3 MB
/
TutsNode.com.txt	0.1 KB
[TGx]Downloaded from torrentgalaxy.to .txt	0.6 KB
/.../8. Matplotlib Plotting and Data Visualization/
4.2 matplotlib-anatomy-of-a-plot.png	378.3 KB
4.1 matplotlib-anatomy-of-a-plot-with-code.png	670.5 KB
13. Plotting from Pandas DataFrames 4.srt	9.6 KB
3. Importing And Using Matplotlib.srt	16.4 KB
16. Plotting from Pandas DataFrames 7.srt	15.3 KB
5. Scatter Plot And Bar Plot.srt	15.0 KB
4. Anatomy Of A Matplotlib Figure.srt	14.5 KB
17. Customizing Your Plots.srt	14.3 KB
11. Plotting From Pandas DataFrames 2.srt	14.0 KB
18. Customizing Your Plots 2.srt	13.6 KB
6. Histograms And Subplots.srt	12.7 KB
14. Plotting from Pandas DataFrames 5.srt	11.9 KB
1. Section Overview.srt	2.8 KB
15. Plotting from Pandas DataFrames 6.srt	11.3 KB
2.2 Matplotlib Documentation.html	0.1 KB
12. Plotting from Pandas DataFrames 3.srt	11.7 KB
8. Quick Tip Data Visualizations.srt	2.4 KB
13.1 heart-disease.csv	11.3 KB
10. Quick Note Regular Expressions.html	0.6 KB
19.1 Introduction to Matplotlib Notebook (from the videos).html	0.2 KB
20. Assignment Matplotlib Practice.html	2.1 KB
9. Plotting From Pandas DataFrames.srt	9.2 KB
2. Matplotlib Introduction.srt	8.2 KB
7. Subplots Option 2.srt	6.6 KB
19. Saving And Sharing Your Plots.srt	6.0 KB
2.1 Introduction to Matplotlib Jupyter Notebook (from the upcoming videos).html	0.2 KB
18. Customizing Your Plots 2.mp4	129.6 MB
16. Plotting from Pandas DataFrames 7.mp4	125.6 MB
11. Plotting From Pandas DataFrames 2.mp4	103.6 MB
17. Customizing Your Plots.mp4	96.7 MB
3. Importing And Using Matplotlib.mp4	90.6 MB
4. Anatomy Of A Matplotlib Figure.mp4	86.1 MB
15. Plotting from Pandas DataFrames 6.mp4	86.0 MB
12. Plotting from Pandas DataFrames 3.mp4	78.3 MB
6. Histograms And Subplots.mp4	73.1 MB
5. Scatter Plot And Bar Plot.mp4	70.3 MB
9. Plotting From Pandas DataFrames.mp4	63.3 MB
14. Plotting from Pandas DataFrames 5.mp4	59.7 MB
19. Saving And Sharing Your Plots.mp4	51.9 MB
13. Plotting from Pandas DataFrames 4.mp4	51.4 MB
7. Subplots Option 2.mp4	39.9 MB
2. Matplotlib Introduction.mp4	33.0 MB
8. Quick Tip Data Visualizations.mp4	12.8 MB
1. Section Overview.mp4	9.0 MB
/.../9. Scikit-learn Creating Machine Learning Models/
7. Typical scikit-learn Workflow.srt	32.5 KB
7. Typical scikit-learn Workflow.mp4	199.4 MB
8. Optional Debugging Warnings In Jupyter.mp4	184.7 MB
41. Tuning Hyperparameters.srt	31.3 KB
48. Putting It All Together.srt	30.3 KB
8. Optional Debugging Warnings In Jupyter.srt	26.1 KB
15. Getting Your Data Ready Handling Missing Values With Scikit-learn.srt	23.7 KB
11. Getting Your Data Ready Convert Data To Numbers.srt	23.3 KB
16. Choosing The Right Model For Your Data.srt	21.9 KB
9.1 scikit-learn-data.zip	21.3 KB
43. Tuning Hyperparameters 3.srt	19.3 KB
38. Evaluating A Model With Cross Validation and Scoring Parameter.srt	18.4 KB
26. Evaluating A Machine Learning Model 2 (Cross Validation).srt	17.7 KB
20. Choosing The Right Model For Your Data 3 (Classification).srt	17.5 KB
42. Tuning Hyperparameters 2.srt	17.4 KB
12. Getting Your Data Ready Handling Missing Values With Pandas.srt	17.4 KB
39. Evaluating A Model With Scikit-learn Functions.srt	16.7 KB
49. Putting It All Together 2.srt	16.5 KB
31. Evaluating A Classification Model 4 (Confusion Matrix).srt	15.5 KB
40. Improving A Machine Learning Model.srt	15.2 KB
33. Evaluating A Classification Model 6 (Classification Report).srt	14.9 KB
25. Evaluating A Machine Learning Model (Score).srt	13.2 KB
28. Evaluating A Classification Model 2 (ROC Curve).srt	12.6 KB
22. Making Predictions With Our Model.srt	12.4 KB
9. Getting Your Data Ready Splitting Your Data.srt	12.4 KB
34. Evaluating A Regression Model 1 (R2 Score).srt	12.3 KB
17. Choosing The Right Model For Your Data 2 (Regression).srt	12.3 KB
23. predict() vs predict_proba().srt	11.8 KB
32. Evaluating A Classification Model 5 (Confusion Matrix).srt	11.4 KB
2. Scikit-learn Introduction.srt	10.9 KB
6. Scikit-learn Cheatsheet.srt	10.3 KB
29. Evaluating A Classification Model 3 (ROC Curve).srt	10.3 KB
46. Saving And Loading A Model.srt	10.1 KB
2.1 Introduction to Scikit-Learn Jupyter Notebook (from the upcoming videos).html	0.2 KB
2.2 Scikit-Learn Documentation.html	0.1 KB
2.3 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html	0.2 KB
3. Quick Note Upcoming Video.html	0.4 KB
5. Quick Note Upcoming Videos.html	1.0 KB
21. Fitting A Model To The Data.srt	9.6 KB
6.1 Scikit-Learn Reference Notebook.html	0.2 KB
36. Evaluating A Regression Model 3 (MSE).srt	9.5 KB
7.1 Example Scikit-Learn Workflow Notebook.html	0.2 KB
