GitHub repositories are like casinos with valuable resources that can kickstart your Machine Learning journey. With the plenty of free resources above, you are well-equipped to learn about Machine learning, Deep Learning and Artificial Intelligence with your very own curriculum. Wait, You may forgot this list or even this website. So, bookmark these resources in your browser or keep them stored in your notes and refer back to them whenever you need them. One more thing, If you think we’ve missed any best repository for machine learning or deep learning, you can share it with us on any of our social media accounts.
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👉 Machine Learning for Beginners (A Curriculum) – Microsoft has created a free MIT-approved learning course titled “Machine Learning For Beginners” to teach students the basics of machine learning. The curriculum covers: What techniques do ML researchers use to build Machine Learning Models, How to build linear and polynomial regression models, How to build a web app to use your trained model, What are the real-world applications of classical Machine learning and a lot more.
👉 ML Glossary – Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more. The goal of the glossary is to present content in the most accessible way possible, with a heavy emphasis on visuals and interactive diagrams.
👉 Homemade Machine Learning – This github repos covers python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.
👉 ML Residency – In this github repo, You’ll find curated AI and ML Residency Programs from top companies like Apple, Facebook, OpenAI, IBM, Uber, Microsoft, Google, NVIDIA, Intel and more.
👉 Machine Learning Pipeline – An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch. This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial which teaches you how to “Train your own neural network” or “Learn deep learning in under 30 minutes”.
👉 Machine Learning Notes – It contains continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides). Topics covered in this notes are Neural Networks Gaussian Process and Neural Tangent Kernel Initialization, Inference, Regression methods, Recommendation system, Word Embeddings, Deep Natural Language Processing, Conjugate Gradient Descend, Lagrangian Dual, Monto Carlo Tree Search, Policy Gradient, Advanced Probabilistic Model, Restricted Boltzmann Machine, Advanced Variational Autoencoder, 3D Geometry Fundamentals and more.
👉 Machine Learning for Software Engineers – An approach to studying Machine Learning that is mainly hands-on and abstracts most of the Math for the beginner. This approach is unconventional because it’s the top-down and results-first approach designed for software engineers.
👉 Applied Machine Learning – Papers & tech blogs by companies sharing their work on data science & machine learning in production.
👉 Papers Reading Roadmap – If you are a newcomer to the Deep Learning area, the first question you may have is “Which paper should I start reading from?” Check out the reading roadmap of Deep Learning papers given in this repo!
👉 Machine Learning Interviews – Machine Learning Interviews from FAAG, Snapchat, LinkedIn. The author has created this repo on the basis of his personal experience.
👉 Production Machine Learning – This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning.
👉 Interactive Tools – This is one of the best and most recommended github repo for all machine learning practitioners. You will find interactive and visualization tools that will help you understand Bert, Convolution Neural Network, GANs, Probability, Statistics and other topics of deep learning and machine learning.
👉 Foundation Of ML – Using this repo, You can learn the foundations of ML through intuitive explanations, clean code and visuals. Also, You can learn how to apply ML to build a production grade product to deliver value.
👉 Best of Machine Learning with Python – This curated list contains 880 awesome open-source projects on Data Visualization, NLP, Time Series, Distributed Machine Learning, Data Pipelines & Streaming, Hyperparameter Optimization & AutoML, Model Interpretability, Reinforcement Learning, Recommender Systems and other topics.
👉 Practical Reinforcement Learning – In this repository, You’ll find an open course on reinforcement learning in the wild. This course has been already taught on-campus at HSE and YSDA and maintained to be friendly to online students.
👉 MLOps – Expert’s Curated Blogs, Articles, Videos, Papers, Codes, Books, Talks, Newsletters and more on Machine Learning Operations.
👉 Machine Learning Projects And Tutorials – In this repository you will find tutorials and projects related to Machine Learning. The author has tried to make the code as clear as possible, and the goal is be to used as a learning resource and a way to lookup problems to solve specific problems.
👉 MLSS – A list of summer schools in machine learning & related fields across the globe.
👉 Deep Learning For Music – Non-exhaustive list of scientific articles, thesis and reports on deep learning for music.
