ML Learning Materials

Machine Learning Books

ML Fundamentals

1. Probabilistic Machine Learning (Murphy, 2012-2023)
2. Deep Learning (Goodfellow, 2016)
3. Pattern Recognition and Machine Learning (Bishop, 2006)
4. Deep Learning: Foundations and Concepts (Bishop & Bishop, 2023)
5. Mathematics for Machine Learning (Deisenroth, 2020)
6. An Introduction to Statistical Learning (Tibshirani, 2023)
7. The Elements of Statistical Learning (Tibshirani, 2009)
8. Linear Algebra and Learning from Data (Strang, 2019)
9. The Little Book of Deep Learning (Fleuret, 2023)

ML Practice

1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition (Géron, 2022)
2. Designing Machine Learning Systems (Huyen, 2022)
3. Fundamentals of Data Engineering (Reis, 20222)
4. Dive into Deep Learning (Zhang, 2023)
5. Reliable Machine Learning (Chen, 2022)

LLM & Gen AI

1. Natural Language Processing with Transformers (Tunstall, 2022)
2. Hands-On Large Language Models (Alammar, 2024)
3. Generative Deep Learning (Foster, 2023)
4. Hands-On Generative AI with Transformers and Diffusion Models (Cuenca, 2024)

Time Series

1. Forecasting: Principles and Practice (Hyndman, 2021)

Recommender Systems

1. Recommender Systems Handbook (2022)
2. Collaborative Recommendations: Algorithms, Practical Challenges and Applications (Berkovsky, 2019)
3. Practical Recommender Systems (Falk, 2019)
4. Personalized Machine Learning (McAuley, 2022)
5. Industrial Recommender System (Hu, 2024)

A/B Testing

1. Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing (Kohavi, 2020)