24. Making Predictions With Our Model (Regression).srt	9.3 KB
47. Saving And Loading A Model 2.srt	9.2 KB
13. Extension Feature Scaling.html	3.0 KB
14. Note Correction in the upcoming video (splitting data).html	2.2 KB
16.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html	0.1 KB
18. Quick Note Decision Trees.html	0.2 KB
19. Quick Tip How ML Algorithms Work.srt	2.0 KB
30. Reading Extension ROC Curve + AUC.html	1.5 KB
37. Machine Learning Model Evaluation.html	7.3 KB
31.1 Notebook from video with updated confusion matrix labels.html	0.2 KB
10. Quick Tip Clean, Transform, Reduce.srt	6.6 KB
4. Refresher What Is Machine Learning.srt	6.5 KB
44. Note Metric Comparison Improvement.html	2.2 KB
27. Evaluating A Classification Model 1 (Accuracy).srt	6.0 KB
48.1 Reading extension Scikit-Learn's Pipeline class explained.html	0.1 KB
49.1 Introduction to Scikit-Learn Jupyter Notebook (from the videos).html	0.2 KB
49.2 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html	0.2 KB
50. Scikit-Learn Practice.html	2.1 KB
35. Evaluating A Regression Model 2 (MAE).srt	5.8 KB
1. Section Overview.srt	4.2 KB
45. Quick Tip Correlation Analysis.srt	3.2 KB
41. Tuning Hyperparameters.mp4	184.3 MB
48. Putting It All Together.mp4	157.9 MB
16. Choosing The Right Model For Your Data.mp4	150.2 MB
15. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4	143.5 MB
11. Getting Your Data Ready Convert Data To Numbers.mp4	141.6 MB
43. Tuning Hyperparameters 3.mp4	127.7 MB
20. Choosing The Right Model For Your Data 3 (Classification).mp4	124.6 MB
49. Putting It All Together 2.mp4	122.5 MB
42. Tuning Hyperparameters 2.mp4	122.4 MB
12. Getting Your Data Ready Handling Missing Values With Pandas.mp4	109.9 MB
26. Evaluating A Machine Learning Model 2 (Cross Validation).mp4	100.6 MB
39. Evaluating A Model With Scikit-learn Functions.mp4	99.4 MB
38. Evaluating A Model With Cross Validation and Scoring Parameter.mp4	95.9 MB
40. Improving A Machine Learning Model.mp4	95.4 MB
4. Refresher What Is Machine Learning.mp4	92.6 MB
33. Evaluating A Classification Model 6 (Classification Report).mp4	91.5 MB
25. Evaluating A Machine Learning Model (Score).mp4	91.4 MB
17. Choosing The Right Model For Your Data 2 (Regression).mp4	91.1 MB
31. Evaluating A Classification Model 4 (Confusion Matrix).mp4	81.5 MB
6. Scikit-learn Cheatsheet.mp4	78.8 MB
34. Evaluating A Regression Model 1 (R2 Score).mp4	73.8 MB
22. Making Predictions With Our Model.mp4	69.7 MB
28. Evaluating A Classification Model 2 (ROC Curve).mp4	69.2 MB
32. Evaluating A Classification Model 5 (Confusion Matrix).mp4	66.9 MB
9. Getting Your Data Ready Splitting Your Data.mp4	66.8 MB
47. Saving And Loading A Model 2.mp4	59.5 MB
21. Fitting A Model To The Data.mp4	59.3 MB
36. Evaluating A Regression Model 3 (MSE).mp4	57.6 MB
23. predict() vs predict_proba().mp4	57.0 MB
46. Saving And Loading A Model.mp4	55.2 MB
29. Evaluating A Classification Model 3 (ROC Curve).mp4	53.1 MB
24. Making Predictions With Our Model (Regression).mp4	47.1 MB
2. Scikit-learn Introduction.mp4	42.6 MB
27. Evaluating A Classification Model 1 (Accuracy).mp4	32.9 MB
35. Evaluating A Regression Model 2 (MAE).mp4	29.9 MB
45. Quick Tip Correlation Analysis.mp4	17.7 MB
10. Quick Tip Clean, Transform, Reduce.mp4	17.3 MB
1. Section Overview.mp4	13.1 MB
19. Quick Tip How ML Algorithms Work.mp4	11.6 MB
/1. Introduction/
2. Join Our Online Classroom!.html	2.6 KB
3. Exercise Meet The Community.html	2.6 KB
1. Course Outline.srt	9.4 KB
4. Your First Day.srt	5.4 KB
1. Course Outline.mp4	81.0 MB
4. Your First Day.mp4	29.3 MB
/7. NumPy/
2.2 NumPy Documentation.html	0.1 KB
4. NumPy DataTypes and Attributes.srt	19.7 KB
13. Exercise Nut Butter Store Sales.srt	17.4 KB
8. Manipulating Arrays.srt	16.6 KB
12. Dot Product vs Element Wise.srt	15.7 KB
2.1 Introduction to NumPy Jupyter Notebook (with annotations).html	0.2 KB
2.3 Introduction to NumPy Jupyter Notebook (from the upcoming videos).html	0.2 KB
3. Quick Note Correction In Next Video.html	1.3 KB
7. Viewing Arrays and Matrices.srt	13.2 KB
8.1 Standard deviation and variance explained.html	0.1 KB
9.1 Standard deviation and variance explained.html	0.1 KB
10.1 Standard deviation and variance explained.html	0.1 KB
5. Creating NumPy Arrays.srt	12.7 KB
12.1 Matrix Multiplication Explained.html	0.1 KB
16.1 Introduction to NumPy Jupyter Notebook (from the videos).html	0.2 KB
16.2 Introduction to NumPy Jupyter Notebook (with annotations).html	0.2 KB
17. Assignment NumPy Practice.html	2.2 KB
18. Optional Extra NumPy resources.html	1.0 KB
