Coursera ML | E9D6C0D130949E16F3F8D7105241D28B55590A18

Coursera ML

Name	Coursera-ML	DOWNLOAD Copy Link
Total Size	1.6 GB
Total Files	274
Hash	E9D6C0D130949E16F3F8D7105241D28B55590A18

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avatar.png	56.8 KB
/I. Introduction (Week 1)/
1 - 1 - Welcome (7 min).mp4	12.5 MB
1 - 1 - Welcome (7 min).srt	10.1 KB
1 - 2 - What is Machine Learning (7 min).mp4	9.8 MB
1 - 2 - What is Machine Learning- (7 min).srt	10.4 KB
1 - 3 - Supervised Learning (12 min).mp4	14.1 MB
1 - 3 - Supervised Learning (12 min).srt	17.2 KB
1 - 4 - Unsupervised Learning (14 min).mp4	17.5 MB
1 - 4 - Unsupervised Learning (14 min).srt	29.8 KB
docs_slides_Lecture1.pdf	3.5 MB
docs_slides_Lecture1.pptx	4.2 MB
/II. Linear Regression with One Variable (Week 1)/
2 - 1 - Model Representation (8 min).mp4	9.4 MB
2 - 1 - Model Representation (8 min).srt	10.2 KB
2 - 2 - Cost Function (8 min).mp4	9.5 MB
2 - 2 - Cost Function (8 min).srt	10.1 KB
2 - 3 - Cost Function - Intuition I (11 min).mp4	12.8 MB
2 - 3 - Cost Function - Intuition I (11 min).srt	12.4 KB
2 - 4 - Cost Function - Intuition II (9 min).mp4	11.9 MB
2 - 4 - Cost Function - Intuition II (9 min).srt	11.4 KB
2 - 5 - Gradient Descent (11 min).mp4	14.2 MB
2 - 5 - Gradient Descent (11 min).srt	15.8 KB
2 - 6 - Gradient Descent Intuition (12 min).mp4	13.7 MB
2 - 6 - Gradient Descent Intuition (12 min).srt	15.9 KB
2 - 7 - Gradient Descent For Linear Regression (10 min).mp4	12.8 MB
2 - 7 - Gradient Descent For Linear Regression (10 min).srt	19.2 KB
2 - 8 - What's Next (6 min).srt	8.7 KB
2 - 8 - Whats Next (6 min).mp4	6.4 MB
docs_slides_Lecture2.pdf	3.0 MB
docs_slides_Lecture2.pptx	5.6 MB
/III. Linear Algebra Review (Week 1, Optional)/
3 - 1 - Matrices and Vectors (9 min).mp4	10.0 MB
3 - 1 - Matrices and Vectors (9 min).srt	16.3 KB
3 - 1 - Matrices and Vectors (9 min).txt	7.2 KB
3 - 2 - Addition and Scalar Multiplication (7 min).mp4	7.8 MB
3 - 2 - Addition and Scalar Multiplication (7 min).srt	12.3 KB
3 - 3 - Matrix Vector Multiplication (14 min).mp4	15.7 MB
3 - 3 - Matrix Vector Multiplication (14 min).srt	24.8 KB
3 - 4 - Matrix Matrix Multiplication (11 min).mp4	13.2 MB
3 - 4 - Matrix Matrix Multiplication (11 min).srt	21.1 KB
3 - 5 - Matrix Multiplication Properties (9 min).mp4	10.3 MB
3 - 5 - Matrix Multiplication Properties (9 min).srt	17.2 KB
3 - 6 - Inverse and Transpose (11 min).mp4	13.5 MB
3 - 6 - Inverse and Transpose (11 min).srt	21.6 KB
docs_slides_Lecture3.pdf	1.9 MB
docs_slides_Lecture3.pptx	5.2 MB
/IV. Linear Regression with Multiple Variables (Week 2)/
4 - 1 - Multiple Features (8 min).mp4	9.3 MB
4 - 1 - Multiple Features (8 min).srt	14.9 KB
4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4	6.1 MB
4 - 2 - Gradient Descent for Multiple Variables (5 min).srt	6.8 KB
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4	9.9 MB
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).srt	17.4 KB
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4	9.7 MB
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).srt	18.9 KB
