| Coursera-ML/avatar.png |
55 KB |
| Coursera-ML/I. Introduction (Week 1)/1 - 1 - Welcome (7 min).mp4 |
12 MB |
| Coursera-ML/I. Introduction (Week 1)/1 - 1 - Welcome (7 min).srt |
10 KB |
| Coursera-ML/I. Introduction (Week 1)/1 - 2 - What is Machine Learning (7 min).mp4 |
9.4 MB |
| Coursera-ML/I. Introduction (Week 1)/1 - 2 - What is Machine Learning- (7 min).srt |
10 KB |
| Coursera-ML/I. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).mp4 |
13.5 MB |
| Coursera-ML/I. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).srt |
17 KB |
| Coursera-ML/I. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).mp4 |
16.7 MB |
| Coursera-ML/I. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).srt |
29 KB |
| Coursera-ML/I. Introduction (Week 1)/docs_slides_Lecture1.pdf |
3.3 MB |
| Coursera-ML/I. Introduction (Week 1)/docs_slides_Lecture1.pptx |
4 MB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 1 - Model Representation (8 min).mp4 |
9 MB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 1 - Model Representation (8 min).srt |
10 KB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).mp4 |
9 MB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).srt |
10 KB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).mp4 |
12.2 MB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).srt |
12 KB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 4 - Cost Function - Intuition II (9 min).mp4 |
11.4 MB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 4 - Cost Function - Intuition II (9 min).srt |
11 KB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).mp4 |
13.5 MB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).srt |
15 KB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).mp4 |
13 MB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).srt |
15 KB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).mp4 |
12.2 MB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).srt |
19 KB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 8 - What's Next (6 min).srt |
8 KB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/2 - 8 - Whats Next (6 min).mp4 |
6.1 MB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/docs_slides_Lecture2.pdf |
2.9 MB |
| Coursera-ML/II. Linear Regression with One Variable (Week 1)/docs_slides_Lecture2.pptx |
5.4 MB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).mp4 |
9.6 MB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).srt |
16 KB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).txt |
7 KB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).mp4 |
7.5 MB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).srt |
12 KB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).mp4 |
15 MB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).srt |
24 KB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).mp4 |
12.6 MB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).srt |
21 KB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).mp4 |
9.8 MB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).srt |
17 KB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).mp4 |
12.9 MB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).srt |
21 KB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/docs_slides_Lecture3.pdf |
1.8 MB |
| Coursera-ML/III. Linear Algebra Review (Week 1, Optional)/docs_slides_Lecture3.pptx |
4.9 MB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).mp4 |
8.8 MB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).srt |
15 KB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4 |
5.8 MB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).srt |
7 KB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4 |
9.5 MB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).srt |
17 KB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4 |
9.3 MB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).srt |
18 KB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).mp4 |
8.3 MB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).srt |
16 KB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).mp4 |
17.1 MB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).srt |
31 KB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4 |
6.2 MB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).srt |
10 KB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/docs_slides_Lecture4.pdf |
1.7 MB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/docs_slides_Lecture4.pptx |
4.4 MB |
| Coursera-ML/IV. Linear Regression with Multiple Variables (Week 2)/ex1.zip |
470 KB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).mp4 |
7.7 MB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).srt |
13 KB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).mp4 |
13.9 MB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).srt |
23 KB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).mp4 |
15.4 MB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).srt |
25 KB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note Unrolling Parameters (8 min).mp4 |
9.4 MB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note- Unrolling Parameters (8 min).srt |
15 KB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).mp4 |
13.5 MB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).srt |
24 KB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).mp4 |
7.6 MB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).srt |
14 KB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).mp4 |
16.3 MB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).srt |
28 KB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).mp4 |
14.9 MB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).srt |
10 KB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/docs_slides_Lecture9.pdf |
3.4 MB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/docs_slides_Lecture9.pptx |
5 MB |
| Coursera-ML/IX. Neural Networks Learning (Week 5)/ex4.zip |
7.6 MB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).mp4 |
17.7 MB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).srt |
25 KB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).mp4 |
20.8 MB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).srt |
29 KB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).mp4 |
15.3 MB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).srt |
25 KB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).mp4 |
13.3 MB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).srt |
17 KB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 5 - Control Statements for while if statements (13 min).mp4 |
16.5 MB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 5 - Control Statements- for, while, if statements (13 min).srt |
23 KB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).mp4 |
16.1 MB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).srt |
25 KB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4 |
5.5 MB |
| Coursera-ML/V. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).srt |
