SOURCE: Andrew Ng’s Machine Learning MOOC on Coursera
CODE: Python 3.6
Link: https://www.coursera.org/learn/machine-learning

• 1. Introduction
  • TOPIC: Linear Regression with One Variable | Model and Cost Function | Parameter Learning
  • LINEAR ALGEBRA REVIEW: LinearAlgebra_NumPy.py
• 2. Linear Regression with Multiple Variables
  • TOPIC: Multivariate Linear Regression | Computing Parameters Analytically
  • PYTHON TUTORIAL: BasicOperations.py | MovingDataAround.py | ComputingOnData.py | PlottingData.py | CostFunction.py
  • PROGRAMMING EXERCISE 1: Linear Regression
• 3. Classification: Logistic Regression
  • TOPIC: Classification and Representation | Logistic Regression Model | Multiclass Classification | Regularization
  • PROGRAMMING EXERCISE 2: Logistic Regression
• 4. Neural Networks
  • TOPIC: Neural Networks Hypotheses Representation | Cost Function and Backpropagation
  • PROGRAMMING EXERCISE 3: Multi-class Classification and Neural Networks'
  • PROGRAMMING EXERCISE 4: Neural Networks Learning
• 5. Advice for Applying Machine Learning
  • TOPIC: Model Selection Train/Validation/Test Sets | Diagnosing Bias vs. Variance | Error analysis | Handling Skewed Data (Precision and Recall)
  • PROGRAMMING EXERCISE 5: Regularized Linear Regression and Bias v.s. Variance
• 6. Support Vector Machines
  • TOPIC: Large Margin Classification | Kernels | SVMs in Practice
  • PROGRAMMING EXERCISE 6: Support Vector Machines
• 7. Unsupervised Learning and Dimensionality Reduction
  • TOPIC: Unsupervised learning introduction | K-means algorithm | K-means for non-separated clusters | Optimization objective | Random initialization | Choosing the number of clusters | Dimensionality Reduction | Principal Component Anlysis algorithm | Data preprocessing | Reconstruction from compressed representation | Choosing the number of principal components | Advice for applying PCA
  • PROGRAMMING EXERCISE 7: K-means Clustering and Principal Component Analysis
• 8. Anomaly Detection and Recommender Systems
  • TOPIC: Gaussian distribution | Parameter estimation | Density estimation | Anomaly detection algorithm | Developing and evaluating an anomaly detection system | Anomaly detection vs. supervised learning | Choosing what features to use | Multivariate Gaussian distribution | Content-based recommendations | Collaborative filtering Algorithm | Vectorization: Low rank matrix factorization | Mean Normalization
  • PROGRAMMING EXERCISE 8: Anomaly Detection and Recommender Systems
• 9. Large scale machine learning
  • TOPIC: Stochastic gradient descent | Mini-batch gradient descent | Online learning | Map-reduce and data parallelism
• 10. Application example: Photo OCR
  • TOPIC: Machine Learning pipelines | Sliding windows | Artificial data synthesis | Ceiling Analysis