👉 ML Surveys – Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
👉 Paper Code Interpretation – Collection of Cvpr2021, Cvpr2020, Cvpr2019, Cvpr2018 and Cvpr2017 thesis/code/interpretation/live broadcast, papers and projects.
👉 Deep Learning In Production – In this repository, You’ll find some useful notes and references about deploying deep learning-based models in production.
👉 Deep Learning Paper Implementations – A collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations and side-by-side notes.
👉 Public Datasets – A topic-centric list of high quality open datasets for Machine Learning, Time Series, NLP, Image Processing and more.
👉 Awesome TensorFlow – A curated list of awesome TensorFlow Tutorials, Models/Projects, Libraries, Tools/Utilities, Videos, Papers, Articles, Community, Books and more.
👉 Deep Learning Drizzle – Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!
👉 Machine Learning on Source Code – A curated list of awesome research papers, datasets and software projects devoted to machine learning and source code.
👉 Autonomous Vehicles – Foundations, Courses, Papers, Research Labs, Datasets, Open Source Software, Hardware, Toys, Companies, Media and Laws related to Autonomous Vehicles.
👉 Computer Vision – Awesome Books, Courses, Papers, Software, Datasets, Pre-trained Computer Vision Models, Tutorials, Talks, Blogs, Links and Songs related to Computer Vision.
👉 Pytorch – This repository has a collection of best tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch.
👉 Awesome NLP – A curated list of Research Summaries and Trends, Prominent NLP Research Labs, Reading Content, Videos and Courses, Books, Libraries, Datasets and Annotation Tools dedicated to Natural Language Processing (NLP)
👉 Have Fun with Machine Learning – An absolute beginner’s guide to Machine Learning and Image Classification with Neural Networks. A hands-on guide to machine learning for programmers with no background in AI.
👉 Production Deep Learning – A guideline for building practical production-level deep learning systems to be deployed in real world applications.
👉 Awesome Reinforcement Learning – A handpicked collection of Lectures, Books, Surveys, Papers / Thesis, Codes, Tutorials / Websites, Online Demos and Open Source Reinforcement Learning Platforms related to Reinforcement Learning.
👉 Machine Learning Cheat Sheets – This repository aims at summing up in the same place all the important notions that are covered in Stanford’s CS 229 Machine Learning course, and include: Refreshers in related topics that highlight the key points of the prerequisites of the course and Cheatsheets for each machine learning field, as well as another dedicated to tips and tricks to have in mind when training a model.
👉 Deep Learning Papers – This repo covers the most cited papers on various topics like Image Segmentation / Object Detection, Natural Language Processing / RNNs, Reinforcement Learning / Robotics, Convolutional Network Models, Unsupervised / Generative Models, and more.
👉 Machine learning Basics – This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations.
👉 Machine Learning Videos – This is a collection of amazing recorded talks at machine learning conferences, workshops, seminars, summer schools, and miscellaneous programs.
👉 Awesome Youtubers – An awesome list of awesome YouTubers that teach about technology.
👉 Machine Learning Tutorials – This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources.
👉 Awesome Deep Learning – A curated list of awesome Deep Learning tutorials, video lectures, research papers, blogs, datasets, frameworks, researchers, conferences, tools, projects, free books pdf, and communities.
👉 Speech and Natural Language Processing – A curated list of speech and natural language processing resources. It includes Finite State Toolkits and Regular Expressions, Language Modelling Toolkits, Speech Recognition Tools, Signal Processing Tools, Machine Translation Tools, Blogs, Books and more.
👉 Machine Learning with Python – This Repository contains python codes for essential and common machine learning algorithms like Random Forest, Linear Regressions, Support Vector Machines, Naive Bayes Classifier, Principal Component Analysis, Logistic Regression, Decision Trees, XgBoost, Clustering and more.
👉 3D Machine Learning – It covers Courses, Datasets for 3D Models, Research Papers for 3D Pose Estimation, Single Object Classification, Multiple Objects Detection, Scene/Object Semantic Segmentation, 3D Geometry Synthesis / Reconstruction, Parametric Morphable Model-based methods, Part-based Template Learning methods, Texture/Material Analysis and Synthesis, and more.
👉 Tutorials – A comprehensive updated list of Artificial Intelligence, Machine Learning & Deep Learning Tutorials. In addition, You will also find deep learning blogs along with rss links.
