Coursera Machine Learning by Andrew Ng | 48D1F81A7493A4B5440B09796F76B89EE160419F

Coursera Machine Learning by Andrew Ng

Name	[Coursera] Machine Learning by Andrew Ng	DOWNLOAD Copy Link
Total Size	1.6 GB
Total Files	185
Last Seen	2024-11-15 01:25
Hash	48D1F81A7493A4B5440B09796F76B89EE160419F

/01. Introduction (Week 1)/
1 - 1 - Welcome (7 min).mp4	12.5 MB
1 - 2 - What is Machine Learning- (7 min).mp4	9.8 MB
1 - 3 - Supervised Learning (12 min).mp4	14.1 MB
1 - 4 - Unsupervised Learning (14 min).mp4	17.5 MB
docs-slides-Lecture1.pdf	3.5 MB
docs-slides-Lecture1.pptx	4.2 MB
/02. Linear Regression with One Variable (Week 1)/
2 - 1 - Model Representation (8 min).mp4	9.4 MB
2 - 2 - Cost Function (8 min).mp4	9.5 MB
2 - 3 - Cost Function - Intuition I (11 min).mp4	12.8 MB
2 - 4 - Cost Function - Intuition II (9 min).mp4	11.9 MB
2 - 5 - Gradient Descent (11 min).mp4	14.2 MB
2 - 6 - Gradient Descent Intuition (12 min).mp4	13.7 MB
2 - 7 - Gradient Descent For Linear Regression (10 min).mp4	12.8 MB
2 - 8 - What-'s Next (6 min).mp4	6.4 MB
docs-slides-Lecture2.pdf	3.0 MB
docs-slides-Lecture2.pptx	5.6 MB
/03. Linear Algebra Review (Week 1, Optional)/
3 - 1 - Matrices and Vectors (9 min).mp4	10.0 MB
3 - 2 - Addition and Scalar Multiplication (7 min).mp4	7.8 MB
3 - 3 - Matrix Vector Multiplication (14 min).mp4	15.7 MB
3 - 4 - Matrix Matrix Multiplication (11 min).mp4	13.2 MB
3 - 5 - Matrix Multiplication Properties (9 min).mp4	10.3 MB
3 - 6 - Inverse and Transpose (11 min).mp4	13.5 MB
docs-slides-Lecture3.pdf	1.9 MB
docs-slides-Lecture3.pptx	5.2 MB
/04. Linear Regression with Multiple Variables (Week 2)/
4 - 1 - Multiple Features (8 min).mp4	9.3 MB
4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4	6.1 MB
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4	9.9 MB
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4	9.7 MB
4 - 5 - Features and Polynomial Regression (8 min).mp4	8.7 MB
4 - 6 - Normal Equation (16 min).mp4	18.0 MB
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4	6.5 MB
docs-slides-Lecture4.pdf	1.8 MB
docs-slides-Lecture4.pptx	4.6 MB
/05. Octave Tutorial (Week 2)/
5 - 1 - Basic Operations (14 min).mp4	18.6 MB
5 - 2 - Moving Data Around (16 min).mp4	21.8 MB
5 - 3 - Computing on Data (13 min).mp4	16.0 MB
5 - 4 - Plotting Data (10 min).mp4	14.0 MB
5 - 5 - Control Statements- for, while, if statements (13 min).mp4	17.3 MB
5 - 6 - Vectorization (14 min).mp4	16.9 MB
5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4	5.7 MB
docs-slides-Lecture5.pdf	248.2 KB
docs-slides-Lecture5.pptx	417.1 KB
/06. Logistic Regression (Week 3)/
6 - 1 - Classification (8 min).mp4	9.2 MB
6 - 2 - Hypothesis Representation (7 min).mp4	8.7 MB
6 - 3 - Decision Boundary (15 min).mp4	17.6 MB
6 - 4 - Cost Function (11 min).mp4	13.7 MB
6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4	12.5 MB
6 - 6 - Advanced Optimization (14 min).mp4	19.0 MB
6 - 7 - Multiclass Classification- One-vs-all (6 min).mp4	7.3 MB
docs-slides-Lecture6.pdf	2.2 MB
docs-slides-Lecture6.pptx	4.0 MB
/07. Regularization (Week 3)/
7 - 1 - The Problem of Overfitting (10 min).mp4	11.7 MB
7 - 2 - Cost Function (10 min).mp4	12.2 MB
7 - 3 - Regularized Linear Regression (11 min).mp4	12.6 MB
7 - 4 - Regularized Logistic Regression (9 min).mp4	11.4 MB
docs-slides-Lecture7.pdf	2.5 MB
