free-algorithm-resources

Algorithm Free Resources | This repo collects 116 of free resources for Algorithm. 🧩 Master the art of problem-solving with our Algorithm Playground! This repository offers a vast array of free algorithm resources, courses, and an online practice environment. Essential for computer science studen...
https://github.com/getvmio/free-algorithm-resources

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Resources
- Advanced Algorithms
- Theory of Computing
- Mathematical Foundations of Computing
- Algorithms
- Introduction to Algorithms
- Software Design and Analysis III
- Advanced Algorithm Design & Analysis
- Algorithms Design and Analysis
- Data Structures in C++
- Fundamental Algorithms
- Advanced Algorithms
- Data Structures
- Data Structures & Functional Programming
- Comprehensive Guide to JSON Decoding Algorithms
- Data Structures and Algorithms for Beginners
- Data Structures and Algorithms in Python
- A Practical Introduction to Data Structures and Algorithm Analysis Third Edition (Java Version)
- The Great Tree List Recursion Problem
- Data Structures (Into Java)
- Data Structures
- Algorithms and Data Structures - With Applications to Graphics and Geometry
- Advanced Algorithms
- Fundamental Algorithms: Design and Analysis
- Programming and Data Structure
- Introduction to Algorithms
- Algorithm Design and Analysis
- Algorithms
- Advanced Mechanism Design
- Advanced Data Structures
- Algorithmic Game Theory
- Data Structures and Algorithms
- Introduction to Theory of Computing
- Information Retrieval - scale systems. Discover the use of classification systems and thesauruses in web search and digital libraries. |
- Discrete Structures - Champaign. |
- Software Foundations - assisted theorem proving, functional programming, and more. |
- 6.854J/18.415J Advanced Algorithms - MIT - year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures. The need for efficient algorithms arises in nearly every area of computer science. But the type of problem to be solved, the notion of what algorithms are "efficient, and even the model of computation can vary widely from area to area. In this second class in algorithms, we will survey many of the techniques that apply broadly in the design of efficient algorithms, and study their application in a wide range of application domains and computational models. The goal is for the class to be broad rather than deep. Our plan is to touch upon the following areas. This is a tentative list of topics that might be covered in the class; we will select material adaptively based on the background, interests, and rate of progress of the students. |
- Advanced Algorithms
- Software Design and Implementation - on testing and debugging experience. |
- Software Design & Analysis I - solving, with a focus on designing, implementing, and evaluating small-scale programs using C++. |
- Analysis of Algorithms - world computing applications in systems, networks, AI, computer vision, data mining, and computational biology. |
- Data Structures & Algorithms in Python - on video tutorials, coding assignments, and project-based learning. |
- Principles of Algorithmic Problem Solving - solving strategies in C++. Suitable for beginners and experienced programmers. |
- Competitive Programmers Handbook - solving techniques. Valuable insights and strategies for aspiring competitive programmers. |
- The Design of Approximation Algorithms - level courses and research in discrete optimization problems. |
- The Algorithm Design Manual
- Text Algorithms
- Sequential and parallel sorting algorithms - connected processor arrays. |
- Purely Functional Data Structures (1996) - offs and performance characteristics of different data structures. |
- Problems on Algorithms (Second Edition)
- Matters Computational: Ideas, Algorithms, Source Code
- Linked List Basics
- A Field Guide To Genetic Programming
- Algorithms for Data Science - Min Sketch, Bloom Filters, and DGIM Algorithm. Gain a solid foundation in the algorithmic aspects of data science. |
- Algorithms for Big Data - scale data, taught by expert Chandra Chekuri. Suitable for data science, machine learning, and big data enthusiasts. |
- Harvard Information Theory 2022
- Design and Analysis of Algorithms - and-conquer, greedy, and dynamic programming. Taught by experienced IIT Bombay faculty. |
- Computer Algorithms - 2 - completeness in this comprehensive NPTEL course from IIT Kanpur. |
- Parallel Algorithm - on programming, and real-world applications. |
- Introduction to Game Theory and Mechanism Design - making, resource allocation, and complex problem-solving. |