9. Manipulating Arrays 2.srt	11.8 KB
16. Turn Images Into NumPy Arrays.srt	10.7 KB
6. NumPy Random Seed.srt	10.0 KB
11. Reshape and Transpose.srt	9.8 KB
10. Standard Deviation and Variance.srt	9.6 KB
15. Sorting Arrays.srt	9.0 KB
2. NumPy Introduction.srt	7.7 KB
14. Comparison Operators.srt	5.4 KB
1. Section Overview.srt	3.2 KB
13. Exercise Nut Butter Store Sales.mp4	95.8 MB
16. Turn Images Into NumPy Arrays.mp4	90.1 MB
12. Dot Product vs Element Wise.mp4	88.0 MB
8. Manipulating Arrays.mp4	84.6 MB
4. NumPy DataTypes and Attributes.mp4	82.8 MB
7. Viewing Arrays and Matrices.mp4	74.1 MB
9. Manipulating Arrays 2.mp4	71.2 MB
5. Creating NumPy Arrays.mp4	70.0 MB
11. Reshape and Transpose.mp4	56.1 MB
6. NumPy Random Seed.mp4	54.4 MB
10. Standard Deviation and Variance.mp4	53.6 MB
15. Sorting Arrays.mp4	34.4 MB
2. NumPy Introduction.mp4	28.1 MB
14. Comparison Operators.mp4	27.7 MB
1. Section Overview.mp4	14.0 MB
16.3 numpy-images.zip	7.6 MB
/.../6. Pandas Data Analysis/
4.1 pandas-anatomy-of-a-dataframe.png	341.2 KB
10.1 pandas-anatomy-of-a-dataframe.png	341.2 KB
9. Manipulating Data.srt	18.5 KB
8. Selecting and Viewing Data with Pandas Part 2.srt	18.4 KB
4. Series, Data Frames and CSVs.srt	17.2 KB
2. Downloading Workbooks and Assignments.html	1.0 KB
3.1 Introduction to Pandas Jupyter Notebook (from the upcoming videos).html	0.2 KB
3.2 10-minutes to pandas (from the pandas documentation).html	0.1 KB
3.3 Pandas Documentation.html	0.1 KB
3.4 Introduction to Pandas Jupyter Notebook (with annotations).html	0.2 KB
5. Data from URLs.html	1.1 KB
7.1 car-sales.csv	0.4 KB
9.1 Jake VanderPlas's Data Manipulation with Pandas.html	0.1 KB
9.2 car-sales-missing-data.csv	0.3 KB
7. Selecting and Viewing Data with Pandas.srt	14.9 KB
11.1 Introduction to Pandas Jupyter Notebook (with annotations).html	0.2 KB
11.2 Introduction to Pandas Jupyter Notebook (from the videos).html	0.2 KB
12. Assignment Pandas Practice.html	2.1 KB
10. Manipulating Data 2.srt	14.2 KB
13.1 Google Colab.html	0.1 KB
13.2 Course notebooks - Github.html	0.1 KB
11. Manipulating Data 3.srt	14.0 KB
6. Describing Data with Pandas.srt	13.9 KB
13. How To Download The Course Assignments.srt	11.3 KB
3. Pandas Introduction.srt	7.2 KB
1. Section Overview.srt	3.8 KB
8. Selecting and Viewing Data with Pandas Part 2.mp4	111.7 MB
9. Manipulating Data.mp4	110.1 MB
4. Series, Data Frames and CSVs.mp4	100.0 MB
11. Manipulating Data 3.mp4	95.4 MB
10. Manipulating Data 2.mp4	90.7 MB
6. Describing Data with Pandas.mp4	79.2 MB
7. Selecting and Viewing Data with Pandas.mp4	75.9 MB
13. How To Download The Course Assignments.mp4	70.0 MB
3. Pandas Introduction.mp4	28.8 MB
1. Section Overview.mp4	11.4 MB
/.../2. Machine Learning 101/
3.1 Teachable Machine.html	0.1 KB
5.1 Machine Learning Playground.html	0.1 KB
7. Are You Getting It Yet.html	0.2 KB
9. Section Review.srt	2.4 KB
1. What Is Machine Learning.srt	8.9 KB
3. Exercise Machine Learning Playground.srt	8.3 KB
4. How Did We Get Here.srt	7.2 KB
2. AIMachine LearningData Science.srt	6.5 KB
8. What Is Machine Learning Round 2.srt	6.2 KB
5. Exercise YouTube Recommendation Engine.srt	5.8 KB
6. Types of Machine Learning.srt	5.4 KB
3. Exercise Machine Learning Playground.mp4	44.7 MB
4. How Did We Get Here.mp4	32.0 MB
1. What Is Machine Learning.mp4	29.7 MB
8. What Is Machine Learning Round 2.mp4	26.7 MB
6. Types of Machine Learning.mp4	23.9 MB
2. AIMachine LearningData Science.mp4	20.6 MB
5. Exercise YouTube Recommendation Engine.mp4	20.4 MB
9. Section Review.mp4	5.8 MB
/.pad/
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316	766.1 KB
317	2.1 MB
318	256.1 KB
319	465.3 KB
/.../14. Neural Networks Deep Learning, Transfer Learning and TensorFlow 2/
32. Training Your Deep Neural Network.srt	23.6 KB
41. Making Predictions On Test Images.srt	20.8 KB
2. Deep Learning and Unstructured Data.srt	20.7 KB
21. Turning Data Into Batches 2.srt	20.6 KB
34. Make And Transform Predictions.srt	19.6 KB
40. Training Model On Full Dataset.srt	19.6 KB
43. Making Predictions On Our Images.srt	19.0 KB
37. Visualizing And Evaluate Model Predictions 2.srt	18.1 KB
35. Transform Predictions To Text.srt	18.0 KB
36. Visualizing Model Predictions.srt	17.4 KB
39. Saving And Loading A Trained Model.srt	17.3 KB
9. Importing TensorFlow 2.srt	17.2 KB
42. Submitting Model to Kaggle.srt	17.0 KB
14. Loading Our Data Labels.srt	16.5 KB
25. Building A Deep Learning Model.srt	16.3 KB
22. Visualizing Our Data.srt	16.0 KB
15. Preparing The Images.srt	15.5 KB
38. Visualizing And Evaluate Model Predictions 3.srt	14.1 KB
