Algorithms & Data Structures
- Foundations of Computer Science — Al Aho and Jeff Ullman
- Algorithm Design — Jon Kleinberg and Éva Tardos
- Algorithms and Complexity — Herbert S. Wilf
- Algorithms Course Materials — Jeff Erickson
- The Art of Computer Programming — Donald Knuth
- The Design of Approximation Algorithms
- Algorithmic Graph Theory
- Algorithms
- Algorithms, 4th Edition
- The Algorithm Design Manual
- Think Complexity — Allen B. Downey
- Principles of Algorithmic Problem Solving — Johan Sannemo
- Lectures Notes on Algorithm Analysis and Computational Complexity — Ian Parberry
- Binary Trees
- Data Structures — Prof. Subhashis Banerjee, IIT Delhi
- Data Structures — Paul N. Hilfinger
- Data Structures Succinctly Part 1
- Data Structures Succinctly Part 2
- Algorithms Notes for Professionals
- Analysis and Design of Algorithms — Sandeep Sen, IIT Delhi
- Animated Algorithm and Data Structure Visualization
- Design of a Programmer — Prakash Hegade
- Homotopy Type Theory: Univalent Foundations of Mathematics
- Introduction to Computer Science — Prof. Subhashis Banerjee, IIT Delhi
- Introduction to Computing
- Introduction to Theory of Computation — Anil Maheshwari and Michiel
- Models of Computation — John E. Savage
- Practical Foundations for Programming Languages, Preview — Robert Harper
- Principles of Programming Languages
- Programming and Programming Languages
- Programming Languages: Theory and Practice — Robert Harper
- Semantics with Applications: A Formal Introduction
- Structure and Interpretation of Computer Programs
- The Black Art of Programming — Mark McIlroy
- The Craft of Programming — John C. Reynolds
- Data Structures and Algorithms: Annotated Reference with Examples
- Elementary Algorithms — Larry LIU Xinyu
- LEDA: A Platform for Combinatorial and Geometric Computing
- Linked List Basics
- Linked List Problems
- Matters Computational: Ideas, Algorithms, Source Code
- Open Data Structures: An Introduction — Pat Morin
- Planning Algorithms
- Problems on Algorithms (Second Edition) — Ian Parberry
- Purely Functional Data Structures (1996) — Chris Okasaki
- Sequential and parallel sorting algorithms
- Text Algorithms
- The Great Tree List Recursion Problem
- The Kademlia Protocol Succinctly — Marc Clifton
Datamining
- A Programmer’s Guide to Data Mining
- Mining of Massive Datasets
- School of Data Handbook
- The Ultimate Guide to 12 Dimensionality Reduction Techniques
- Data Mining Algorithms In R — Wikibooks
- Introduction to Data Science — Jeffrey Stanton
- Introduction to Data Science — Rafael A Irizarry
- Statistical inference for data science
Python
- Python Programming
- Python Tutorial
- Scipy Lecture Notes
- Test-Driven Web Development with Python
- Text Processing in Python
- The Coder’s Apprentice: Learning Programming with Python 3
- The Definitive Guide to Jython, Python for the Java Platform
- The Little Book of Python Anti-Patterns (Source)
- The Programming Historian
- Python 3 Tutorial
- Python Data Science Handbook
- Python for Everybody
- Practical Programming in Python
- Problem Solving with Algorithms and Data Structures using Python
- Python for Informatics: Exploring Information
- Python for you and me
- Python Idioms
- Python in Education
- Python Notes for Professionals
- Python Practice Book (2.7.1)
- Python Practice Projects
- The Python GTK+ 3 Tutorial
- The Standard Python Library
- Think Complexity
- Think Python 2nd Edition
- Tiny Python
- 100 Page Python Intro
- 20 Python Libraries You Aren’t Using (But Should)
- A Byte of Python
- A Guide to Python’s Magic Methods
- A Whirlwind Tour of Python
- Automate the Boring Stuff with Python: Practical Programming for Total Beginners