👉 Python Machine Learning Jupyter Notebooks – Practice and tutorial-style notebooks covering wide variety of machine learning techniques. Jupyter notebooks covers a wide range of functions and operations on the topics of NumPy, Pandas, Seaborn, Matplotlib etc. Tutorial-type notebooks covers regression, classification, clustering, dimensionality reduction, and some basic neural network algorithms.
👉 Machine Learning Interpretability – A curated list of awesome machine learning interpretability resources. Resources includes Comprehensive Software Examples and Tutorials, Machine learning environment management tools, Free Books, Government and Regulatory Documents, Other Interpretability and Fairness Resources and Lists, Review and General Paper, Classes and more.
👉 Awesome Artificial Intelligence – A curated list of Artificial Intelligence courses, books, video lectures, competitions, AI newsletters, Free books, and papers.
👉 Start Machine Learning in 2021 – A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without any background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
👉 Projects – A huge list of Artificial Intelligence, Deep learning, Computer vision, NLP and Machine learning Projects with source code.
👉 Mobile Machine Learning – A curated list of amazing mobile machine learning resources for iOS, Android, and edge devices. You’ll find tutorials, codes, articles, research papers, libraries, ebooks, courses and a lot more.
👉 Machine Learning Surveys – A curated list of Surveys, Tutorials and Books on Active Learning, Bioinformatics, Classification, Metric Learning, Monte Carlo, Multi-Armed Bandit, Multi-View Learning, Semi-Supervised Learning, Submodular Functions, Transfer Learning, Unsupervised Learning and more.
👉 Machine Learning From Scratch – Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. It aims to cover everything from linear regression to deep learning.
👉 System for ML – A curated list of research in machine learning system. In this repo, You’ll also find the summary of some of the most interesting research papers.
👉 Quant Machine Learning Trading – Quant/Algorithm trading resources with an emphasis on Machine Learning. Resources covered by the machine learning research engineer includes books, youtube videos, blogs and articles, interviews, research papers, environments, codes and more
👉 Awesome ML for Cyber Security – A curated list of amazingly awesome tools and resources related to the use of machine learning for cyber security.
👉 Machine Learning Algorithms – A collection of minimal and clean implementations of machine learning algorithms. This repo is targeting people who want to learn internals of ml algorithms or implement them from scratch.
👉 Stock Prediction Models – A helpful list of machine learning and deep learning models for Stock forecasting including trading bots and simulations.
👉 Deep Learning Projects – In this repo, There are specific bite-sized projects to learn an aspect of deep learning, starting from scratch. The projects are in order from beginner to more advanced, but feel free to skip around.
👉 Models – Simple Implementation of machine learning and deep learning models. The original implementations are quite complex and not really beginner friendly. The author has created this repo to break that complexity and tried to simplify most of it.
👉 Deep Vision – A curated list of deep learning Papers, Courses, Books, Videos, Tutorials, Blogs and Softwares for computer vision.
👉 Deep Learning Project Ideas – A Handpicked collection of 30+ Natural Language Processing, Recommender Systems, Deep Learning and Machine Learning Project Ideas.
👉 Multimodal ML – Research Papers for Multimodal Machine learning. Apart from papers, You’ll also find Datasets, Workshops, Tutorials and Courses on Multimodal ML.
👉 TensorFlow without a PHD – A crash course in six episodes for software developers who want to learn machine learning, with examples, theoretical concepts, and engineering tips, tricks and best practices to build and train the neural networks that solve your problems.
👉 Machine Learning With Ruby – A curated list comprises of awesome libraries, data sources, tutorials and presentations about Machine Learning utilizing the Ruby programming language.
👉 Graph-based Deep Learning – The repository contains links primarily to conference publications in graph-based deep learning.
👉 Image Classification – A curated list of top 5 deep learning image classification papers and codes.
👉 Graph Deep Learning – A comprehensive collection of recent papers on deep learning for graphs.
👉 Awesome Python – A curated list of awesome Python frameworks, libraries, software and resources.
👉 LearnOpenCV – A list of 100+ project based OpenCV articles and their codes.