16. Turning Data Labels Into Numbers.srt	14.1 KB
18. Preprocess Images.srt	13.2 KB
19. Preprocess Images 2.srt	13.2 KB
26. Building A Deep Learning Model 2.srt	12.8 KB
11. Using A GPU.srt	12.4 KB
28. Building A Deep Learning Model 4.srt	12.3 KB
20. Turning Data Into Batches.srt	11.9 KB
17. Creating Our Own Validation Set.srt	11.6 KB
27. Building A Deep Learning Model 3.srt	11.5 KB
30. Evaluating Our Model.srt	10.7 KB
4. Setting Up Google Colab.srt	9.9 KB
33. Evaluating Performance With TensorBoard.srt	9.8 KB
6. Uploading Project Data.srt	8.8 KB
23. Preparing Our Inputs and Outputs.srt	8.0 KB
13. Optional Reloading Colab Notebook.srt	8.0 KB
7. Setting Up Our Data.srt	6.5 KB
5. Google Colab Workspace.srt	6.5 KB
12. Optional GPU and Google Colab.srt	6.1 KB
29. Summarizing Our Model.srt	6.1 KB
31. Preventing Overfitting.srt	5.7 KB
10. Optional TensorFlow 2.0 Default Issue.srt	4.6 KB
44. Finishing Dog Vision Where to next.html	4.0 KB
1. Section Overview.srt	2.8 KB
24. Optional How machines learn and what's going on behind the scenes.html	2.8 KB
8. Setting Up Our Data 2.srt	2.2 KB
3. Setting Up With Google.html	0.6 KB
43.2 End-to-end Dog Vision Notebook (from the videos).html	0.2 KB
43.1 End-to-end Dog Vision Notebook (with annotations).html	0.2 KB
4.5 End-to-end Dog Vision Notebook (the project we'll be working through).html	0.2 KB
42.1 Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.html	0.2 KB
4.1 Kaggle Dog Breed Identification Competition (the basis of our upcoming project).html	0.1 KB
4.2 Google Colab (our workspace for the upcoming project).html	0.1 KB
4.3 Google Colab IO example (how to get data in and out of your Colab notebook).html	0.1 KB
4.4 Introduction to Google Colab example notebook.html	0.1 KB
27.2 Step by step breakdown of a convolutional neural network (what MobileNetV2 is made of).html	0.2 KB
41.1 Dog Vision Prediction Probabilities Array.html	0.2 KB
5.1 Google Colab FAQ (things you should know about Google Colab).html	0.1 KB
5.2 Google Colab (our workspace for the upcoming project).html	0.1 KB
28.1 [Article] How to choose loss & activation functions when building a deep learning model.html	0.2 KB
6.1 Kaggle Dog Breed Identification Competition Data.html	0.1 KB
6.2 Google Colab IO example (how to get data in and out of your Colab notebook).html	0.1 KB
27.3 MobileNetV2 (the model we're using) architecture explanation by Sik-Ho Tsang.html	0.2 KB
31.1 Early Stopping Callback (a way to stop your model from training when it stops improving) Documentation.html	0.1 KB
30.1 TensorBoard Callback Documentation.html	0.1 KB
25.3 MobileNetV2 (the model we're using) on TensorFlow Hub.html	0.1 KB
10.1 Loading TensorFlow 2.0 into a Colab Notebook (if it isn't the default).html	0.1 KB
14.1 Documentation on how many images Google recommends for image problems.html	0.1 KB
35.1 TensorFlow documentation for the unbatch() function.html	0.1 KB
11.1 Google Colab example GPU usage.html	0.1 KB
21.1 Yann LeCun's (OG of deep learning) Tweet on Batch Sizes.html	0.1 KB
12.1 Introduction to Google Colab example notebook.html	0.1 KB
12.2 Google Colab Example of GPU speed up versus CPU.html	0.1 KB
18.1 Documentation for loading images in TensorFlow.html	0.1 KB
17.1 Blog post by Rachel Thomas (of fast.ai) on how and why you should create a validation set.html	0.1 KB
26.1 Keras in TensorFlow Overview Documentation.html	0.1 KB
27.1 The Softmax Function (activation function we use in our model).html	0.1 KB
18.2 TensorFlow guidelines for loading all kinds of data (turning your data into Tensors).html	0.1 KB
23.1 TensorFlow Hub (resource for pre-trained deep learning models and more).html	0.1 KB
25.1 Andrei Karpathy's talk on AI at Tesla.html	0.1 KB
25.2 Papers with Code (a great resource for some of the best machine learning papers with code examples).html	0.1 KB
25.4 PyTorch Hub (PyTorch version of TensorFlow Hub).html	0.1 KB
25.5 TensorFlow Hub (resource for pre-trained deep learning models and more).html	0.1 KB
32. Training Your Deep Neural Network.mp4	174.7 MB
34. Make And Transform Predictions.mp4	162.5 MB
21. Turning Data Into Batches 2.mp4	156.6 MB
37. Visualizing And Evaluate Model Predictions 2.mp4	150.8 MB
41. Making Predictions On Test Images.mp4	147.7 MB
40. Training Model On Full Dataset.mp4	146.6 MB
15. Preparing The Images.mp4	140.4 MB
35. Transform Predictions To Text.mp4	136.2 MB
39. Saving And Loading A Trained Model.mp4	133.2 MB
22. Visualizing Our Data.mp4	127.9 MB
25. Building A Deep Learning Model.mp4	127.8 MB