4 - 5 - Features and Polynomial Regression (8 min).mp4	8.7 MB
4 - 5 - Features and Polynomial Regression (8 min).srt	16.3 KB
4 - 6 - Normal Equation (16 min).mp4	18.0 MB
4 - 6 - Normal Equation (16 min).srt	31.9 KB
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4	6.5 MB
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).srt	10.0 KB
docs_slides_Lecture4.pdf	1.8 MB
docs_slides_Lecture4.pptx	4.6 MB
ex1.zip	481.1 KB
/IX. Neural Networks Learning (Week 5)/
9 - 1 - Cost Function (7 min).mp4	8.0 MB
9 - 1 - Cost Function (7 min).srt	13.5 KB
9 - 2 - Backpropagation Algorithm (12 min).mp4	14.6 MB
9 - 2 - Backpropagation Algorithm (12 min).srt	23.4 KB
9 - 3 - Backpropagation Intuition (13 min).mp4	16.2 MB
9 - 3 - Backpropagation Intuition (13 min).srt	25.6 KB
9 - 4 - Implementation Note Unrolling Parameters (8 min).mp4	9.8 MB
9 - 4 - Implementation Note- Unrolling Parameters (8 min).srt	15.3 KB
9 - 5 - Gradient Checking (12 min).mp4	14.2 MB
9 - 5 - Gradient Checking (12 min).srt	24.1 KB
9 - 6 - Random Initialization (7 min).mp4	7.9 MB
9 - 6 - Random Initialization (7 min).srt	14.3 KB
9 - 7 - Putting It Together (14 min).mp4	17.1 MB
9 - 7 - Putting It Together (14 min).srt	28.3 KB
9 - 8 - Autonomous Driving (7 min).mp4	15.6 MB
9 - 8 - Autonomous Driving (7 min).srt	10.0 KB
docs_slides_Lecture9.pdf	3.5 MB
docs_slides_Lecture9.pptx	5.2 MB
ex4.zip	7.9 MB
/V. Octave Tutorial (Week 2)/
5 - 1 - Basic Operations (14 min).mp4	18.6 MB
5 - 1 - Basic Operations (14 min).srt	26.0 KB
5 - 2 - Moving Data Around (16 min).mp4	21.8 MB
5 - 2 - Moving Data Around (16 min).srt	29.3 KB
5 - 3 - Computing on Data (13 min).mp4	16.0 MB
5 - 3 - Computing on Data (13 min).srt	25.5 KB
5 - 4 - Plotting Data (10 min).mp4	14.0 MB
5 - 4 - Plotting Data (10 min).srt	17.8 KB
5 - 5 - Control Statements for while if statements (13 min).mp4	17.3 MB
5 - 5 - Control Statements- for, while, if statements (13 min).srt	23.9 KB
5 - 6 - Vectorization (14 min).mp4	16.9 MB
5 - 6 - Vectorization (14 min).srt	25.8 KB
5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4	5.7 MB
5 - 7 - Working on and Submitting Programming Exercises (4 min).srt	4.5 KB
docs_slides_Lecture5.pdf	248.2 KB
docs_slides_Lecture5.pptx	417.1 KB
/VI. Logistic Regression (Week 3)/
6 - 1 - Classification (8 min).mp4	9.2 MB
6 - 1 - Classification (8 min).srt	16.6 KB
6 - 2 - Hypothesis Representation (7 min).mp4	8.7 MB
6 - 2 - Hypothesis Representation (7 min).srt	14.5 KB
6 - 3 - Decision Boundary (15 min).mp4	17.6 MB
6 - 3 - Decision Boundary (15 min).srt	27.4 KB
6 - 4 - Cost Function (11 min).mp4	13.7 MB
6 - 4 - Cost Function (11 min).srt	22.7 KB
6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4	12.5 MB
6 - 5 - Simplified Cost Function and Gradient Descent (10 min).srt	20.0 KB
6 - 6 - Advanced Optimization (14 min).mp4	19.0 MB
6 - 6 - Advanced Optimization (14 min).srt	28.5 KB
6 - 7 - Multiclass Classification One-vs-all (6 min).mp4	7.3 MB
6 - 7 - Multiclass Classification- One-vs-all (6 min).srt	12.9 KB