4 KB |
| Coursera-ML/V. Octave Tutorial (Week 2)/docs_slides_Lecture5.pdf |
242 KB |
| Coursera-ML/V. Octave Tutorial (Week 2)/docs_slides_Lecture5.pptx |
407 KB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).mp4 |
8.8 MB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).srt |
16 KB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).mp4 |
8.3 MB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).srt |
14 KB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).mp4 |
16.7 MB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).srt |
27 KB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).mp4 |
13.1 MB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).srt |
22 KB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4 |
12 MB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).srt |
20 KB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).mp4 |
18.2 MB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).srt |
28 KB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification One-vs-all (6 min).mp4 |
6.9 MB |
| Coursera-ML/VI. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification- One-vs-all (6 min).srt |
13 KB |
| Coursera-ML/VI. Logistic Regression (Week 3)/docs_slides_Lecture6.pdf |
2.1 MB |
| Coursera-ML/VI. Logistic Regression (Week 3)/docs_slides_Lecture6.pptx |
3.8 MB |
| Coursera-ML/VII. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).mp4 |
11.1 MB |
| Coursera-ML/VII. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).srt |
19 KB |
| Coursera-ML/VII. Regularization (Week 3)/7 - 2 - Cost Function (10 min).mp4 |
11.6 MB |
| Coursera-ML/VII. Regularization (Week 3)/7 - 2 - Cost Function (10 min).srt |
20 KB |
| Coursera-ML/VII. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).mp4 |
12 MB |
| Coursera-ML/VII. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).srt |
20 KB |
| Coursera-ML/VII. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).mp4 |
10.9 MB |
| Coursera-ML/VII. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).srt |
17 KB |
| Coursera-ML/VII. Regularization (Week 3)/docs_slides_Lecture7.pdf |
2.3 MB |
| Coursera-ML/VII. Regularization (Week 3)/docs_slides_Lecture7.pptx |
2.6 MB |
| Coursera-ML/VII. Regularization (Week 3)/ex2.zip |
243 KB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).mp4 |
10.9 MB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).srt |
19 KB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).mp4 |
9.9 MB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).srt |
16 KB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).mp4 |
13.5 MB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).srt |
22 KB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).mp4 |
13.5 MB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).srt |
22 KB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).mp4 |
7.9 MB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).srt |
13 KB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).mp4 |
14 MB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).srt |
17 KB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).mp4 |
4.8 MB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).srt |
7 KB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/docs_slides_Lecture8.pdf |
5 MB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/docs_slides_Lecture8.pptx |
40.4 MB |
| Coursera-ML/VIII. Neural Networks Representation (Week 4)/ex3.zip |
7.5 MB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 1 - Deciding What to Try Next (6 min).mp4 |
6.9 MB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 1 - Deciding What to Try Next (6 min).srt |
12 KB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).mp4 |
8.5 MB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).srt |
11 KB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mp4 |
14.1 MB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).srt |
25 KB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4 |
9 MB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 4 - Diagnosing Bias vs. Variance (8 min).srt |
16 KB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias_Variance (11 min).mp4 |
12.6 MB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias_Variance (11 min).srt |
23 KB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).mp4 |
12.9 MB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).srt |
25 KB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 7 - Deciding What to Do Next Revisited (7 min).mp4 |
8.2 MB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/10 - 7 - Deciding What to Do Next Revisited (7 min).srt |
14 KB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/docs_slides_Lecture10.pdf |
1.5 MB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/docs_slides_Lecture10.pptx |
3.4 MB |
| Coursera-ML/X. Advice for Applying Machine Learning (Week 6)/ex5.zip |
177 KB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/11 - 1 - Prioritizing What to Work On (10 min).mp4 |
11.2 MB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/11 - 1 - Prioritizing What to Work On (10 min).srt |
20 KB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).mp4 |
15.4 MB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).srt |
27 KB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).mp4 |
13.3 MB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).srt |
22 KB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).mp4 |
16 MB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).srt |
29 KB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).mp4 |
12.9 MB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).srt |
23 KB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/docs_slides_Lecture11.pdf |
498 KB |
| Coursera-ML/XI. Machine Learning System Design (Week 6)/docs_slides_Lecture11.pptx |
1.9 MB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).mp4 |
16.7 MB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).srt |
29 KB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).mp4 |
11.8 MB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).srt |
21 KB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4 |
21.8 MB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).srt |
36 KB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).mp4 |
17.6 MB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).srt |
29 KB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min) (1).mp4 |
17.4 MB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min) (1).srt |
31 KB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).mp4 |
17.4 MB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).srt |
31 KB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).mp4 |
24 MB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).srt |
44 KB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/docs_slides_Lecture12.pdf |
2.3 MB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/docs_slides_Lecture12.pptx |
5.4 MB |