42. Submitting Model to Kaggle.mp4	127.2 MB
36. Visualizing Model Predictions.mp4	125.1 MB
43. Making Predictions On Our Images.mp4	125.0 MB
9. Importing TensorFlow 2.mp4	122.4 MB
14. Loading Our Data Labels.mp4	120.4 MB
38. Visualizing And Evaluate Model Predictions 3.mp4	118.7 MB
16. Turning Data Labels Into Numbers.mp4	112.7 MB
27. Building A Deep Learning Model 3.mp4	111.1 MB
26. Building A Deep Learning Model 2.mp4	111.0 MB
19. Preprocess Images 2.mp4	110.2 MB
2. Deep Learning and Unstructured Data.mp4	107.0 MB
18. Preprocess Images.mp4	94.5 MB
13. Optional Reloading Colab Notebook.mp4	93.0 MB
20. Turning Data Into Batches.mp4	92.0 MB
28. Building A Deep Learning Model 4.mp4	90.5 MB
11. Using A GPU.mp4	84.5 MB
30. Evaluating Our Model.mp4	83.1 MB
4. Setting Up Google Colab.mp4	77.8 MB
33. Evaluating Performance With TensorBoard.mp4	77.8 MB
17. Creating Our Own Validation Set.mp4	69.7 MB
6. Uploading Project Data.mp4	54.5 MB
23. Preparing Our Inputs and Outputs.mp4	52.5 MB
12. Optional GPU and Google Colab.mp4	48.1 MB
29. Summarizing Our Model.mp4	47.6 MB
7. Setting Up Our Data.mp4	44.3 MB
5. Google Colab Workspace.mp4	41.6 MB
31. Preventing Overfitting.mp4	38.3 MB
10. Optional TensorFlow 2.0 Default Issue.mp4	29.5 MB
8. Setting Up Our Data 2.mp4	21.9 MB
1. Section Overview.mp4	12.8 MB
/.../11. Milestone Project 1 Supervised Learning (Classification)/
22. Finding The Most Important Features.srt	22.9 KB
9. Finding Patterns 2.srt	22.9 KB
10. Finding Patterns 3.srt	19.3 KB
14. TuningImproving Our Model.srt	18.1 KB
5. Step 1~4 Framework Setup.srt	17.0 KB
15. Tuning Hyperparameters.srt	16.0 KB
19. Evaluating Our Model.srt	15.5 KB
16. Tuning Hyperparameters 2.srt	15.5 KB
3. Project Environment Setup.srt	14.7 KB
23. Reviewing The Project.srt	14.1 KB
8. Finding Patterns.srt	13.7 KB
12. Choosing The Right Models.srt	13.3 KB
6. Getting Our Tools Ready.srt	13.1 KB
11. Preparing Our Data For Machine Learning.srt	12.3 KB
21. Evaluating Our Model 3.srt	11.8 KB
7. Exploring Our Data.srt	11.7 KB
7.1 heart-disease.csv	11.3 KB
2. Project Overview.srt	10.3 KB
17. Tuning Hyperparameters 3.srt	10.2 KB
13. Experimenting With Machine Learning Models.srt	9.9 KB
20. Evaluating Our Model 2.srt	7.6 KB
2.1 End-to-end Heart Disease Classification Notebook (with annotations).html	0.2 KB
2.2 Structured Data Projects on GitHub.html	0.2 KB
2.3 End-to-end Heart Disease Classification Notebook (same as in videos).html	0.2 KB
4. Optional Windows Project Environment Setup.srt	5.7 KB
18. Quick Note Confusion Matrix Labels.html	1.1 KB
23.1 End-to-end Heart Disease Classification Notebook (with annotations).html	0.2 KB
23.2 End-to-end Heart Disease Classification Notebook (same as in videos).html	0.2 KB
1. Section Overview.srt	3.2 KB
10. Finding Patterns 3.mp4	144.6 MB
22. Finding The Most Important Features.mp4	133.7 MB
15. Tuning Hyperparameters.mp4	113.2 MB
5. Step 1~4 Framework Setup.mp4	110.6 MB
16. Tuning Hyperparameters 2.mp4	109.2 MB
14. TuningImproving Our Model.mp4	107.8 MB
3. Project Environment Setup.mp4	105.7 MB
9. Finding Patterns 2.mp4	104.8 MB
12. Choosing The Right Models.mp4	101.1 MB
23. Reviewing The Project.mp4	90.3 MB
6. Getting Our Tools Ready.mp4	83.2 MB
11. Preparing Our Data For Machine Learning.mp4	76.1 MB
19. Evaluating Our Model.mp4	75.1 MB
7. Exploring Our Data.mp4	70.1 MB
21. Evaluating Our Model 3.mp4	68.0 MB
8. Finding Patterns.mp4	66.4 MB
17. Tuning Hyperparameters 3.mp4	66.1 MB
13. Experimenting With Machine Learning Models.mp4	58.0 MB
20. Evaluating Our Model 2.mp4	43.6 MB
4. Optional Windows Project Environment Setup.mp4	37.6 MB
2. Project Overview.mp4	36.1 MB
1. Section Overview.mp4	10.7 MB
/.../12. Milestone Project 2 Supervised Learning (Time Series Data)/
9. Turning Data Into Numbers.srt	22.9 KB
8. Feature Engineering.srt	22.7 KB
6. Exploring Our Data.srt	20.5 KB
19. Preproccessing Our Data.srt	18.2 KB
21. Feature Importance.srt	17.7 KB
10. Filling Missing Numerical Values.srt	17.3 KB
15. Custom Evaluation Function.srt	16.5 KB
3. Project Environment Setup.srt	16.3 KB
16. Reducing Data.srt	15.0 KB
13. Splitting Data.srt	13.8 KB
17. RandomizedSearchCV.srt	13.0 KB
4. Step 1~4 Framework Setup.srt	12.7 KB
20. Making Predictions.srt	11.6 KB
11. Filling Missing Categorical Values.srt	11.5 KB
18. Improving Hyperparameters.srt	11.3 KB
12. Fitting A Machine Learning Model.srt	10.7 KB
7. Exploring Our Data 2.srt	8.8 KB
2. Project Overview.srt	6.8 KB
1. Section Overview.srt	1.9 KB
2.1 Structured Data Projects on GitHub.html	0.2 KB
