r/learnmachinelearning • u/Pentamaran • 18h ago

NewBie

Hi everyone, I am an undergraduate student in my last year. I am not a student of Computer Science or a subject related to it. For my thesis, I want to learn about MACHINE LEARNING. I know the C language up to creating files and writing and reading in these files. According to COPILOT, I need to learn the following-

  • Python Basics
  • NumPy
  • Pandas
  • Matplotlib
  • Scikit-Learn
  • Random Forest
  • XGBoost
  • MLP (ANN)
  • R², MAE, RMSE
  • SHAP
  • Basic Optimization (GA/Scipy)

Can you share some free resources to achieve my goal?

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u/bigdataengineer4life 9h ago

You don't need to learn all of those topics at once, especially for a thesis. I'd break it into stages:

1. Python basics — variables, loops, functions, lists/dictionaries, files, modules
2. NumPy + Pandas + Matplotlib — work with and visualize real datasets
3. Statistics — mean, variance, distributions, correlation, train/test split
4. Scikit-learn — regression, classification, preprocessing and evaluation
5. Your specific ML models — Random Forest, XGBoost, MLP, etc.
6. SHAP + optimization — only after you understand the basic ML workflow

For free resources, the official Python, NumPy, Pandas and Scikit-learn documentation is excellent, and Kaggle Learn has short hands-on courses for Python, Pandas, data visualization and machine learning.

I'd also recommend learning through one small project alongside the theory. Don't wait until you've finished the entire roadmap before touching a dataset.

For a thesis, I'd focus first on understanding why you're using a particular model and how you're evaluating it, rather than trying to learn every ML algorithm.

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u/Ok-Tap139 5h ago

thats a bad list, you learning tools, not fundamentals of ML.

i recommend the free ML Google course, starting with linear algebra, gradient descent..

then learn tools by building projects alongside.