These are the references I used during my college studies. You can explore them for a deeper understanding.

Select Subject (Tap to Select) ↓

Little Reminder:

To excel in Machine Learning and NLP, it's essential to continuously update and expand your knowledge as these fields evolve rapidly.

Data Structure and Algorithms

Introduction to Algorithms, fourth edition by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein.
Algorithm Design by Jon Kleinberg and Eva Tardos.
NPTEL: Design and Analysis of Algorithms by Madhavan Mukund – An NPTEL YouTube course covering algorithm fundamentals.

Linear Algebra

Linear Algebra Done Right by Sheldon Axler – This best-selling textbook for a second course in linear algebra is aimed at undergraduate math majors and graduate students.
Lecture Notes for Linear Algebra by Gilbert Strang – A foundational textbook for linear algebra concepts.
NPTEL: Linear Algebra by Pranav Haridas – An NPTEL YouTube course covering linear algebra.

Probability Theory

A First Course in Probability by Sheldon M. Ross – A foundational textbook for probability concepts.

Theory of Computation

Introduction to the Theory of Computation by Michael Sipser – An upper-level undergraduate or introductory graduate text in computer science theory.

Graph Theory

Graph Theory with Applications to Engineering and Computer Science by Narsingh Deo – A fundamental book that covers an easy explanation of graph theory.
Introduction to Graph Theory by Douglas West – A thorough understanding of the structure of graphs and the techniques used to analyze problems in graph theory.

Machine Learning

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurélien Géron – A practical book that uses Python for hands-on learning of ML techniques.
Introduction to Machine Learning, Third Edition by Ethem Alpaydın – A foundational textbook for ML concepts.
Stanford CS229: Machine Learning by Anand Avati – A Stanford YouTube course covering ML fundamentals and applications.