2.2 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html	0.2 KB
2.3 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html	0.2 KB
2.4 Kaggle Bluebook for Bulldozers Competition.html	0.1 KB
5. Downloading the data for the next two projects.html	1.7 KB
10.1 Pandas Categorical Datatype Documentation.html	0.1 KB
14. Challenge What's wrong with splitting data after filling it.html	1.8 KB
21.1 End-to-end Bluebook Bulldozer Regression Notebook (with annotations).html	0.2 KB
21.2 End-to-end Bluebook Bulldozer Regression Notebook (same as in videos).html	0.2 KB
8. Feature Engineering.mp4	166.9 MB
9. Turning Data Into Numbers.mp4	153.3 MB
21. Feature Importance.mp4	149.2 MB
19. Preproccessing Our Data.mp4	146.1 MB
6. Exploring Our Data.mp4	144.5 MB
10. Filling Missing Numerical Values.mp4	111.5 MB
15. Custom Evaluation Function.mp4	108.4 MB
3. Project Environment Setup.mp4	106.2 MB
16. Reducing Data.mp4	98.0 MB
17. RandomizedSearchCV.mp4	90.0 MB
4. Step 1~4 Framework Setup.mp4	89.8 MB
13. Splitting Data.mp4	86.7 MB
18. Improving Hyperparameters.mp4	83.1 MB
20. Making Predictions.mp4	83.1 MB
11. Filling Missing Categorical Values.mp4	70.2 MB
12. Fitting A Machine Learning Model.mp4	58.2 MB
7. Exploring Our Data 2.mp4	54.6 MB
2. Project Overview.mp4	34.5 MB
1. Section Overview.mp4	9.4 MB
/.../3. Machine Learning and Data Science Framework/
3.1 A 6 Step Field Guide for Machine Learning Modelling (blog post).html	0.1 KB
12. Overfitting and Underfitting Definitions.html	2.0 KB
15. Optional Elements of AI.html	1.0 KB
4. Types of Machine Learning Problems.srt	14.3 KB
11. Modelling - Comparison.srt	13.4 KB
8. Modelling - Splitting Data.srt	7.9 KB
7. Features In Data.srt	6.9 KB
3. 6 Step Machine Learning Framework.srt	6.8 KB
5. Types of Data.srt	6.7 KB
9. Modelling - Picking the Model.srt	6.4 KB
14. Tools We Will Use.srt	6.1 KB
13. Experimentation.srt	5.1 KB
10. Modelling - Tuning.srt	5.0 KB
1. Section Overview.srt	4.8 KB
6. Types of Evaluation.srt	4.4 KB
2. Introducing Our Framework.srt	3.8 KB
4. Types of Machine Learning Problems.mp4	63.4 MB
11. Modelling - Comparison.mp4	47.1 MB
7. Features In Data.mp4	38.6 MB
5. Types of Data.mp4	30.8 MB
8. Modelling - Splitting Data.mp4	28.9 MB
14. Tools We Will Use.mp4	28.7 MB
3. 6 Step Machine Learning Framework.mp4	24.6 MB
9. Modelling - Picking the Model.mp4	24.4 MB
13. Experimentation.mp4	22.4 MB
6. Types of Evaluation.mp4	18.6 MB
10. Modelling - Tuning.mp4	16.8 MB
1. Section Overview.mp4	14.0 MB
2. Introducing Our Framework.mp4	11.9 MB
/.../16. Career Advice + Extra Bits/
9. CWD Git + Github.srt	21.7 KB
3. What If I Don't Have Enough Experience.srt	20.5 KB
10. CWD Git + Github 2.srt	18.7 KB
11. Contributing To Open Source.srt	17.5 KB
9. CWD Git + Github.mp4	184.7 MB
12. Contributing To Open Source 2.srt	10.4 KB
7. JTS Start With Why.srt	3.0 KB
6. JTS Learn to Learn.srt	2.6 KB
1. Endorsements On LinkedIn.html	2.1 KB
14. Exercise Contribute To Open Source.html	1.5 KB
13. Coding Challenges.html	0.9 KB
2. Quick Note Upcoming Video.html	0.6 KB
5. Quick Note Upcoming Videos.html	0.6 KB
8. Quick Note Upcoming Videos.html	0.4 KB
4. Learning Guideline.html	0.3 KB
3. What If I Don't Have Enough Experience.mp4	168.8 MB
11. Contributing To Open Source.mp4	136.6 MB
10. CWD Git + Github 2.mp4	124.1 MB
12. Contributing To Open Source 2.mp4	118.5 MB
7. JTS Start With Why.mp4	16.2 MB
6. JTS Learn to Learn.mp4	11.7 MB
/4. The 2 Paths/
2. Python + Machine Learning Monthly.html	0.9 KB
3. Endorsements On LinkedIN.html	2.1 KB
1. The 2 Paths.srt	4.8 KB
1. The 2 Paths.mp4	10.2 MB
/.../17. Learn Python/
17. Variables.srt	16.4 KB
11. Numbers.srt	11.4 KB
35. List Methods.srt	11.0 KB
27. Built-In Functions + Methods.srt	10.5 KB
49. Sets 2.srt	9.5 KB
25. String Indexes.srt	9.4 KB
4. Our First Python Program.srt	9.2 KB
24. Formatted Strings.srt	9.0 KB
29. Exercise Type Conversion.srt	8.8 KB
33. List Slicing.srt	8.7 KB
2. Python Interpreter.srt	8.7 KB
6. Python 2 vs Python 3.srt	8.6 KB
48. Sets.srt	8.6 KB
31. Exercise Password Checker.srt	8.1 KB
45. Dictionary Methods 2.srt	7.3 KB
41. Dictionaries.srt	7.3 KB
1. What Is A Programming Language.srt	7.2 KB
3. How To Run Python Code.srt	6.7 KB
20. Strings.srt	6.4 KB
38. Common List Patterns.srt	6.0 KB
46. Tuples.srt	5.8 KB
32. Lists.srt	5.7 KB
12. Math Functions.srt	5.6 KB
30. DEVELOPER FUNDAMENTALS II.srt	5.4 KB
44. Dictionary Methods.srt	5.4 KB
13. DEVELOPER FUNDAMENTALS I.srt	5.3 KB
9. Python Data Types.srt	5.3 KB
37. List Methods 3.srt	5.1 KB
23. Escape Sequences.srt	5.1 KB
16. Optional bin() and complex.srt	4.9 KB