docs_slides_Lecture6.pdf	2.2 MB
docs_slides_Lecture6.pptx	4.0 MB
/VII. Regularization (Week 3)/
7 - 1 - The Problem of Overfitting (10 min).mp4	11.7 MB
7 - 1 - The Problem of Overfitting (10 min).srt	19.7 KB
7 - 2 - Cost Function (10 min).mp4	12.2 MB
7 - 2 - Cost Function (10 min).srt	20.2 KB
7 - 3 - Regularized Linear Regression (11 min).mp4	12.6 MB
7 - 3 - Regularized Linear Regression (11 min).srt	20.9 KB
7 - 4 - Regularized Logistic Regression (9 min).mp4	11.4 MB
7 - 4 - Regularized Logistic Regression (9 min).srt	17.6 KB
docs_slides_Lecture7.pdf	2.5 MB
docs_slides_Lecture7.pptx	2.7 MB
ex2.zip	248.8 KB
/VIII. Neural Networks Representation (Week 4)/
8 - 1 - Non-linear Hypotheses (10 min).mp4	11.4 MB
8 - 1 - Non-linear Hypotheses (10 min).srt	19.5 KB
8 - 2 - Neurons and the Brain (8 min).mp4	10.4 MB
8 - 2 - Neurons and the Brain (8 min).srt	16.8 KB
8 - 3 - Model Representation I (12 min).mp4	14.2 MB
8 - 3 - Model Representation I (12 min).srt	22.1 KB
8 - 4 - Model Representation II (12 min).mp4	14.1 MB
8 - 4 - Model Representation II (12 min).srt	23.0 KB
8 - 5 - Examples and Intuitions I (7 min).mp4	8.3 MB
8 - 5 - Examples and Intuitions I (7 min).srt	13.4 KB
8 - 6 - Examples and Intuitions II (10 min).mp4	14.7 MB
8 - 6 - Examples and Intuitions II (10 min).srt	17.5 KB
8 - 7 - Multiclass Classification (4 min).mp4	5.1 MB
8 - 7 - Multiclass Classification (4 min).srt	7.6 KB
docs_slides_Lecture8.pdf	5.2 MB
docs_slides_Lecture8.pptx	42.3 MB
ex3.zip	7.9 MB
/X. Advice for Applying Machine Learning (Week 6)/
10 - 1 - Deciding What to Try Next (6 min).mp4	7.2 MB
10 - 1 - Deciding What to Try Next (6 min).srt	12.7 KB
10 - 2 - Evaluating a Hypothesis (8 min).mp4	8.9 MB
10 - 2 - Evaluating a Hypothesis (8 min).srt	11.8 KB
10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mp4	14.8 MB
10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).srt	25.2 KB
10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4	9.4 MB
10 - 4 - Diagnosing Bias vs. Variance (8 min).srt	16.5 KB
10 - 5 - Regularization and Bias_Variance (11 min).mp4	13.2 MB
10 - 5 - Regularization and Bias_Variance (11 min).srt	23.1 KB
10 - 6 - Learning Curves (12 min).mp4	13.5 MB
10 - 6 - Learning Curves (12 min).srt	25.3 KB
10 - 7 - Deciding What to Do Next Revisited (7 min).mp4	8.6 MB
10 - 7 - Deciding What to Do Next Revisited (7 min).srt	14.4 KB
docs_slides_Lecture10.pdf	1.6 MB
docs_slides_Lecture10.pptx	3.5 MB
ex5.zip	181.3 KB
/XI. Machine Learning System Design (Week 6)/
11 - 1 - Prioritizing What to Work On (10 min).mp4	11.7 MB
11 - 1 - Prioritizing What to Work On (10 min).srt	20.1 KB
11 - 2 - Error Analysis (13 min).mp4	16.2 MB
11 - 2 - Error Analysis (13 min).srt	28.1 KB
11 - 3 - Error Metrics for Skewed Classes (12 min).mp4	13.9 MB
11 - 3 - Error Metrics for Skewed Classes (12 min).srt	22.6 KB
11 - 4 - Trading Off Precision and Recall (14 min).mp4	16.8 MB
11 - 4 - Trading Off Precision and Recall (14 min).srt	29.3 KB
11 - 5 - Data For Machine Learning (11 min).mp4	13.5 MB
