1️⃣ Prerequisites

✅ Mathematics – Linear Algebra, Probability, Statistics, Calculus basics ✅ Programming – Python fundamentals, NumPy, Pandas, Matplotlib ✅ Tools – Git, GitHub, Jupyter Notebook

Also: Data Wrangling skills (cleaning, exploring data), SQL basics (many ML/AI roles still expect this).

Graphs: Recommended Book —> Play with Graphs [ author: Amit M. Agarwal]

DSA: Time complexity, Space complexity.

Data Base: RDBMS, SQL, DB like posgresql, mongoDB.

AI Excel tool : Quadratic AI

Mathematics:

A good understanding of math helps you grasp how ML models work internally.

Focus on:

• Linear Algebra: Vectors, matrices, eigenvalues, and transformations.
• Probability & Statistics: Normal Distribution, Optimization, Poisson Distribution.[Recommended Book: Probability and Statistics in Engineering, author: William Hines.], Random variables, hypothesis testing.
• Calculus Basics: Differentiation, gradients, and optimization.

Programming

Python is the most popular language for AI due to its simplicity and extensive libraries.

Learn:

• Python Fundamentals: Data types, loops, functions, file handling.
• Book recommended : Python for Data Analysis data wrangling with pandas, numpy & jupyter [ Wes McKinney] 🔘 Click here to read this book
• Libraries:
  • NumPy for numerical computations
  • Pandas for data manipulation.
  • Matplotlib for data visualization