36. List Methods 2.srt	4.6 KB
43. Dictionary Keys.srt	4.3 KB
34. Matrix.srt	4.2 KB
28. Booleans.srt	4.0 KB
42. DEVELOPER FUNDAMENTALS III.srt	3.7 KB
14. Operator Precedence.srt	3.6 KB
26. Immutability.srt	3.6 KB
22. Type Conversion.srt	3.2 KB
47. Tuples 2.srt	3.1 KB
19. Augmented Assignment Operator.srt	3.0 KB
39. List Unpacking.srt	3.0 KB
7. Exercise How Does Python Work.srt	2.9 KB
5. Latest Version Of Python.srt	2.8 KB
8. Learning Python.srt	2.6 KB
40. None.srt	2.2 KB
18. Expressions vs Statements.srt	1.8 KB
21. String Concatenation.srt	1.5 KB
15. Exercise Operator Precedence.html	0.7 KB
10. How To Succeed.html	0.3 KB
6.3 Python 2 vs Python 3 - another one.html	0.2 KB
6.1 Python 2 vs Python 3.html	0.1 KB
44.1 Dictionary Methods.html	0.1 KB
17.1 Python Keywords.html	0.1 KB
36.1 Python Keywords.html	0.1 KB
19.1 Exercise Repl.html	0.1 KB
27.1 String Methods.html	0.1 KB
47.1 Tuple Methods.html	0.1 KB
35.1 List Methods.html	0.1 KB
49.2 Sets Methods.html	0.1 KB
16.1 Base Numbers.html	0.1 KB
27.2 Built in Functions.html	0.1 KB
14.1 Exercise Repl.html	0.1 KB
15.1 Exercise Repl.html	0.1 KB
30.1 Python Comments Best Practices.html	0.1 KB
6.2 The Story of Python.html	0.1 KB
11.1 Floating point numbers.html	0.1 KB
24.1 Exercise Repl.html	0.1 KB
25.1 Exercise Repl.html	0.1 KB
45.1 Exercise Repl.html	0.1 KB
36.2 Exercise Repl.html	0.1 KB
38.1 Exercise Repl.html	0.1 KB
34.1 Exercise Repl.html	0.1 KB
33.1 Exercise Repl.html	0.1 KB
49.1 Exercise Repl.html	0.1 KB
2.1 python.org.html	0.1 KB
3.1 Glot.io.html	0.1 KB
3.2 Repl.it.html	0.1 KB
1. What Is A Programming Language.mp4	109.9 MB
17. Variables.mp4	98.1 MB
2. Python Interpreter.mp4	81.8 MB
11. Numbers.mp4	76.2 MB
6. Python 2 vs Python 3.mp4	72.9 MB
27. Built-In Functions + Methods.mp4	72.8 MB
49. Sets 2.mp4	67.4 MB
35. List Methods.mp4	64.8 MB
13. DEVELOPER FUNDAMENTALS I.mp4	62.6 MB
3. How To Run Python Code.mp4	55.4 MB
31. Exercise Password Checker.mp4	53.6 MB
29. Exercise Type Conversion.mp4	52.8 MB
33. List Slicing.mp4	52.3 MB
24. Formatted Strings.mp4	51.6 MB
25. String Indexes.mp4	51.5 MB
4. Our First Python Program.mp4	49.5 MB
45. Dictionary Methods 2.mp4	44.4 MB
12. Math Functions.mp4	43.8 MB
38. Common List Patterns.mp4	42.4 MB
8. Learning Python.mp4	40.4 MB
48. Sets.mp4	38.8 MB
41. Dictionaries.mp4	34.3 MB
20. Strings.mp4	32.5 MB
30. DEVELOPER FUNDAMENTALS II.mp4	30.7 MB
9. Python Data Types.mp4	30.3 MB
37. List Methods 3.mp4	29.0 MB
36. List Methods 2.mp4	28.7 MB
44. Dictionary Methods.mp4	28.5 MB
42. DEVELOPER FUNDAMENTALS III.mp4	27.9 MB
7. Exercise How Does Python Work.mp4	27.2 MB
46. Tuples.mp4	26.9 MB
23. Escape Sequences.mp4	24.3 MB
32. Lists.mp4	23.0 MB
16. Optional bin() and complex.mp4	23.0 MB
26. Immutability.mp4	21.8 MB
43. Dictionary Keys.mp4	21.4 MB
34. Matrix.mp4	20.1 MB
22. Type Conversion.mp4	19.9 MB
47. Tuples 2.mp4	17.8 MB
28. Booleans.mp4	17.4 MB
19. Augmented Assignment Operator.mp4	16.1 MB
14. Operator Precedence.mp4	15.1 MB
39. List Unpacking.mp4	14.5 MB
18. Expressions vs Statements.mp4	11.5 MB
5. Latest Version Of Python.mp4	11.2 MB
40. None.mp4	8.3 MB
21. String Concatenation.mp4	7.7 MB
/.../18. Learn Python Part 2/
2. Conditional Logic.srt	16.0 KB
24. return.srt	15.3 KB
45. Modules in Python.srt	13.0 KB
48. Packages in Python.srt	12.8 KB
47. Optional PyCharm.srt	10.8 KB
18. Our First GUI.srt	10.6 KB
36. Pure Functions.srt	10.3 KB
41. List Comprehensions.srt	9.6 KB
21. Functions.srt	9.4 KB
32. Scope Rules.srt	8.7 KB
8. Exercise Logical Operators.srt	8.6 KB
40. reduce().srt	8.6 KB
9. is vs ==.srt	8.3 KB
7. Logical Operators.srt	8.3 KB
29. args and kwargs.srt	8.3 KB
19. DEVELOPER FUNDAMENTALS IV.srt	8.0 KB
10. For Loops.srt	7.7 KB
49. Different Ways To Import.srt	7.7 KB
15. While Loops.srt	7.5 KB
11. Iterables.srt	7.0 KB
33. global Keyword.srt	6.8 KB
42. Set Comprehensions.srt	6.7 KB
16. While Loops 2.srt	6.6 KB
37. map().srt	6.4 KB
4. Truthy vs Falsey.srt	6.1 KB
23. Default Parameters and Keyword Arguments.srt	6.1 KB
13. range().srt	6.0 KB
28. Clean Code.srt	5.5 KB
3. Indentation In Python.srt	5.4 KB
17. break, continue, pass.srt	5.4 KB
26. Methods vs Functions.srt	5.4 KB
38. filter().srt	5.2 KB
43. Exercise Comprehensions.srt	5.1 KB
22. Parameters and Arguments.srt	5.0 KB
5. Ternary Operator.srt	4.9 KB
35. Why Do We Need Scope.srt	4.9 KB
30. Exercise Functions.srt	4.8 KB
14. enumerate().srt	4.7 KB
6. Short Circuiting.srt	4.6 KB
20. Exercise Find Duplicates.srt	4.5 KB
27. Docstrings.srt	4.4 KB
34. nonlocal Keyword.srt	4.2 KB
31. Scope.srt	3.9 KB