| Coursera-ML/XII. Support Vector Machines (Week 7)/ex6.zip |
896 KB |
| Coursera-ML/XIII. Clustering (Week 8)/13 - 1 - Unsupervised Learning Introduction (3 min).mp4 |
3.8 MB |
| Coursera-ML/XIII. Clustering (Week 8)/13 - 1 - Unsupervised Learning- Introduction (3 min).srt |
7 KB |
| Coursera-ML/XIII. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).mp4 |
13.8 MB |
| Coursera-ML/XIII. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).srt |
26 KB |
| Coursera-ML/XIII. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).mp4 |
8.1 MB |
| Coursera-ML/XIII. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).srt |
14 KB |
| Coursera-ML/XIII. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).mp4 |
8.7 MB |
| Coursera-ML/XIII. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).srt |
16 KB |
| Coursera-ML/XIII. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).mp4 |
9.4 MB |
| Coursera-ML/XIII. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).srt |
18 KB |
| Coursera-ML/XIII. Clustering (Week 8)/docs_slides_Lecture13.pdf |
2.2 MB |
| Coursera-ML/XIII. Clustering (Week 8)/docs_slides_Lecture13.pptx |
2.8 MB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I Data Compression (10 min).mp4 |
14.3 MB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I- Data Compression (10 min).srt |
20 KB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II Visualization (6 min).mp4 |
6.3 MB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II- Visualization (6 min).srt |
10 KB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4 |
10.5 MB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).srt |
18 KB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).mp4 |
17.8 MB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).srt |
29 KB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).mp4 |
11.8 MB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).srt |
21 KB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).mp4 |
5 MB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).srt |
8 KB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).mp4 |
14.7 MB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).srt |
26 KB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/docs_slides_Lecture14.pdf |
1.6 MB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/docs_slides_Lecture14.pptx |
3.6 MB |
| Coursera-ML/XIV. Dimensionality Reduction (Week 8)/ex7.zip |
11 MB |
| Coursera-ML/XIX. Conclusion/19 - 1 - Summary and Thank You (5 min).mp4 |
6.1 MB |
| Coursera-ML/XIX. Conclusion/19 - 1 - Summary and Thank You (5 min).srt |
8 KB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).mp4 |
8.3 MB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).srt |
16 KB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).mp4 |
11.7 MB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).srt |
21 KB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).mp4 |
14 MB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).srt |
23 KB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4 |
15.2 MB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).srt |
27 KB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4 |
9.3 MB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).srt |
16 KB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).mp4 |
14.1 MB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).srt |
25 KB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4 |
15.9 MB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).srt |
27 KB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4 |
16.3 MB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).srt |
26 KB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/docs_slides_Lecture15.pdf |
3.3 MB |
| Coursera-ML/XV. Anomaly Detection (Week 9)/docs_slides_Lecture15.pptx |
6 MB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).mp4 |
10.7 MB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).srt |
17 KB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).mp4 |
16.9 MB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).srt |
29 KB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).mp4 |
11.8 MB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).srt |
20 KB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).mp4 |
10.3 MB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).srt |
16 KB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 5 - Vectorization Low Rank Matrix Factorization (8 min).mp4 |
9.7 MB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).srt |
16 KB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 6 - Implementational Detail Mean Normalization (9 min).mp4 |
9.7 MB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/16 - 6 - Implementational Detail- Mean Normalization (9 min).srt |
17 KB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/docs_slides_Lecture16.pdf |
1.4 MB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/docs_slides_Lecture16.pptx |
3.6 MB |
| Coursera-ML/XVI. Recommender Systems (Week 9)/ex8.zip |
795 KB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).mp4 |
6.5 MB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).srt |
8 KB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).mp4 |
15.3 MB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).srt |
18 KB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).mp4 |
7.3 MB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).srt |
8 KB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4 |
13.3 MB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).srt |
16 KB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).mp4 |
14.9 MB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).srt |
28 KB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).mp4 |
16.1 MB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).srt |
29 KB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/docs_slides_Lecture17.pdf |
2 MB |
| Coursera-ML/XVII. Large Scale Machine Learning (Week 10)/docs_slides_Lecture17.pptx |
3.8 MB |
| Coursera-ML/XVIII. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).mp4 |
7.9 MB |
| Coursera-ML/XVIII. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).srt |
15 KB |
| Coursera-ML/XVIII. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).mp4 |
16.5 MB |
| Coursera-ML/XVIII. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).srt |
31 KB |
| Coursera-ML/XVIII. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4 |
18.8 MB |
| Coursera-ML/XVIII. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).srt |
35 KB |
| Coursera-ML/XVIII. Application Example Photo OCR/18 - 4 - Ceiling Analysis What Part of the Pipeline to Work on Next (14 min).mp4 |
16.1 MB |
| Coursera-ML/XVIII. Application Example Photo OCR/18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).srt |
31 KB |
| Coursera-ML/XVIII. Application Example Photo OCR/docs_slides_Lecture18.pdf |
2 MB |
| Coursera-ML/XVIII. Application Example Photo OCR/docs_slides_Lecture18.pptx |
6.1 MB |
Magnet link