- Beej’s Guide to Python Programming — For Beginners
- Build applications in Python
- Building Skills in Object-Oriented Design
- Building Skills in Python
- Code Like a Pythonista: Idiomatic Python
- Code Cademy Python
- Cracking Codes with Python
- Data Structures and Algorithms in Python
- Dive into Python 3
- From Python to NumPy
- Full Stack Python
- Functional Programming in Python
- Google’s Python Class
- Google’s Python Style Guide
- Hands-On Natural Language Processing with Python — Rajesh Arumugam, Rajalingappaa Shanmugamani (Packt account required)
- Hitchhiker’s Guide to Python! (2.6)
- How to Code in Python 3
- How to Make Mistakes in Python
- How to Think Like a Computer Scientist: Learning with Python, Interactive Edition
- How to Think Like a Computer Scientist: Learning with Python 2nd Edition
- How to Think Like a Computer Scientist: Learning with Python 3
- Intermediate Python — Muhammad Yasoob Ullah Khalid (1st edition)
- Introduction to Programming with Python (3.3)
- Introduction to Programming Using Python — Cody Jackson (1st edition) (2.3)
- Introduction to Python
- Invent Your Own Computer Games With Python
- Learn Python, Break Python
- Learn to Program Using Python
- Learning to Program
- Lectures on scientific computing with python
- Making Games with Python & Pygame
- Math for programmers
- Modeling and Simulation in Python
- Modeling Creativity: Case Studies in Python
- Natural Language Processing (NLP) with Python
- Natural Language Processing with Python
- Non-Programmer’s Tutorial for Python 3
- Non-Programmer’s Tutorial for Python 2.6
- Picking a Python Version: A Manifesto
- Porting to Python 3: An In-Depth Guide
- Program Arcade Games With Python And Pygame
- Programming Computer Vision with Python
- Programming for Non-Programmers
- Python 101
Machine Learning
- Mathematics for Machine Learning
- Mathematics for Machine Learning
- Neural Networks and Deep Learning
- Probabilistic Models in the Study of Language
- Python Machine Learning Projects
- Reinforcement Learning: An Introduction
- Speech and Language Processing (3rd Edition Draft)
- The Elements of Statistical Learning
- The Python Game Book
- Top 10 Machine Learning Algorithms Every Engineer Should Know
- Understanding Machine Learning: From Theory to Algorithms
- A Brief Introduction to Machine Learning for Engineers
- A Brief Introduction to Neural Networks
- A Comprehensive Guide to Machine Learning
- A Course in Machine Learning
- IBM Machine Learning for Dummies
- Information Theory, Inference, and Learning Algorithms
- Interpretable Machine Learning
- Introduction to CNTK Succinctly
- Introduction to Machine Learning
- Learn Tensorflow — Jupyter Notebooks
- Learning Deep Architectures for AI
- Machine Learning
- Machine Learning for Data Streams
- Machine Learning from Scratch — Danny Friedman
- Machine Learning, Neural and Statistical Classification
- A First Encounter with Machine Learning
- A Selective Overview of Deep Learning
- Algorithms for Reinforcement Learning
- An Introduction to Statistical Learning
- Bayesian Reasoning and Machine Learning
- Deep Learning — Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Deep Learning for Coders with Fastai and PyTorch
- Deep Learning with PyTorch
- Dive into Deep Learning
- Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises
- Foundations of Machine Learning, Second Edition
- Free and Open Machine Learning
- Gaussian Processes for Machine Learning
Software Engineering
- Kanban and Scrum — making the most of both
- Migrating to Cloud-Native Application Architectures
- Naked objects
- OAuth — The Big Picture
- Object-Oriented Reengineering Patterns
- Kanban for skeptics
