Learning AI

This is my personal dive into AI after keeping my head in the sand for far too long. The intent is to grasp the basic concepts and then apply them to some relevant problems.

https://medium.com/bitgrit-data-science-publication/a-roadmap-to-learn-ai-in-2024-cc30c6aa6e16 from Benedict Neo is going to be my guide.

Directory structure

• ./python-lessons - Various Python classes and lessons. The README contains accumulated info. Subdirectories are separate courses
• ./python-lessons/practical-python - Code and exercises from the excellent Practical Python by David Beazley
• ./python-lessons/python-mastery - Code, course material, exercises for Advanced Python Mastery by David Beazley
• ./coursera-ai-courses - AI/ML courses on Coursera. See subdirectories for each course
• ./coursera-ai-courses/ai-for-everyone/ - Andrew Ng's less technical intro to AI
• ./coursera-ai-courses/machine-learning/ - Andrew Ng's updated technical ML course
• ./coursera-ai-courses/advanced-learning-algorithms/ - Advanced learning algorithms like k-means, PCA, decision trees, etc.
• ./pytorch-intro - Intro material on using PyTorch. See the README for the details there.

PyTorch tutorials on YouTube

Watch on YouTube

Glossary

• conda Package and environment management system
• Anaconda Distribution of conda, Anaconda navigator, and 250+ scientific and ML packages
• PyTorch A Python machine learning framework, library, etc.
• venv The somewhat standard Python tool to manage virtual environments. There are about 3,000 such other tools doing similar things with confusingly similar names. Just pick one so you don't lose your mind. I'll go with this for now.
• Tensor (PyTorch) The PyTorch analog to ND arrays. They resemble NumPy arrays in many was and are convertible to/from numpy. For data on the CPU, memory is shared between the Tensor and NumPy array.
• Dataset (PyTorch) Data structure storing data samples and corresponding labels
• DataLoader (PyTorch) Wraps an iterable around a Dataset for easy access

Resources

• ✅ https://dabeaz-course.github.io/practical-python/Notes/Contents.html
• ✅ https://github.com/dabeaz-course/python-mastery?tab=readme-ov-file
• https://python-patterns.guide/
• https://medium.com/bitgrit-data-science-publication/a-roadmap-to-learn-ai-in-2024-cc30c6aa6e16
• https://www.swyx.io/learn-in-public
• https://pytorch.org/tutorials/beginner/basics/intro.html
• Andrew Ng on Coursera
  • ✅ https://www.coursera.org/learn/ai-for-everyone
  • ✅ https://www.coursera.org/learn/machine-learning
  • ✅ https://www.coursera.org/learn/advanced-learning-algorithms/
  • https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning
  • https://www.coursera.org/learn/neural-networks-deep-learning
  • https://www.coursera.org/specializations/deep-learning#courses
  • https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
  • https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning
  • https://github.com/dibgerge/ml-coursera-python-assignments

Actions

• Start my personal blog
• Start my personal twitter
• Keep track of what I'm learning publicly