Machine Learning
Available resources online.
- Pattern Recognition and Machine Learning. Christopher Bishop.
- Statistics and Machine Learning in Python. Edouard Duchesnay, Tommy Löfstedt, Feki Younes.
- Machine and Deep Learning Compendium. Ori Cohen.
- Neural Networks and Deep Learning. Michael Nielsen.
- Deep Learning. I. Goodfellow, Y. Bengio and A. Courville.
- Papers with Code: free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables.
- Mathematics for Machine Learning. Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.
- Probabilistic Machine Learning: An Introduction. Kevin P. Murphy.
- Patterns, Predictions and Actions. A history about Machine Learning. Morris Hardt and Benjamin Recht.
- The Elements of Statistical Learning. Data Mining, Inference and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman.
- Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow 2. Sebastian Raschka and Vahid Mirkalili.
- Machine Learning from Scratch. Derivations in Concepts and Code. Danny Friedman.
- Natural Language Processing with Python. Steven Bird, Ewan Klein, and Edward Loper.
- Dive into Deep Learning. Interactive deep learning book with code, math, and discussions. Various authors.
- Introduction to Machine Learning with Python. Andreas Muller and Sarah Guido.
- Hands-on in Machine Learning with Scikit-Learn, Keras & Tensorflow (ML Notebooks). Aurélien Géron.