- Deep learning (MLP, CNN, RNN, LSTM)
- Linear regression, logistic regression
- Random Forests
- Support vector machine (SVM) with kernels (Linear, Poly, RBF)
- K-Means
- Gaussian Mixture Model
- K-nearest neighbors
- Naive bayes
- Principal component analysis (PCA)
- Factorization machines
- Restricted Boltzmann machine (RBM)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Gradient Boosting trees (also known as GBDT, GBRT, GBM, XGBoost)
- Reinforcement learning (Deep Q learning)
- Real World Machine Learning [Free Chapters]
- An Introduction To Statistical Learning - Book + R Code
- Elements of Statistical Learning - Book
- Computer Age Statistical Inference (CASI) (Permalink as of October 2017) - Book
- Probabilistic Programming & Bayesian Methods for Hackers - Book + IPython Notebooks
- Think Bayes - Book + Python Code
- Information Theory, Inference, and Learning Algorithms
- Gaussian Processes for Machine Learning
- Data Intensive Text Processing w/ MapReduce
- Reinforcement Learning: - An Introduction (Permalink to Nov 2017 Draft)
- Mining Massive Datasets
- A First Encounter with Machine Learning
- Pattern Recognition and Machine Learning
- Machine Learning & Bayesian Reasoning
- Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan
- A Probabilistic Theory of Pattern Recognition
- Introduction to Information Retrieval
- Forecasting: principles and practice
- Practical Artificial Intelligence Programming in Java
- Introduction to Machine Learning - Amnon Shashua
- Reinforcement Learning
- Machine Learning
- A Quest for AI
- Introduction to Applied Bayesian Statistics and Estimation for Social Scientists - Scott M. Lynch
- Bayesian Modeling, Inference and Prediction
- A Course in Machine Learning
- Machine Learning, Neural and Statistical Classification
- Bayesian Reasoning and Machine Learning Book+MatlabToolBox
- R Programming for Data Science
- Data Mining - Practical Machine Learning Tools and Techniques Book
- Machine Learning with TensorFlow Early access book
- Reactive Machine Learning Systems Early access book
- Hands‑On Machine Learning with Scikit‑Learn and TensorFlow - Aurélien Géron
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data - Wickham and Grolemund. Great as introduction on how to use R.
- Advanced R - Hadley Wickham. More advanced usage of R for programming.
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41 Essential Machine Learning Interview Questions (with answers)
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How can a computer science graduate student prepare himself for data scientist interviews?
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What are the advantages of different classification algorithms?
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A curated list of awesome Deep Learning tutorials, projects and communities