Trust an actual AI and ML engineer who is working in field for 8+ years and read 20+ books

Seven non-negotiable books for AI/ML engineers. The seventh one being the most important. First, Mathematics of Machine Learning. It helps you understand the linear algebra and calculus behind machine learning in a more intuitive way. Second, Practical Statistics for Data Scientists. People underestimate how useful statistics is for both ML and AI engineering. This book teaches you AB testing, experimentation, and how to reason properly about data. Third, Machine Learning with PyTorch and Scikit-Learn. It takes you from classical machine learning to deep learning and helps you understand how models are built and evaluated. Fourth, Hands-On Large Language Models. This book is enough to build a strong understanding of embeddings, Transformers, semantic search, and LLMs. Later, you can even read specific papers for individual models and techniques. Fifth, Designing Machine Learning Systems. This forms a strong foundation in MLOps and also gives you intuition understanding LLMs. Sixth, AI Engineering. If you are a beginner, it is one of the best starting points for understanding rag, evaluation, model selection, latency, and production AI applications. Finally, Building Generative AI Services with FastAPI. Today, you cannot stay in a Jupyter notebook. You need to build real systems and this book shows you how AI applications become real services and APIs, assistance, Docker, and web bases. Today, you cannot stay in a Jupyter notebook. You need to build real systems and this book shows you how AI applications become real services and APIs, assistance, Docker, and web bases.