👉 Roadmaps – This repository contains Artificial Intelligence Roadmap, Machine Learning Roadmap, Deep Learning Roadmap, Data Engineer Roadmap, Big Data Roadmap and Data Science Roadmap.
👉 Tools – A collection of image annotation, video annotation, semantic segmentation and data labelling tools.
👉 Reinforcement Learning – A series of simple Reinforcement Learning Methods and Tutorials covering basic RL algorithms to recently developed and updated advanced algorithms.
👉 NLP Overview – This github repos contains an overview of recent trends in deep learning based natural language processing (NLP). It covers the theoretical descriptions and implementation details behind deep learning models, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning, used to solve various NLP tasks and applications. The overview also contains a summary of state of the art results for NLP tasks such as machine translation, question answering, and dialogue systems.
👉 Community Detection – A curated list of community detection research papers with implementations.
👉 AutoDL – A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.
👉 Knowledge Graphs – A collection of Libraries, Softwares, Tools, Courses, Tutorials, Seminars, Related Github Repos, Research Papers and more on Knowledge Graphs.
👉 Style Transfer In Text – This is a paper list for style transfer in text. It also contains some related research areas, including controlled text generation.
👉 Awesome Pytorch – A comprehensive list of pytorch related content on github, such as different models, research paper implementations, helper libraries, books, talks & conferences, tutorials etc.
👉 NLP Tasks – A Handpicked list of Natural Language Processing Tasks, Projects, Papers, Challenges and Selected References.
👉 Courses And Video Lectures – A Huge Collection of free Image Processing, Computer Vision, Artificial Intelligence, Machine Learning, Programming and Computer Science related courses and video lectures.
👉 3D Reconstruction – A curated list of papers & resources linked to 3D reconstruction from images.
👉 Human Pose Estimation – If you want to learn the basics of Human Pose Estimation and understand how the field has evolved, check out this repo filled with curated papers and free resources.
👉 Text Detection and Recognition – A curated list of resources for text detection/recognition (optical character recognition ) with deep learning methods.
👉 Image To Image – A collection of image-to-image papers. Papers are ordered in arXiv first version submitting time (if applicable).
👉 Capsule Networks – A List of Videos, Blogs, Papers with Source Code and Implementations, and other resources related to capsule networks.
👉 Synthetic Computer Vision – A list of synthetic dataset and tools for computer vision. This is a repo for tracking the progress of using synthetic images for computer vision research.
👉 Neural Rendering – A collection of papers, implementations and other resources on neural rendering.
👉 TensorFlow 2.x Tutorials – TensorFlow 2.x version’s Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc.
👉 Data Augmentation – In this repo, You’ll find a curated list of useful data augmentation resources. You will also find here some not common techniques, libraries, links to github repos, papers and others.
👉 Time Series in Python – This repo contains a list of popular python packages for time series analysis
👉 Anomaly Detection – A curated list of awesome anomaly detection papers and their source code.
👉 AutoML – This repo has a curated list of automated machine learning papers, articles, tutorials, slides and projects.
👉 Deep / Algorithmic Trading – A collection of code, papers, and resources for AI/deep learning/machine learning/neural networks applied to algorithmic trading.
👉 Action Recognition – A curated list of Action Recognition, Action Classification, Object Recognition and Pose Estimation Resources.
👉 Object Detection – In this repo, You’ll find a list of awesome articles about object detection. If you want to read the paper according to time, you can refer to Date.
👉 Pattern Classification – A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks.
👉 Satellite Imagery – This repository explores the different deep learning and machine learning techniques people are applying to common problems in satellite imagery analysis.
👉 TensorFlow Tutorials – A collection of updated tutorials for TensorFlow 2. This repository is intended for those who are beginning their journey in Deep Learning and Tensorflow.
👉 Adversarial Nets Papers – You will find awesome papers about Generative Adversarial Networks. The majority of papers are related to Image Translation, Facial Attribute Manipulation, Generative Models, Image Inpainting, GAN Theory and more.
👉 Paper Summaries – This repository contains a list of NLP paper summaries intended to make NLP techniques and topics more approachable and accessible. The contributors and authors have identified and listed several important papers with summaries in this repository.