docs-slides-Lecture7.pptx	2.7 MB
/08. Neural Networks Representation (Week 4)/
8 - 1 - Non-linear Hypotheses (10 min).mp4	11.4 MB
8 - 2 - Neurons and the Brain (8 min).mp4	10.4 MB
8 - 3 - Model Representation I (12 min).mp4	14.2 MB
8 - 4 - Model Representation II (12 min).mp4	14.1 MB
8 - 5 - Examples and Intuitions I (7 min).mp4	8.3 MB
8 - 6 - Examples and Intuitions II (10 min).mp4	14.7 MB
8 - 7 - Multiclass Classification (4 min).mp4	5.1 MB
docs-slides-Lecture8.pdf	5.2 MB
docs-slides-Lecture8.pptx	42.3 MB
/09. Neural Networks Learning (Week 5)/
9 - 1 - Cost Function (7 min).mp4	8.0 MB
9 - 2 - Backpropagation Algorithm (12 min).mp4	14.6 MB
9 - 3 - Backpropagation Intuition (13 min).mp4	16.2 MB
9 - 4 - Implementation Note- Unrolling Parameters (8 min).mp4	9.8 MB
9 - 5 - Gradient Checking (12 min).mp4	14.2 MB
9 - 6 - Random Initialization (7 min).mp4	7.9 MB
9 - 7 - Putting It Together (14 min).mp4	17.1 MB
9 - 8 - Autonomous Driving (7 min).mp4	15.6 MB
docs-slides-Lecture9.pdf	3.5 MB
docs-slides-Lecture9.pptx	5.2 MB
/10. Advice for Applying Machine Learning (Week 6)/
10 - 1 - Deciding What to Try Next (6 min).mp4	7.2 MB
10 - 2 - Evaluating a Hypothesis (8 min).mp4	8.9 MB
10 - 3 - Model Selection and Train-Validation-Test Sets (12 min).mp4	14.8 MB
10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4	9.4 MB
10 - 5 - Regularization and Bias-Variance (11 min).mp4	13.2 MB
10 - 6 - Learning Curves (12 min).mp4	13.5 MB
10 - 7 - Deciding What to Do Next Revisited (7 min).mp4	8.6 MB
docs-slides-Lecture10.pdf	1.6 MB
docs-slides-Lecture10.pptx	3.5 MB
/11. Machine Learning System Design (Week 6)/
11 - 1 - Prioritizing What to Work On (10 min).mp4	11.7 MB
11 - 2 - Error Analysis (13 min).mp4	16.2 MB
11 - 3 - Error Metrics for Skewed Classes (12 min).mp4	13.9 MB
11 - 4 - Trading Off Precision and Recall (14 min).mp4	16.8 MB
11 - 5 - Data For Machine Learning (11 min).mp4	13.5 MB
docs-slides-Lecture11.pdf	509.6 KB
docs-slides-Lecture11.pptx	2.0 MB
/12. Support Vector Machines (Week 7)/
12 - 1 - Optimization Objective (15 min).mp4	17.5 MB
12 - 2 - Large Margin Intuition (11 min).mp4	12.4 MB
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4	22.9 MB
12 - 4 - Kernels I (16 min).mp4	18.4 MB
12 - 5 - Kernels II (16 min).mp4	18.3 MB
12 - 6 - Using An SVM (21 min).mp4	25.1 MB
docs-slides-Lecture12.pdf	2.4 MB
docs-slides-Lecture12.pptx	5.6 MB
/13. Clustering (Week 8)/
13 - 1 - Unsupervised Learning- Introduction (3 min).mp4	4.0 MB
13 - 2 - K-Means Algorithm (13 min).mp4	14.5 MB
13 - 3 - Optimization Objective (7 min).mp4	8.5 MB
13 - 4 - Random Initialization (8 min).mp4	9.1 MB
13 - 5 - Choosing the Number of Clusters (8 min).mp4	9.9 MB
docs-slides-Lecture13.pdf	2.3 MB
docs-slides-Lecture13.pptx	2.9 MB
/14. Dimensionality Reduction (Week 8)/
14 - 1 - Motivation I- Data Compression (10 min).mp4	15.0 MB
14 - 2 - Motivation II- Visualization (6 min).mp4	6.6 MB
14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4	11.0 MB
14 - 4 - Principal Component Analysis Algorithm (15 min).mp4	18.7 MB
14 - 5 - Choosing the Number of Principal Components (11 min).mp4	12.4 MB
14 - 6 - Reconstruction from Compressed Representation (4 min).mp4	5.2 MB
14 - 7 - Advice for Applying PCA (13 min).mp4	15.4 MB
docs-slides-Lecture14.pdf	1.7 MB
docs-slides-Lecture14.pptx	3.8 MB
