cpsc330-2026S1

Reference material

Relevant companion materials

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

Python resources

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:

• https://learnxinyminutes.com/docs/python3/

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

Visualization

• Fundamentals of Data Visualization: General principles of visualization independent of programming language.

Online courses

Introduction to Machine Learning by Key Capabilities for Data Science program

• 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)

Short posts/articles

• A Visual Introduction to Machine Learning (Part 1)

• [Machine Learning

  What’s Inside the Box?](https://medium.com/@randylaosat/machine-learning-whats-inside-the-box-861f5c7e72a3)

• Comments on ML “versus” statistics

Lists of resources

• A list of data science textbooks.
• Awesome Deep Learning is a list of deep learning resources.

Misc

• Machine Learning 101 slide deck

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