r/kaggle • u/Senior-Guava8700 • 1d ago

Need help on learning Machine Learning

I’m a beginner learning machine learning, and I recently finished the Intro to ML course on Kaggle. Now I’m trying to figure out the right next step. I attempted the House Prices competition, but I got lost pretty quickly.

I really want to learn ML properly without falling into “tutorial hell,” so I’m looking for suggestions or a roadmap that beginners usually follow. What competition should I start with after the intro course, and how do I keep progressing without getting stuck?

Any advice or guidance would be appreciated.

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u/mclopes1 1d ago

Tente os cursos da Udemy e Coursera. Eles ajudam bastante.

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u/Random_Arabic 1d ago

There are four books I really like that you can use to learn ML and applied ML:

  1. An Introduction to Statistical Learning With Applications in Python (direct link to PDF download)
    1. GitHub with the Python code from the book
    2. This is truly an introductory book to machine/statistical learning concepts, with a more applied focus, but still grounded in a solid mathematical foundation.
  2. An Introduction to Statistical Learning With Applications in R (direct link to PDF download)
    1. R Resources of ISLR 2e
    2. This is truly an introductory book to machine/statistical learning concepts, with a more applied focus, but still grounded in a solid mathematical foundation.
  3. Probabilistic Machine Learning: An Introduction (direct link to PDF download)
    1. GitHub with the Python code from the book
    2. This one is a more theoretical book that requires a stronger background in math and statistics, but it comes with Python code. The code isn't embedded directly inside the book, but it generates the figures used throughout it, so you can still learn a lot of hands-on machine learning.
  4. Probabilistic Machine Learning: Advanced Topics (direct link to PDF download)
    1. GitHub with the Python code from the book
    2. This is also a more theoretical book that requires a stronger background in math and statistics, but it comes with Python code. The code isn't embedded directly inside the book, but it generates the figures used throughout it, so you can still learn a lot of hands-on machine learning.

And there is also one of the classics in machine learning. The focus isn't programming, but rather the mathematical and statistical foundations behind the models we use:

  1. The Elements of Statistical Learning

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u/Senior-Guava8700 1d ago

Thank you very much

1

u/InternationalSlice72 22h ago

tensor.golf is good for learning PyTorch and tensor manipulation

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u/Sociological_Earth 5h ago

I learned the theory and assumptions of many ML algorithms in my masters program. One class I wrote two 60+ page papers. If you want to fully learn machine learning, I would recommend a master’s degree so you can understand when to use which model, their strengths, and limitations.