Courses tagged "computer-science"

Learn to read, write, and reason about algorithmic complexity. Understand how to measure time and space efficiency so you can make smarter engineering decisions.

Master the fundamental data structures and algorithms every software engineer needs — arrays, linked lists, trees, graphs, sorting, and dynamic programming.

The mathematical foundations every programmer needs — logic, sets, functions, graph theory, and number theory — explained with code and real engineering applications.

The linear algebra every ML practitioner needs — vectors, matrices, transformations, eigenvalues, and how these map directly to neural networks, embeddings, and dimensionality reduction.

The mathematical toolkit for competitive programming — number theory, modular arithmetic, combinatorics, graph algorithms, and dynamic programming patterns used in contests like LeetCode, Codeforces, and ICPC.