That's great that you're diving into Machine Learning (ML)! Since you're a 2nd-year Computer Science student, you likely have a solid foundation in programming and math, which sets you up well for understanding ML concepts. Here’s a structured set of resources tailored to your current level and long-term goal of becoming proficient in ML:

1. Foundational Math & Programming Refresher

Before diving deep into ML, ensure your math (especially linear algebra, probability, and calculus) and Python programming skills are strong.

• Books:

  • Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
    (Great for building ML-relevant math intuition)
  • Think Python by Allen B. Downey (if you want to strengthen Python)

• Online Courses:

  • Khan Academy Linear Algebra
  • Essence of Linear Algebra by 3Blue1Brown (YouTube)

2. Introductory Machine Learning

Once comfortable with math and basic Python, start with beginner-friendly but comprehensive ML courses and books.

• Courses:

  • Andrew Ng’s Machine Learning Course (Coursera)
    (Classic and highly recommended, covers algorithms and intuition with Octave/Matlab)
  • Google’s Machine Learning Crash Course
    (Hands-on with TensorFlow and practical exercises)

• Books:

  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
    (Practical, Python-based, great for hands-on learning)
  • Pattern Recognition and Machine Learning by Christopher M. Bishop
    (More theoretical, great next step once comfortable)

3. Intermediate to Advanced Topics

After grasping fundamentals, move to deep learning, NLP, reinforcement learning, and other subfields.

• Courses:

  • Deep Learning Specialization by Andrew Ng (Coursera)
  • Fast.ai Practical Deep Learning for Coders (Very practical and code-first)
  • CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)
    (Excellent for computer vision and deep learning)

• Books:

  • Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (freely available online)
  • Reinforcement Learning: An Introduction by Richard Sutton and Andrew Barto

4. Practice Platforms

Practical experience is key:

• Kaggle (https://www.kaggle.com/)
  (Competitions and datasets to build projects and learn from others)
• Google Colab (https://colab.research.google.com/)
  (Run Python notebooks in the cloud with free GPU)

5. YouTube Channels & Websites

• 3Blue1Brown — Excellent math intuition videos (especially the Neural Networks series)
• StatQuest with Josh Starmer — Clear explanations of statistics and ML concepts
• Sentdex — Practical Python ML tutorials
• Two Minute Papers — Good for catching up on cutting-edge research in digestible videos
• Distill.pub — Interactive, visual essays on ML topics

6. Additional Tips

• Build Projects: Nothing beats learning by doing. Try implementing classic algorithms (linear regression, decision trees) from scratch.
• Join Communities: Reddit’s r/MachineLearning, Stack Overflow, and Discord servers for ML learners.
• Research Papers: As you advance, start reading papers from arXiv; tools like Papers with Code link research to implementations.

If you want, I can suggest a detailed study plan week-by-week or help you pick resources for specific ML areas like NLP or Computer Vision. Just let me know!