12. Exercise Tricky Counter.srt	3.7 KB
39. zip().srt	3.3 KB
1. Breaking The Flow.srt	3.1 KB
44. Python Exam Testing Your Understanding.html	1.1 KB
50. Next Steps.html	1.0 KB
51. Bonus Resource Python Cheatsheet.html	0.5 KB
46. Quick Note Upcoming Videos.html	0.4 KB
25. Exercise Tesla.html	0.4 KB
4.1 Truthy vs Falsey Stackoverflow.html	0.2 KB
30.1 Solution Repl.html	0.1 KB
20.1 Solution Repl.html	0.1 KB
43.2 Solution Repl.html	0.1 KB
43.1 Exercise Repl.html	0.1 KB
18.1 Exercise Repl.html	0.1 KB
18.2 Solution Repl.html	0.1 KB
34.1 Solution Repl.html	0.1 KB
12.1 Solution Repl.html	0.1 KB
45. Modules in Python.mp4	86.2 MB
2. Conditional Logic.mp4	78.2 MB
48. Packages in Python.mp4	75.9 MB
36. Pure Functions.mp4	70.6 MB
24. return.mp4	66.1 MB
41. List Comprehensions.mp4	55.9 MB
47. Optional PyCharm.mp4	55.6 MB
40. reduce().mp4	54.8 MB
19. DEVELOPER FUNDAMENTALS IV.mp4	52.7 MB
18. Our First GUI.mp4	52.0 MB
21. Functions.mp4	51.0 MB
49. Different Ways To Import.mp4	50.3 MB
8. Exercise Logical Operators.mp4	48.9 MB
11. Iterables.mp4	45.3 MB
29. args and kwargs.mp4	45.1 MB
4. Truthy vs Falsey.mp4	44.9 MB
37. map().mp4	40.2 MB
23. Default Parameters and Keyword Arguments.mp4	40.0 MB
32. Scope Rules.mp4	39.5 MB
33. global Keyword.mp4	38.3 MB
42. Set Comprehensions.mp4	37.1 MB
10. For Loops.mp4	36.0 MB
9. is vs ==.mp4	35.2 MB
26. Methods vs Functions.mp4	32.2 MB
13. range().mp4	29.7 MB
7. Logical Operators.mp4	29.7 MB
15. While Loops.mp4	29.7 MB
3. Indentation In Python.mp4	29.4 MB
16. While Loops 2.mp4	27.2 MB
14. enumerate().mp4	26.0 MB
38. filter().mp4	24.7 MB
22. Parameters and Arguments.mp4	24.3 MB
17. break, continue, pass.mp4	23.3 MB
43. Exercise Comprehensions.mp4	23.0 MB
30. Exercise Functions.mp4	22.9 MB
39. zip().mp4	22.3 MB
1. Breaking The Flow.mp4	21.3 MB
20. Exercise Find Duplicates.mp4	21.2 MB
31. Scope.mp4	21.1 MB
5. Ternary Operator.mp4	20.7 MB
28. Clean Code.mp4	20.6 MB
6. Short Circuiting.mp4	20.3 MB
35. Why Do We Need Scope.mp4	20.1 MB
34. nonlocal Keyword.mp4	19.1 MB
27. Docstrings.mp4	18.2 MB
12. Exercise Tricky Counter.mp4	17.2 MB
/.../13. Data Engineering/
9. Optional OLTP Databases.srt	12.4 KB
7. Types Of Databases.srt	8.6 KB
2. What Is Data.srt	7.8 KB
4. What Is A Data Engineer 2.srt	6.5 KB
5. What Is A Data Engineer 3.srt	5.5 KB
13. Kafka and Stream Processing.srt	5.2 KB
3. What Is A Data Engineer.srt	5.0 KB
11. Hadoop, HDFS and MapReduce.srt	4.8 KB
1. Data Engineering Introduction.srt	4.4 KB
6. What Is A Data Engineer 4.srt	4.0 KB
12. Apache Spark and Apache Flink.srt	2.4 KB
2.1 Kaggle.html	0.1 KB
7.1 OLTP vs OLAP.html	0.1 KB
7.2 A Primer on ACID Transactions.html	0.1 KB
8. Quick Note Upcoming Video.html	0.5 KB
10. Optional Learn SQL.html	0.4 KB
9. Optional OLTP Databases.mp4	83.6 MB
2. What Is Data.mp4	44.3 MB
7. Types Of Databases.mp4	34.1 MB
5. What Is A Data Engineer 3.mp4	25.5 MB
4. What Is A Data Engineer 2.mp4	25.4 MB
13. Kafka and Stream Processing.mp4	20.2 MB
3. What Is A Data Engineer.mp4	15.9 MB
6. What Is A Data Engineer 4.mp4	15.7 MB
1. Data Engineering Introduction.mp4	14.2 MB
11. Hadoop, HDFS and MapReduce.mp4	10.6 MB
12. Apache Spark and Apache Flink.mp4	6.0 MB
/.../15. Storytelling + Communication How To Present Your Work/
5. Weekend Project Principle.srt	9.2 KB
4. Communicating With Co-Workers.srt	5.7 KB
2. Communicating Your Work.srt	5.0 KB
3. Communicating With Managers.srt	4.6 KB
6. Communicating With Outside World.srt	4.6 KB
7. Storytelling.srt	4.2 KB
8. Communicating and sharing your work Further reading.html	3.2 KB
2.1 How to Think About Communicating and Sharing Your Work (blog post).html	0.1 KB
6.1 fast_template by fast.ai (a template you can use for your blog on GitHub Pages).html	0.1 KB
6.2 Devblog by Hashnode (an easy and free way to create a blog you own).html	0.1 KB
5. Weekend Project Principle.mp4	24.7 MB
2. Communicating Your Work.mp4	21.2 MB
4. Communicating With Co-Workers.mp4	19.9 MB
3. Communicating With Managers.mp4	19.3 MB
6. Communicating With Outside World.mp4	15.2 MB
7. Storytelling.mp4	12.6 MB
1. Section Overview.mp4	11.4 MB
1. Section Overview.srt	5.1 MB
/.../10. Supervised Learning Classification + Regression/
1. Milestone Projects!.html	0.7 KB
/.../20. Where To Go From Here/
2. Thank You.srt	3.7 KB
1. Become An Alumni.html	0.9 KB
3. Course Review.html	0.2 KB
4. The Final Challenge.html	0.2 KB
2. Thank You.mp4	11.7 MB
/.../21. BONUS SECTION/
1. Bonus Lecture.html	3.4 KB
/.../19. Bonus Learn Advanced Statistics and Mathematics for FREE!/
1. Statistics and Mathematics.html	0.7 KB
Total files 1163