👉 Deep Learning Roadmap – The author has created a deep learning roadmap for all the beginners. This roadmap repository contains a collection of resources like tutorials, free courses, blogs, papers and a lot more.
👉 Models – In this repo, You’ll find a collection of pre-trained, state-of-the-art deep learning and machine learning models in the ONNX format.
👉 Self Supervised Learning – A curated list of awesome self-supervised learning Graphs, Talks, Thesis, Blogs, Theories, surveys, papers and a lot more.
👉 NLP Best Practices – This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language.
👉 CNN Explainer – An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs)
👉 Face Recognition – In this repo, You’ll find papers about Face Detection, Face Alignment, Face Recognition, Face Reconstruction, Face Tracking, Face Super-Resolution, Face Generation, Face Transfer, Face Anti-Spoofing and Face Retrieval
👉 Open Source Projects – In this repository, A collection of popular github projects related to deep learning are provided and rated according to stars.
👉 Recommender System – This repository provides a curated list of papers about Recommender Systems including comprehensive surveys, general recommender system, social recommender system, deep learing-based recommender system, cold start problem in recommender system, efficient recommender system, exploration and exploitation problem in recommender system and more.
👉 Visual Tracking Papers – In this repo, You’ll find a mindmap of deep learning and visual tracking papers.
👉 Computational Advertising Papers – A collection of computing advertising-related papers and learning materials that have been implemented or read in the work and share with the industry, as a summary of their own work, and hope to bring convenience to students in computing advertising-related industries.
👉 60 Days Of Deep Reinforcement Learning – Learn Beginners, Intermediate and Advanced Deep Reinforcement Learning topics in 60 days! In this repo, You’ll find everything well arranged from articles, tutorials, youtube videos, papers implementations, projects and codes.
👉 Tracking & Detection – A collection of papers, datasets, code and other resources for object tracking and detection using deep learning.
👉 DeepLearn Implementation – This repository contains implementation of some popular research papers on Natural Language Processing, Computer Vision, ML, and Deep Learning.
👉 Machine Learning Notebooks – A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
👉 100 Days of Machine Learning Challenge – A machine learning challenge repo with insightful infographics, tutorials, codes and more. Take this challenge and start diving into machine learning coding.
👉 Awesome AWS – A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome.
👉 Deep Learning Research Papers – A list of recent papers regarding deep learning and deep reinforcement learning. They are sorted by time to see the recent papers first.
👉 Core ML Models – The largest collection of machine learning models in Core ML format, to help iOS, macOS, tvOS, and watchOS developers experiment with machine learning techniques.
👉 Useful Java Links – A list of useful Java tools, frameworks, libraries, software and hello worlds examples for Machine Learning, Web development, Game Development and Java Practitioners.
👉 The GAN Zoo – Every week, new GAN papers are coming out and it’s hard to keep track of them all, not to mention the incredibly creative ways in which researchers are naming these GANs! So, In this repo, You’ll find a list of what started as a fun activity compiling all named GANs!
👉 PyTorch Image Models – PyTorch image models, scripts, pretrained weights — ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more.
👉 Robotics – A curated list of robotics libraries and simulators.
👉 Machine Learning Interviews – This repo aims to be an enlightening guideline to prepare for Machine Learning / AI technical interviews. It has compiled based on the personal experience and notes from author’s own ML interview preparation early 2020, when he received offers from Facebook (ML Specialist), Google (ML Engineer), Amazon (Applied Scientist), Apple (Applied Scientist), and Roku.
👉 Machine Learning Tutorials – A collection of 100+ machine learning tutorials mostly written in python. The content aims to strike a good balance between mathematical notations, educational implementation from scratch using Python’s scientific stack including numpy, numba, scipy, pandas, matplotlib, etc. and open-source library usage such as scikit-learn, pyspark, gensim, keras, pytorch, tensorflow, etc.
👉 TensorFlow Examples – TensorFlow Tutorial and Examples for Beginners. This tutorial was designed for easily diving into TensorFlow, through examples.
👉 Quantum Machine Learning – In this repo, You will find resources related to Quantum Machine Learning Basics, Quantum Machine Learning Algorithms, Quantum Neural Networks, Quantum Statistical Data Analysis, Quantum Artificial Intelligence, Quantum Computer Vision and more.