/15. Anomaly Detection (Week 9)/
15 - 1 - Problem Motivation (8 min).mp4	8.8 MB
15 - 2 - Gaussian Distribution (10 min).mp4	12.3 MB
15 - 3 - Algorithm (12 min).mp4	14.6 MB
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4	15.9 MB
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4	9.7 MB
15 - 6 - Choosing What Features to Use (12 min).mp4	14.8 MB
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4	16.7 MB
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4	17.1 MB
docs-slides-Lecture15.pdf	3.5 MB
docs-slides-Lecture15.pptx	6.3 MB
/16. Recommender Systems (Week 9)/
16 - 1 - Problem Formulation (8 min).mp4	11.2 MB
16 - 2 - Content Based Recommendations (15 min).mp4	17.8 MB
16 - 3 - Collaborative Filtering (10 min).mp4	12.3 MB
16 - 4 - Collaborative Filtering Algorithm (9 min).mp4	10.8 MB
16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).mp4	10.2 MB
16 - 6 - Implementational Detail- Mean Normalization (9 min).mp4	10.2 MB
docs-slides-Lecture16.pdf	1.5 MB
docs-slides-Lecture16.pptx	3.8 MB
/17. Large Scale Machine Learning (Week 10)/
17 - 1 - Learning With Large Datasets (6 min).mp4	6.8 MB
17 - 2 - Stochastic Gradient Descent (13 min).mp4	16.1 MB
17 - 3 - Mini-Batch Gradient Descent (6 min).mp4	7.7 MB
17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4	14.0 MB
17 - 5 - Online Learning (13 min).mp4	15.6 MB
17 - 6 - Map Reduce and Data Parallelism (14 min).mp4	16.8 MB
docs-slides-Lecture17.pdf	2.1 MB
docs-slides-Lecture17.pptx	4.0 MB
/18. Application Example Photo OCR/
18 - 1 - Problem Description and Pipeline (7 min).mp4	8.3 MB
18 - 2 - Sliding Windows (15 min).mp4	17.3 MB
18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4	19.7 MB
18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).mp4	16.9 MB
docs-slides-Lecture18.pdf	2.1 MB
docs-slides-Lecture18.pptx	6.4 MB
/19. Conclusion/
19 - 1 - Summary and Thank You (5 min).mp4	6.4 MB
/
_ Coursera.pdf	217.0 KB
small-icon.hover.png	26.2 KB
Wiki - Course FAQ _ Coursera.pdf	100.4 KB
Wiki - Course Information _ Coursera.pdf	83.5 KB
Wiki - Course Schedule _ Coursera.pdf	72.1 KB
Wiki - Octave __ Matlab Tutorial _ Coursera.pdf	907.4 KB
Wiki - Tutoring _ Coursera.pdf	2.4 MB
/Homeworks/
01. Introduction.pdf	93.2 KB
02. Linear regression with one variable.pdf	619.4 KB
03. Linear Algebra.pdf	657.9 KB
04. Linear Regression with Multiple Variables.pdf	579.1 KB
05. Octave Tutorial.pdf	660.8 KB
06. Logistic Regression.pdf	692.1 KB
07. Regularization.pdf	624.2 KB
08. Neural Networks Representation.pdf	1.1 MB
09. Neural Networks Learning.pdf	619.7 KB
10. Advice for Applying Machine Learning.pdf	295.5 KB
11. Machine Learning System Design.pdf	583.2 KB
12. Support Vector Machines.pdf	2.0 MB
13. Clustering.pdf	591.6 KB
14. Anomaly Detection.pdf	646.7 KB
15. Principal Component Analysis.pdf	1.1 MB
16. Recommender Systems.pdf	704.8 KB
17. Large Scale Machine Learning.pdf	626.1 KB
18. Application Photo OCR.pdf	700.2 KB
View Review Questions _ Coursera.pdf	150.9 KB
/Programming Assignments/
Assignment Details _ Coursera.pdf	56.9 KB
List Assignments _ Coursera.pdf	197.3 KB
mlclass-ex1-004.zip	475.4 KB
mlclass-ex2-004.zip	243.9 KB
mlclass-ex3-004.zip	7.9 MB
mlclass-ex4-004.zip	8.0 MB
mlclass-ex5-004.zip	176.7 KB
mlclass-ex6-004.zip	914.5 KB
mlclass-ex7-004.zip	11.6 MB
mlclass-ex8-004.zip	810.0 KB
Total files 185