- Programming, Data Structures & Algorithms in Python
- 6.046J - Introduction to Algorithms - Fall 2005, MIT OCW - and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; and number-theoretic algorithms. |
- Design and Analysis of Algorithms - solving. |
- Data Structures - solving techniques. Ideal for software engineers, computer scientists, and data professionals. |
- Data Structures and Algorithms - solving, and prepares students for technical interviews. |
- Programming Challenges - solving skills with this challenging course taught by renowned expert Professor Skiena at the prestigious Hong Kong University of Science and Technology. |
- Advanced Algorithms - and-conquer. Dive deep into algorithm design and analysis for complex problem-solving. |
- Algorithm Design and Analysis - and-conquer, greedy algorithms, and dynamic programming. Hands-on programming assignments and project. |
- Beyond Worst-Case Analysis - case analysis and their applications in various computational domains, including online algorithms, machine learning, and more. |
- Efficient Algorithms
- Advanced Algorithms for Optimization
- Algorithms - solving skills. Ideal for computer science and software engineering students. |
- Applied Algorithms - and-conquer approaches, in this comprehensive course from the University of Washington. |
- Graph Theory - on learning and real-world applications. Ideal for students and professionals in math, computer science, and operations research. |
- Algorithmic Game Theory - making in this comprehensive course on Algorithmic Game Theory at the University of Pennsylvania. |
- Open Data Structures (In C++)
- Algorithms
- Software Design and Analysis II
- Graph Theory
- Advanced Data Structures - edge data structures and their applications in computer science with MIT's 6.851 Advanced Data Structures course. |
- Advanced Algorithms Design - and-conquer, dynamic programming, and cryptography. Ideal for computer science and software engineering students. |
- Hacking a Google Interview - solving tricks. |
- Data Structures & Algorithms - lists, stacks, queues, trees, hash tables, and more. Hands-on labs and projects for computer science students. |
- CS 97SI: Introduction to Competitive Programming - solving techniques for programming contests like ACM-ICPC. Includes lecture slides, practice problems, and tips to excel in competitive programming. |
- JavaScript Algorithms and Data Structures - solving skills. |
- Data Structures - world applications. |
- Data Structures and Algorithms Full Course - solving. |
- Problem Solving with Algorithms and Data Structures using Python - solving. |
- The Code Challenge Book - solving techniques and algorithm analysis skills. |
- A Complete Guide to Standard C++ Algorithms
- Algorithms Design (In C)
- Structure and Interpretation of Computer Programs 246
- Linked List Problems - solving abilities for coding interviews and exams. |
- Learning Algorithm - solving. |
- Essential Algorithms - solving skills with Essential Algorithms, a comprehensive guide covering essential concepts for beginners and advanced programmers. |
- Elementary Algorithms
- Data Structures and Algorithm Analysis in C++ - solving using C++. Suitable for students and professionals interested in algorithmic problem-solving. |
- Algorithms for Big Data
- Data Structures And Algorithms - solving. |
- Programming, Data Structures & Algorithms - solving skills. |
- Design and Analysis of Algorithms - solving skills for careers in computer science and software engineering. |
- Sketching Algorithms
- Introduction to Algorithms - on problem-solving exercises, and programming assignments taught by renowned MIT professors. |
- Advanced Data Structures - edge data structure topics, including persistence, memory hierarchies, and geometry, taught by renowned expert Prof. Erik Demaine. |
- Advanced Algorithms - level MIT course, covering dynamic programming, network flows, and more. |
- Algorithmic Lower Bounds: Fun with Hardness Proofs
- Data Structures
- Programming and Data Structures with Python
- Algorithms: Design & Analysis 1
- Algorithms and Uncertainty
- Algorithmic Game Theory - theoretic principles underlying modern computer systems and internet applications, taught by renowned expert Tim Roughgarden. |
- Data Structures - on experience in C++ programming and problem-solving. |
- Combinatorial Algorithms & Data Structures
- Numerical Algorithms
- Introduction to Scientific Computing
- Algorithms & Models of Computation
- Algorithms
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