🤖 Ultra-Learning Artificial Intelligence

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Phase 1: Prerequisite-Math Foundations

• Calculus
  • Goals: Understand derivatives, integrals, and fundamental theorems
  • Resources (Choose One):
    • Khan Academy: Calculus Diferential | Integral
  • Recommended Books:
    • Calculus Made Easy

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
    • Stats: Learn statistics topics like linear regression, classification,tree-based methods
    • Khan Academy: Statistics and Probability

• All in one course: Math Of Intelligence

Phase 2: Programming Fundamentals

• Data Structures and Algorithms

  • Goals: Learn common data structures and algorithms
  • Resources
    • Data Structures and Algorithms in Python - Full Course for Beginners

• Python

  • Goals: Become proficient in Python syntax and libraries.
  • Harvard CS50: Python
  • Recommended Boks
    • Fluent Python: Clear, Concise, and Effective Programming - Luciano Ramalho

Phase 3: AI Fundamentals

• Machine Learning
  • Goals: Understand supervised and unsupervised learning, large language models, and reinforcement learning.
  • Recommended Books
    • CS229: Lecture Notes

Study Log

• Today I Learnt

Extras

• Why Machines Learn: The Elegant Math Behind Modern AI

• Calculus Made Easy

• Machine Learning with PyTorch and Scikit-Learn

• Learning From Data

• ML Google For Devs

• Ian Goodfellow and Yoshua Bengio and Aaron Courville

• Deep Learning Book

• StanfordEDx

• Machine Learning Mastery

• AI and DS - Roadmap

• Python Deliberate Practice

• Introduction to Deep Learning

• Lil'Log

• Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

• LLM101n: Let's Build a Storyteller

• The Rise of the AI Engineer

• 5 Formatting Techniques for Long-Form Content

• Gradient Descent Method

• Mathematics for Machine Learning

• Fast Way to Learn Go

• AI Safety

• Python Documentation Contents

• Data Science vs Data Engineering

• Calm Code

• Integral Calculus - Khan Academy

• Multivariable Calculus - Khan Academy

• The Essence of Calculus - 3B1B

• How To Become A 10x Developer: Step-By-Step Guide

• How to Become a Machine Learning Engineer: Step-By-Step Guide

• How To Become An AI Engineer From Scratch in 2024

• Become a Great Software Engineer (Use These 4 Habits)

• Don’t Be a Junior Developer: The Roadmap From Junior to Senior

• The Prompt Report: A Systematic Survey of Prompting Techniques

• How to Write a Paper

• MLE Job Hunt

Acknowledgements

• AI and Data Scientist Roadmap
• Roadmap to Learn AI in 2024
• ML Engineer Roadmap
• Lil
• Neo
• Leonie
• Gautam Kunapuli
• EXA AI