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- Calculus
- Goals: Understand derivatives, integrals, and fundamental theorems
- Resources (Choose One):
- Khan Academy: Calculus Diferential | Integral
- Recommended Books:
Note: If you have time, learn about Gradient, it's great to understand how Gradient Descet and BackPropagation Works.
-
Linear Algebra
- Goals: Master matrix operations, vector spaces, and linear transformations.
- Resources (choose one):
- Khan Academy: Linear Algebra
-
Probability and Statistics
- Goals
- Learn probability topics like counting, random variables, mean variance, Bayes’ theorem, distributions, limit theorems
- Learn statistics topics like linear regression, classification, tree-based methods
- Resources
- Goals
-
All in one course: Math Of Intelligence
-
Data Structures and Algorithms
- Goals: Learn common data structures and algorithms
- Resources
-
Python
- Goals: Become proficient in Python syntax and libraries.
- Harvard CS50: Python
- Recommended Boks
- Fluent Python: Clear, Concise, and Effective Programming - Luciano Ramalho
- Machine Learning
- Goals: Understand supervised and unsupervised learning, large language models, and reinforcement learning.
- Recommended Books
- CS229: Lecture Notes
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Ian Goodfellow and Yoshua Bengio and Aaron Courville
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Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
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How to Become a Machine Learning Engineer: Step-By-Step Guide
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Don’t Be a Junior Developer: The Roadmap From Junior to Senior
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The Prompt Report: A Systematic Survey of Prompting Techniques