Reading List

This list reflects my personal journey and the books that helped bridge the gap in my understanding of AI and Data Science. Some of these are core references from my own academic studies. I have divided them into three levels to help you navigate your own learning path.

Level 1: Fundamental

Focus: Building the mathematical intuition and the "Big Picture" of Data Science.

Book Cover	Title	Author	Year
	Before Machine Learning Volume 1 - Linear Algebra	Jorge Brasil	2023
	Before Machine Learning Volume 2 - Calculus for A.I: The fundamental mathematics for Data Science and Artificial Intelligence	Jorge Brasil	2023
	Before Machine Learning Volume 3 - Probability and Statistics for A.I: The fundamental mathematics for Data Science and Artificial Intelligence	Jorge Brasil	2024

Level 2: Deep Diving

Focus: Moving into complex architectures, Neural Networks, and Deep Learning.

Book Cover	Title	Author	Year
	An Introduction to Optimization 4th edition	Chong, Edwin K. P., Zak, Stanislaw H.	2013
	Mathematics for Machine Learning	A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth	2020
	Neural Network Design (2nd Edition)	Martin T. Hagan	2014
	Digital Image Processing	Rafael C. Gonzalez	2007
	Introduction to Robotics: Mechanics and Control	John Craig	2017

Level 3: Implementation & Robotics

Focus: Applying AI to the physical world—where Engineering meets Automation.

Book Cover	Title	Author	Year
	Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow	Sebastian Raschka and Valid Mirjalili	2017
	Robotics, Vision and Control: Fundamental Algorithms in Python	Peter Corke	2023

Don't feel pressured to read these all at once. Pick one that matches your current project or curiosity. Remember, the goal isn't just to finish the book, but to implement what you've learned. Enjoy your time, and let's start this journey together!