11 - 5 - Data For Machine Learning (11 min).srt	23.7 KB
docs_slides_Lecture11.pdf	509.6 KB
docs_slides_Lecture11.pptx	2.0 MB
/XII. Support Vector Machines (Week 7)/
12 - 1 - Optimization Objective (15 min).mp4	17.5 MB
12 - 1 - Optimization Objective (15 min).srt	30.1 KB
12 - 2 - Large Margin Intuition (11 min).mp4	12.4 MB
12 - 2 - Large Margin Intuition (11 min).srt	21.8 KB
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4	22.9 MB
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).srt	36.7 KB
12 - 4 - Kernels I (16 min).mp4	18.4 MB
12 - 4 - Kernels I (16 min).srt	29.8 KB
12 - 5 - Kernels II (16 min) (1).mp4	18.3 MB
12 - 5 - Kernels II (16 min) (1).srt	31.4 KB
12 - 5 - Kernels II (16 min).mp4	18.3 MB
12 - 5 - Kernels II (16 min).srt	31.4 KB
12 - 6 - Using An SVM (21 min).mp4	25.1 MB
12 - 6 - Using An SVM (21 min).srt	44.5 KB
docs_slides_Lecture12.pdf	2.4 MB
docs_slides_Lecture12.pptx	5.6 MB
ex6.zip	917.9 KB
/XIII. Clustering (Week 8)/
13 - 1 - Unsupervised Learning Introduction (3 min).mp4	4.0 MB
13 - 1 - Unsupervised Learning- Introduction (3 min).srt	7.2 KB
13 - 2 - K-Means Algorithm (13 min).mp4	14.5 MB
13 - 2 - K-Means Algorithm (13 min).srt	26.9 KB
13 - 3 - Optimization Objective (7 min).mp4	8.5 MB
13 - 3 - Optimization Objective (7 min).srt	14.0 KB
13 - 4 - Random Initialization (8 min).mp4	9.1 MB
13 - 4 - Random Initialization (8 min).srt	16.6 KB
13 - 5 - Choosing the Number of Clusters (8 min).mp4	9.9 MB
13 - 5 - Choosing the Number of Clusters (8 min).srt	18.4 KB
docs_slides_Lecture13.pdf	2.3 MB
docs_slides_Lecture13.pptx	2.9 MB
/XIV. Dimensionality Reduction (Week 8)/
14 - 1 - Motivation I Data Compression (10 min).mp4	15.0 MB
14 - 1 - Motivation I- Data Compression (10 min).srt	20.6 KB
14 - 2 - Motivation II Visualization (6 min).mp4	6.6 MB
14 - 2 - Motivation II- Visualization (6 min).srt	10.4 KB
14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4	11.0 MB
14 - 3 - Principal Component Analysis Problem Formulation (9 min).srt	18.9 KB
14 - 4 - Principal Component Analysis Algorithm (15 min).mp4	18.7 MB
14 - 4 - Principal Component Analysis Algorithm (15 min).srt	29.3 KB
14 - 5 - Choosing the Number of Principal Components (11 min).mp4	12.4 MB
14 - 5 - Choosing the Number of Principal Components (11 min).srt	21.7 KB
14 - 6 - Reconstruction from Compressed Representation (4 min).mp4	5.2 MB
14 - 6 - Reconstruction from Compressed Representation (4 min).srt	7.7 KB
14 - 7 - Advice for Applying PCA (13 min).mp4	15.4 MB
14 - 7 - Advice for Applying PCA (13 min).srt	27.0 KB
docs_slides_Lecture14.pdf	1.7 MB
docs_slides_Lecture14.pptx	3.8 MB
ex7.zip	11.6 MB
/XIX. Conclusion/
19 - 1 - Summary and Thank You (5 min).mp4	6.4 MB
19 - 1 - Summary and Thank You (5 min).srt	8.3 KB
/XV. Anomaly Detection (Week 9)/
15 - 1 - Problem Motivation (8 min).mp4	8.8 MB
15 - 1 - Problem Motivation (8 min).srt	16.4 KB
15 - 2 - Gaussian Distribution (10 min).mp4	12.3 MB
15 - 2 - Gaussian Distribution (10 min).srt	21.1 KB
15 - 3 - Algorithm (12 min).mp4	14.6 MB
15 - 3 - Algorithm (12 min).srt	24.1 KB
