An Introduction to Statistical Learning: With Applications in R

The authors Hastie and Tibshirani are legends in the stats world, creating GAM and LASSO respectively. Their other textbook "The Elements of Statistical Learning" is geared for PhD students. This textbook is very accessible, with figures and lots of sample code. The target audience is any aspiring data scientist who can learn to code and wants to actually understand what the code/models are doing (but doesn't need to be able to derive all the original math by hand). In addition to teaching different analyses, this book does a great job on explaining key statistical analysis concepts, like bias vs variance tradeoff, k-fold cross-validation, bootstrapping, finding the right balance in model complexity for your dataset, etc. There is both an R and a Python edition. The 2nd edition includes 3 new chapters on survival analysis, multiple testing, and neural nets. There is a free Stanford MOOC that uses this text.

Excelente livro introdutório para o aprendizado estatístico. Redefine muito bem conceitos estatísticos tradicionais sob um olhar mais atual no contexto do aprendizado de máquina. Exemplos na linguagem R, o que é ótimo para os alunos "meterem a mão na massa". Uma referência essencial para estatísticos e cientistas de dados.

I like a lot the decision tree with codes

I reviewed this book for a class in my master's program and I loved it from start to end.I already knew most of the concepts but became hooked because of how clear the explanations are. The authors convey complex ideas with remarkable simplicity, and for that, I think this is the most important book for data scientists.I am an avid opposer of the R programming language (ew) and even I enjoyed the applied programming parts of the book.In all honesty, the applications in R are very good, but it's not the main focus of the book. I think people should read this to understand the inner workings of the most popular AI algorithms instead of learning how to train predictive models (especially in R, haha).Overall, I think this is a great book for beginners and veterans alike. I would not hesitate to recommend this book to anyone interested in statistics, data and AI.

the book is best for intermediate people and for people who want to learn in depth the mathematics of machine learning