- Reactive Microsystems: The Evolution of Microservices at Scale
- Serverless apps: Architecture, patterns, and Azure implementation
- Serverless Design Patterns and Best Practices
- Shape Up — Stop Running in Circles and Ship Work that Matters
- Site Reliability Engineering
- Software Architecture Patterns
- Software Engineering for Internet Applications
- Source Making Design Patterns and UML
- Test Driven Development, Extensive Tutorial
- The Site Reliability Workbook
- Web API Design
- Working with Web APIs
- Agile Planning: From Ideas to Story Cards
- Best Kept Secrets of Peer Code Review
- Building Secure & Reliable Systems
- Data-Oriented Design
- Domain Driven Design Quickly
- Exploring CQRS and Event Sourcing
- Guide to the Software Engineering Body of Knowledge
- How to Design Programs
- How to Write Unmaintainable Code
- Microservices Anti Patterns and Pitfalls
- Microservices vs. Service-Oriented Architecture
- Practicing Domain-Driven Design — Part 1
- Reactive Microservices Architecture
Networking
- Computer Networks: A Systems Approach
- Distributed systems for fun and profit
- High-Performance Browser Networking
- How HTTPS Works
- HTTP Succinctly, Syncfusion
- HTTP2 Explained
- Understanding IP Addressing: Everything you ever wanted to know
- An Introduction to Computer Networks
- Bits, Signals, and Packets: An Introduction to Digital Communications and Networks
- Computer Networking : Principles, Protocols and Practice
- Introduction to HTTP
- IPv6 for IPv4 Experts
- Network Science
- The TCP/IP Guide
HTML , Java Script and CSS
- Learn CSS Layout
- Learn CSS Layout the pedantic way
- Learn to Code HTML & CSS — Shay Howe
- MaintainableCSS
- Pocket Guide to Writing SVG — Joni Trythall
- Pro HTML5 Programming
- Resilient Web Design
- Scalable and Modular Architecture for CSS
- A beginner’s guide to HTML&CSS
- A free guide to learn HTML and CSS
- Adaptive Web Design
- An advanced guide to HTML&CSS
- Robust Client-Side JavaScript
- Speaking JavaScript — Dr. Axel Rauschmayer
- The JavaScript Tutorial
- The JavaScript Way
- The Problem with Native JavaScript APIs
- Thinking in JavaScript
- Understanding JavaScript OOP
- Google JavaScript Style Guide
- JavaScript Allongé
- JavaScript Bible
- JavaScript Challenges Book
- JavaScript Enlightenment
- JavaScript for Impatient Programmers (ES2020 edition)
- JavaScript Notes for Professionals
- JavaScript Patterns Collection
- JavaScript Spessore
- JavaScript Succinctly
- JavaScript the Right Way
- jQuery Fundamentals
- JS Robots
- You Don’t Know JS
- Basic JavaScript for the impatient programmer
- Book of Modern Frontend Tooling
- Building Front-End Web Apps with Plain JavaScript
- Clean Code JavaScript
- Crockford’s JavaScript
- Deep JavaScript: Theory and techniques
- Mozilla Developer Network’s JavaScript Guide
- Neural Networks with JavaScript Succinctly
- Patterns For Large-Scale JavaScript Application Architecture
- Practical Modern JavaScript
- Designing Scalable JavaScript Applications
- Eloquent JavaScript 3rd edition
- Exploring ES6
- Learning JavaScript Design Patterns
- Professor Frisby’s Mostly Adequate Guide to Functional Programming
- Atomic Design
- HTML Canvas Deep Dive
- HTML5 Canvas
- HTML5 for Publishers
- HTML5 For Web Designers
- HTML5 Notes for Professionals
- HTML5 Quick Learning Guide
- CSS Notes for Professionals
- Dive Into HTML5
- Google’s HTML/CSS Style Guide
- How to Code in HTML5 and CSS3
- W3.CSS Succinctly
- CSS Animation 101
Node.js
Operating Systems
- Operating Systems: Three Easy Pieces
- Practical File System Design: The Be File System
- Project Oberon: The Design of an Operating System, a Compiler, and a Computer
- The Art of Unix Programming
