Hastie and Tibshirani teach a free course based on this book on Stanford's OpenEdX (<a href="https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning" rel="nofollow">https://online.stanford.edu/courses/sohs-ystatslearning-stat...</a>). I highly recommend taking this course or reading the book before delving into ESL. IMO, ESL is excellent as a reference, but trying to learn by reading it linearly is not an optimal time investment.<p>Now if only a similar course existed for Wasserman's "All of Statistics..."
I'm a big fan of ISL - one of the best intro machine-learning oriented textbooks out there IMO. If you're looking for book that still offers a broad survey while going a bit deeper into the math, I recommend Elements of Statistical Learning as well (they share 2 authors):<p><a href="https://web.stanford.edu/~hastie/Papers/ESLII.pdf" rel="nofollow">https://web.stanford.edu/~hastie/Papers/ESLII.pdf</a>
The authors also published a set of very good lectures covering content in the book: <a href="https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/" rel="nofollow">https://www.r-bloggers.com/in-depth-introduction-to-machine-...</a>
I'm currently working through this book. Highly recommend, even if you have no intention on learning R. The R part is very limited, you will not learn R programming, but if you already know R, it is very useful to end each chapter with a practical demonstration of the theory.
I am trying to decide between going through a statistical learning vs. a deep learning textbook/course. Any thoughts on what would be more rewarding for someone with no immediate plans to work in ML nor do graduate level research. Thank you.
This is one of my all time favorite technical books. I wrote a review of sorts a few years back[0]. It doesn’t cover any deep learning topics, which perhaps dates it at this point, but it gives solid fundamentals on a breadth of techniques common in industry. This is always in my recommendation list for folks making the transition from more systems or product engineering to ML.<p>[0] <a href="https://www.linkedin.com/pulse/introduction-statistical-learning-book-luke-duncan" rel="nofollow">https://www.linkedin.com/pulse/introduction-statistical-lear...</a>
I've been working my way through this book, and it's fantastic. I love the way this book grounds all the discussion of statistical learning with a practical data analysis problem.