For Machine Learning mostly Linear Algebra and Calculus I-III are needed. In Linear Algebra, you should be good at eigenvectors and matrix operation. In Calculus, you should be quite comfortable with differentiation. You might also want to know basics of matrix differentiation before you start.
Check out Andrew Ng's suggestion what to know from Math.
For being able to apply Math in case of Machine Learning, you'll also need to know basic programming. There's no recommended programming language, as you'll probably need to switch between them from time to time.
Some people are also arguing that Topology is necessary and having a Physics and Biology background could help. But they are not crucial to start.
Check out this resource list on Mathematics in our group is MIT course.
- Think Stats, Allen B. Downey
- Think Bayes, Allen B. Downey
- Probabilistic Programming & Bayesian Methods for Hackers, Cam Davidson-Pilon
- The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani and Jerome Friedman
- Introduction to Linear Algebra [$], Gilbert Strang
- Numerical Linear Algebra [$], LLoyd N. Trefethen and David Bau III
- Matrix Computations [$], Gene H. Golub and Charles F. Van Loan.
- Datacamp's Data Science Cheat Sheets with includes Python (Keras, NumPy, PySpark, Bokeh, Jupyter, Pandas), R, and Matlab cheatsheets
- RStudio Cheat Sheets or their Github about R
- The Art of Computer Programming, all volumes [$], by Donald Knuth et al
- Introduction to Algorithms 3rd Edition (commonly known as CLRS) [$] by Thomas H. Cormen, Clifford Stein, Ronald L. Rivest, Charles E. Leiserson
- Algorithms in C [$] by Roberts Sedgewick
- Probability and Statistics for Programmers, Allen B. Downey
- Foundations of Data Science, Avrim Blum, John Hopcroft, and Ravindran Kannan
- A Programmer's Guide to Data Mining: The Ancient Art of the Numerati, Ron Zacharski
- Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman and Jeff Ullman
- Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition [$] by Sebastian Raschka and Vahid Mirjalili
- Think Python [$], Allen B. Downey
- An Introduction to Statistical Learning with Applications in R, Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
- R for Data Science [$], Hadley Wickham and Garrett Grolemund