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4	15.9 MB
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).srt	27.9 KB
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4	9.7 MB
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).srt	16.8 KB
15 - 6 - Choosing What Features to Use (12 min).mp4	14.8 MB
15 - 6 - Choosing What Features to Use (12 min).srt	25.7 KB
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4	16.7 MB
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).srt	28.1 KB
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4	17.1 MB
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).srt	26.9 KB
docs_slides_Lecture15.pdf	3.5 MB
docs_slides_Lecture15.pptx	6.3 MB
/XVI. Recommender Systems (Week 9)/
16 - 1 - Problem Formulation (8 min).mp4	11.2 MB
16 - 1 - Problem Formulation (8 min).srt	17.2 KB
16 - 2 - Content Based Recommendations (15 min).mp4	17.8 MB
16 - 2 - Content Based Recommendations (15 min).srt	29.3 KB
16 - 3 - Collaborative Filtering (10 min).mp4	12.3 MB
16 - 3 - Collaborative Filtering (10 min).srt	20.7 KB
16 - 4 - Collaborative Filtering Algorithm (9 min).mp4	10.8 MB
16 - 4 - Collaborative Filtering Algorithm (9 min).srt	16.9 KB
16 - 5 - Vectorization Low Rank Matrix Factorization (8 min).mp4	10.2 MB
16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).srt	16.7 KB
16 - 6 - Implementational Detail Mean Normalization (9 min).mp4	10.2 MB
16 - 6 - Implementational Detail- Mean Normalization (9 min).srt	17.0 KB
docs_slides_Lecture16.pdf	1.5 MB
docs_slides_Lecture16.pptx	3.8 MB
ex8.zip	813.9 KB
/XVII. Large Scale Machine Learning (Week 10)/
17 - 1 - Learning With Large Datasets (6 min).mp4	6.8 MB
17 - 1 - Learning With Large Datasets (6 min).srt	8.1 KB
17 - 2 - Stochastic Gradient Descent (13 min).mp4	16.1 MB
17 - 2 - Stochastic Gradient Descent (13 min).srt	18.6 KB
17 - 3 - Mini-Batch Gradient Descent (6 min).mp4	7.7 MB
17 - 3 - Mini-Batch Gradient Descent (6 min).srt	8.0 KB
17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4	14.0 MB
17 - 4 - Stochastic Gradient Descent Convergence (12 min).srt	16.6 KB
17 - 5 - Online Learning (13 min).mp4	15.6 MB
17 - 5 - Online Learning (13 min).srt	28.3 KB
17 - 6 - Map Reduce and Data Parallelism (14 min).mp4	16.8 MB
17 - 6 - Map Reduce and Data Parallelism (14 min).srt	29.6 KB
docs_slides_Lecture17.pdf	2.1 MB
docs_slides_Lecture17.pptx	4.0 MB
/XVIII. Application Example Photo OCR/
18 - 1 - Problem Description and Pipeline (7 min).mp4	8.3 MB
18 - 1 - Problem Description and Pipeline (7 min).srt	15.1 KB
18 - 2 - Sliding Windows (15 min).mp4	17.3 MB
18 - 2 - Sliding Windows (15 min).srt	32.2 KB
18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4	19.7 MB
18 - 3 - Getting Lots of Data and Artificial Data (16 min).srt	36.0 KB
18 - 4 - Ceiling Analysis What Part of the Pipeline to Work on Next (14 min).mp4	16.9 MB
18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).srt	31.2 KB
docs_slides_Lecture18.pdf	2.1 MB
docs_slides_Lecture18.pptx	6.4 MB
Total files 274