- Think OS: A Brief Introduction to Operating Systems
- Writing a Simple Operating System from Scratch
- A short introduction to operating systems
- Computer Science from the Bottom Up
- How to write a simple operating system in assembly language
- Operating Systems and Middleware
- The Design and Implementation of the Anykernel and Rump Kernels
- The little book about OS development
- The Little Book of Semaphores
Bash
- Introduction to Bash Scripting — Bobby Iliev
- Introduction to the Command Line
- Linux Shell Scripting Tutorial — A Beginner’s Handbook (2002)
- Linux Shell Scripting Tutorial (LSST) v2.0
- Advanced Bash-Scripting Guide
- Bash Guide for Beginners
- Conquering the Command Line — Mark Bates
- Getting Started with BASH
- Google Shell Style Guide
- Bash Notes for Professionals
- Bash Reference Manual (HTML)
- Bash tutorial — Anthony Scemama
- BashGuide — Maarten Billemont
C++
- The Rook’s Guide to C++
- The Ultimate Question of Programming, Refactoring, and Everything
- Think C++: How To Think Like a Computer Scientist
- Thinking in C++, Second Edition, Vol. 1
- C++ Core Guidelines — Editors: Bjarne Stroustrup, Herb Sutter
- How to make an Operating System — Samy Pesse
- How To Think Like a Computer Scientist: C++ — Allen B. Downey
- Open Data Structures
- Programming Fundamentals — A Modular Structured Approach using C++
- Software Design Using C++
- The Boost C++ libraries
- C++ Language
- C++ Notes for Professionals
- C++ Tricks
- CS106X Programming Abstractions in C++
- Elements of Programming
- Game Programming Patterns
- Google’s C++ Style Guide
Hadoop
All Machine Learning Algorithms & Models with Python
- Assumptions on Machine Learning Algorithms
- Missing Values Calculation
- t-SNE Algorithm
- AutoKeras Tutorial
- Bias and Variance
- Perceptron
- Class Balancing Techniques
- One vs All & One vs One
- Polynomial Regression
- BIRCH Clustering
- Independent Component Analysis
- Kernel PCA
- Sparse PCA
- Non Negative Matrix Factorization
- Neural Networks Tutorial
- PyCaret
- Scikit-learn Tutorial
- NLTK Tutorial
- TextBlob Tutorial
- Streamlit Tutorial
- DBSCAN Clustering
- Naive Bayes
- Passive Aggressive Classifier
- Gradient Boosting (Used in implementing the Instagram Algorithm)
- Logistic Regression
- Linear Regression
- K-Means Clustering
- Dimensionality Reduction
- Principal Component Analysis
- Automatic EDA
- Feature Scaling
- Apriori Algorithm
- K Nearest Neighbor
- CatBoost
- SMOTE
- Hypothesis Testing (Commonly used in Outlier Detection)
- Content-Based Filtering
- Collaborative Filtering
- Cosine Similarity
- Tf-Idf Vectorization
- Cross-Validation
- Confusion Matrix
- 4 Graph Algorithms (Connected Components, Shortest Path, Pagerank, Centrality Measures)
- Ridge and Lasso Regression
- StandardScaler
- SARIMA
- ARIMA
- Auc and ROC Curve
- XGBoost Algorithm
- Long Short Term Memory (LSTM)
- One Hot Encoding
- Bidirectional Encoder Representations from Transformers (BERT)
- Facebook Prophet
- NeuralProphet
- AdaBoost Algorithm
- Random Forest Algorithm
- H2O AutoML
- Polynomial Regression
- Gradient Descent Algorithm
- Grid Search Algorithm
- Manifold Learning
- Decision Trees
- Support Vector Machines
- Neural Networks
- FastAI
- LightGBM
- Pyforest Tutorial
All the above algorithms are explained properly by using the python programming language. These were the common and most used machine learning algorithms. We will update this article with more algorithms soon. I hope you liked this article on all machine learning algorithms with Python programming language. Feel free to ask your valuable questions in the comments section below.
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