Introduction to Statistical Learning
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data.
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- Great book: An Introduction to Statistical Learning
PDF here:
- Get a book on applied statistics, such as
Think Stats: Probability and Statistics for Programmers (https://allendowney.github.io/ThinkStats/)
Computer Age Statistical Inference (https://hastie.su.domains/CASI/)
Statistical Rethinking (https://xcelab.net/rm/)
An Introduction to Statistical Learning (https://www.statlearning.com/)
Obviously maths is going to be involved to do the subject justice. These recommendations are more about applied statistics, but that's the foundation. From there it is a small transition to statistical process control.
- Start with statistics by David Freedman. It is very approachable as an introduction, not too theory heavy, can get a handle on all of the "main" issues. Afterwards, you have 2 options:
1) Do you want "theoretical" knowledge(math background required)? If so, then you need to get a decent mathematical statistics book like Casella-Berger. I think a good US CS degree grad could handle it, but you might need to go a bit slow and google around/ maybe fill in some gaps in probability/calculus.
2)Introduction to Statistical Learning is unironically a great intro to "applied" stats. You have most of the "vanilla" models/algorithms, theoretical background behind each but not too much, you can follow along with the R version and see how stuff actually works and exercises that vary in difficulty.
With regards to Gelman and Bayesian data analysis, I should note that in my experience the Bayesian approach is 1st year MS /4th year of a Bachelors in the US. It's very useful to know and have in your toolbox but IMO it should be left aside until you are confident in the "frequentist" basics.
- Seems similar, or at least partially overlap, with what I would say is the best reference on the subject, an Introduction to Statistical Learning from Gareth James et al [1].
I wonder it this one might be a bit more accessible, although I guess the R/Python examples are helpful on the latter.
- I'd start with An Introduction to Statistical Learning - a strong background in statistics is key, especially for being able to work with models and understand their limits and how to adapt them to your own use case. The pdf for the book is free and available here: https://www.statlearning.com/
For real, this is an amazing book and it's such a gift that they have it free online and with a python version - I learned with the first edition having to translate the R code to python! I wouldn't start with The Elements to Statistical Learning - that has a much heavier emphasis on the math and theory and is hard to get through!
- 1) depends, I find ML much more interesting than software engineering.
2) Not me
3) With difficulty, but through courses like fast.ai
If you’re not into maths and problem solving this is probably the wrong path. The main value add you bring is being able to transform a business problem into solvable maths.
Read Introduction to statistical learning and look at the fast.ai courses. I also recommend the paper attention is all you need. If you find all of those things interesting and not too mathsy then I’d say go for the switch. Else, maybe look at options in data engineering or as a software engineer in a ML team.
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