Data Structures & Algorithms
Introduction to Algorithms, 4th Edition Book
The definitive reference for algorithms — CLRS. Covers sorting, graph algorithms, dynamic programming, and much more with rigorous proofs.
Algorithm Design Book
Focuses on algorithm design techniques — greedy algorithms, divide and conquer, network flow, and NP-completeness with elegant real-world examples.
NPTEL: Design and Analysis of Algorithms Video
A well-structured NPTEL course covering algorithm fundamentals, time complexity analysis, and algorithmic paradigms.
Linear Algebra
Linear Algebra Done Right Book
A landmark textbook that develops linear algebra from a conceptual, determinant-free perspective. Ideal for a second course in linear algebra.
Lecture Notes for Linear Algebra Book
MIT's foundational linear algebra resource, accessible and geometrically intuitive. Great companion to Gilbert Strang's famous video lectures.
NPTEL: Linear Algebra Video
A systematic NPTEL course covering vector spaces, eigenvalues, inner product spaces, and SVD with clear mathematical rigour.
Probability Theory
A First Course in Probability, 9th Edition Book
An accessible yet rigorous introduction to probability theory with many worked examples. Covers sample spaces, random variables, distributions, and limit theorems.
Theory of Computation
Introduction to the Theory of Computation Book
The gold standard text for ToC. Covers finite automata, context-free grammars, Turing machines, decidability, and complexity theory in clear, elegant prose.
Graph Theory
Graph Theory with Applications to Engineering and Computer Science Book
A classic introductory text that covers graph fundamentals with a strong engineering and CS focus — flows, trees, planarity, and matchings.
Introduction to Graph Theory Book
A thorough treatment of graph structure and proof techniques. Covers connectivity, coloring, planar graphs, Ramsey theory, and more.
Machine Learning
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, 2nd Ed. Book
The definitive practical ML book. Covers everything from classical ML to deep learning with excellent code examples. Perfect for hands-on learners.
Introduction to Machine Learning, 3rd Edition Book
A balanced theoretical and applied introduction to ML. Covers supervised, unsupervised, and reinforcement learning with solid mathematical grounding.
Stanford CS229: Machine Learning Course
Stanford's legendary ML course available on YouTube. Covers linear models, SVMs, neural networks, EM algorithm, and more with rigorous derivations.
Group Theory
Group Theory — Short Notes Notes
Personal handwritten notes covering the core concepts of Group Theory — groups, subgroups, cosets, homomorphisms, and related structures. Written for quick revision and exam prep.
Group Theory — Complete Playlist Video
A comprehensive YouTube playlist covering Group Theory from fundamentals to advanced topics. Highly recommended for exam-focused study with clear Hindi-medium explanations.