There are a lot of good materials created by Andreas Müller, one of the core developers of scikit-learn:
- Book: Introduction to Machine Learning with Python: A Guide for Data Scientists
- Companion Jupyter notebooks for the book available online here
- Applied Machine Learning course at Columbia University
Mike's resources:
- Programming in Python for Data Science: a set of interactive pandas/Python lessons created by Mike and two colleagues. Of particular relevance: Modules 1, 2, 4, 8.
- Python videos and notebooks from DSCI 511
- Old CPSC 330 notes on Python
Hands-on tutorials:
- https://swcarpentry.github.io/python-novice-inflammation/
- https://www.codecademy.com/learn/python
- https://dataquest.io/
- https://www.datacamp.com
Python cheat sheet:
Numpy notes:
- Numpy quickstart tutorial
- https://engineering.ucsb.edu/~shell/che210d/numpy.pdf
- http://www.cs.ubc.ca/~pcarter/cs189/cs189_ch7.html
Courses:
- https://www.coursera.org/learn/python
- https://www.udacity.com/course/programming-foundations-with-python--ud036
- https://www.coursera.org/learn/python-programming-introduction
- https://www.coursera.org/learn/python-data-analysis
- https://www.coursera.org/learn/principles-of-computing-1
- https://github.com/dabeaz-course/practical-python/blob/main/README.md
- Machine Learning (Andrew Ng's famous Coursera course)
- Foundations of Machine Learning online course from Bloomberg.
- Elements of AI
- Machine Learning Exercises In Python, Part 1 (translation of Andrew Ng's course to Python)
- A Visual Introduction to Machine Learning (Part 1)
- Machine Learning | What’s Inside the Box?
- Comments on ML "versus" statistics
- A list of data science textbooks.
- Awesome Deep Learning is a list of deep learning resources.
- Machine Learning 101 slide deck