http://shop.oreilly.com/product/0636920030157.do
It's more focused on how to analyze existing biological data with the shell, R, and how to use git.
Personally, I've rarely seen advanced machine learning being used outside of genome-wide association studies, and even there most people just use PLINK's logistic regression without understanding what's being done and call it a day.
Another really good book on how to understand statistics is Motulsky's Intuitive Biostatistics - it introduces all common "tests" and methodologies people working in the life sciences use, but without the formulas (you use R for that anyway). It's more about the caveats of each test, in which situation you'd use it, what can go wrong, how to interpret the results etc., all written in a very lively style.
- https://news.ycombinator.com/item?id=22283368
- https://news.ycombinator.com/item?id=18896204
I personally learned it from bioinformaticians theres great coverage of this and other command line data skills in this book: https://www.oreilly.com/library/view/bioinformatics-data-ski...
The SQLite, pandas, bash, make stack for quick data science projects is a great and maintainable one that doesn’t require too much specialized knowledge.