Machine Learning Specialization by Standford

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Course 1: Supervised Learning: Regression and Classification

• Topics Covered: Classification & Regression machine learning problems, over & under fitting, gradient descent, MSE, MAE and logistic loss functions, Regularization, Polynomial Regression & Feature Maps
• Projects & Code: Here
• Course Notes: Here

Course 2: Advanced Learning Algorithms

• Topics Covered: Neural Networks (ANNs), forward and backward propagation (Training ANNs), Activation functions, and Decision Trees, Multi-Class Classification, Xgboost
• Projects & Code: Here
• Course Notes: Here

Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning

• Topics Covered: Unsupervised Learning Algorithms, Dimensionality Reduction with PCA, K-Means Clustering, Anomaly Detection, Building Recommendation Systems, Deep Reinforcement Learning Models
• Projects & Code: Here
• Course Notes: Here

Additional Resources

1. Introduction to Machine Learning by Stanford University
2. Pattern Recognition and Machine Learning by Bishop
3. The Elements of Statistical Learning (ESL) by Hastie, Tibshirani, and Friedman
4. Information Theory, Inference, and Learning Algorithms by MacKay
5. Bayesian Reasoning and Machine Learning by Barber
6. Reinforcement Learning: An Introduction by Sutton and Barto
7. Math for ML by Deisenroth, Faisal, and Ong
8. Understanding Machine Learning: From Theory to Algorithms by Shalev-Shwartz and Ben-David
9. Machine Learning: a Probabilistic Perspective by Murphy