| 08. Neural Networks Representation (Week 4)/docs-slides-Lecture8.pptx | 40.36 MB |
| 12. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).mp4 | 23.95 MB |
| 12. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4 | 21.83 MB |
| 05. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).mp4 | 20.77 MB |
| 18. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4 | 18.82 MB |
| 06. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).mp4 | 18.15 MB |
| 14. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).mp4 | 17.79 MB |
| 05. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).mp4 | 17.72 MB |
| 12. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).mp4 | 17.57 MB |
| 12. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).mp4 | 17.45 MB |
| 04. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).mp4 | 17.13 MB |
| 16. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).mp4 | 16.93 MB |
| 06. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).mp4 | 16.74 MB |
| 01. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).mp4 | 16.66 MB |
| 12. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).mp4 | 16.65 MB |
| 18. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).mp4 | 16.52 MB |
| 05. Octave Tutorial (Week 2)/5 - 5 - Control Statements- for, while, if statements (13 min).mp4 | 16.49 MB |
| 15. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4 | 16.34 MB |
| 09. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).mp4 | 16.3 MB |
| 18. Application Example Photo OCR/18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).mp4 | 16.11 MB |
| 05. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).mp4 | 16.09 MB |
| 17. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).mp4 | 16.06 MB |
| 11. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).mp4 | 15.99 MB |
| 15. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4 | 15.93 MB |
| 09. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).mp4 | 15.44 MB |
| 11. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).mp4 | 15.43 MB |
| 17. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).mp4 | 15.33 MB |
| 05. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).mp4 | 15.25 MB |
| 15. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4 | 15.15 MB |
| 03. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).mp4 | 15 MB |
| 17. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).mp4 | 14.91 MB |
| 09. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).mp4 | 14.88 MB |
| 14. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).mp4 | 14.7 MB |
| 14. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I- Data Compression (10 min).mp4 | 14.31 MB |
| 15. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).mp4 | 14.12 MB |
| 10. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train-Validation-Test Sets (12 min).mp4 | 14.07 MB |
| 08. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).mp4 | 14 MB |
| 15. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).mp4 | 13.95 MB |
| 09. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).mp4 | 13.94 MB |
| 13. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).mp4 | 13.81 MB |
| 08. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).mp4 | 13.51 MB |
| 02. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).mp4 | 13.5 MB |
| 09. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).mp4 | 13.5 MB |
| 01. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).mp4 | 13.45 MB |
| 08. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).mp4 | 13.45 MB |
| 17. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4 | 13.33 MB |
| 05. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).mp4 | 13.32 MB |
| 11. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).mp4 | 13.25 MB |
| 06. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).mp4 | 13.09 MB |
| 02. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).mp4 | 13.03 MB |