My curated list of Machine Learning resources, including books, papers, software, libraries, notebooks, etc. Most of the libraries are for Python though the rest of the materials here are generally suited for working with data.

• Foundations of Machine Learning: I strongly suggest reading this book
• Readings in Database Systems(The Red Book): I strongly suggest reading this book
• A Course in Machine Learning: Good book to start learning ML
• Mining Massive Datasets: Great book about Big Data concepts, Data Mining algorithms and their applications
• Networks, Crowds, and Markets: Reasoning About a Highly Connected World : Good starter book to Network Science and its applications(e.g. graph analysis, social network analysis)
• An Introduction to Statistical Learning
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction
• Arxiv.org/ML
• Python Machine Learning
• Python Data Science Handbook
• Whirlwind Tour Of Python: Good starter book for learning Python
• Python Machine Learning (second edition)
• Deep Learning Book(MIT Press)
• Probability and Statistics Cookbook
• An ML Cheat Sheet
• Hand-book on STATISTICAL DISTRIBUTIONS for experimentalists
• Deep Learning Papernotes: A repository of many of the research papers published about various DL related topics over the years
• NLR Papers: A very good collection of papers on Network Representation Learning and Network Embedding
• KRL Papers: A nice collection of papers on Knowledge Representation Learning and Knowledge Embedding
• Stanford CS 229 ML Cheatsheets: A nice collection of ML cheat-sheets on various important subject matters
• Machine Learning for Business: Machine Learning for Business teaches you how to make your company more automated, productive, and competitive by mastering practical, implementable machine learning techniques and tools
• A gentle introduction to Tensors and their uses: An introduction to Tensors and their sample applications
• Linear Algebra course book: Jim Hefferon's Linear Algebra book, A good companion book for learning linear algebra fundamentals
• Top 10 Data Mining Algorithms: A good article describing how 10 of more famous Data Mining algorithms work
• Representation Learning: A Review and New Perspectives: A good introduction to Representation Learning and its implications
• NLTK Book: A great book if you want to process and analyse texts with NLTK
• An Introduction to Variable and Feature Selection

• UCI Machine Learning Repository: Lots of interesting datasets, piled up just for you to use them!
• Kaggle: A very active community, great place to learn from others
• Network Repository: Many network/graph datasets, If you like graphs it's the place for you!
• Deep Learning Datasets: DL datasets of course!
• MLDatasets: Another nice dataset repository
• Open Data for Deep Learning: Deep means big here I guess!
• Wikipedia List of Datasets for Machine Learning Research: It's Wikipedia!
• GHTorrent: GHTorrent project is an attempt to make an offline queryable mirror of Github projects' data available for everyone
• SOTorrent: A very rich dataset of StackOverflow posts and related contents such as post comments
• Datalist.com: A handy list of ML related datasets from all over the web

• Quora Data Science: A nice place to ask and seek answers!
• Stack Exchange Data Science: Another nice Q&A community with emphasis on the technical side
• Kaggle: Kaggle again:)
• Quora Machine Learning: Quora again:)
• Stack Overflow: General Q&A for developers, need help with your code then it's the place

• Kaggle: Kaggle again:)
• Reddit Machine Learning Community
• CrowdAI: A Kaggle alternative, popular among students
• Quora: Quora Q&A platform
• Github.com: Github contains many useful resources such as the code for many algorithms, all in one centralized platform!
• Apache Projects: A few hundred cool software projects, many are related to data management in some way! (e.g. Hadoop ecosystem)
• Stanford Machine Learning Course(Have a look at the project section!)
• NIPS Website: A very prestigious AI conference held every year
• Scipy Lectures
• Nice website about Data Mining
• ML Resources on Github
• A list of researches on a few interesting topics
• Open Machine Learning Course: An ML course covering so many topics
• Tanagra - Data Mining and Data Science Tutorials: TANAGRA's tutorials, covering a vast amount of topics
• Papers with code: It is a very handy repository of research papers that are coming with their code also published too, you can access the code from many recent cutting-edge algorithms from here

• Spyder: A great Python IDE for scientists in general
• Pycharm CE: A very good IDE for development of anything with Python
• GNU Emacs: GNU Emacs is an environment for doing almost anything
• IDLE: Default Python IDE, lean and clean environment to develop in Python
• Rodeo: A Python IDE for data scientists

• Anaconda: A very user friendly environment for scientific Python development
• Miniconda
• Vowpal Wabbit
• StackNet
• Sofia ML
• LIBLINEAR
• LibFM
• SVM Rank

• Jupyter
• Apache Zeppelin
• Beacker
• nteract
• JupyterLab: Nex generation jupyter notebook environment

• Pandas: Famous Python's data manipulation library
• Scipy: Defacto Pythons scientific computation library
• Numpy: Linear algebra library for fast numerical computation
• Scikit Learn: High-level Machine Learning library with tons of features, very easy-to-use and extendable
• Bokeh: An interactive high-level data visualization library
• Matplotlib: A very powerful data visualization library, More low-level than other visualization libs
• Graph Tool: A very fast and powerful library for working with graphs in Python, It's developed on top of Boost C++ libraries so consequently it's very efficient
• NetworkX: A Python module for Complex Network modeling and analysis, Very easy-to-use but may be slow on times because it's in pure Python
• TensorFlow: Low-level library for creating deep artificial neural networks, works both on CPU and GPU, Usually you use TF in conjunction with a library with higher level API exposing TF's functionalities like Keras
• Keras: "Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano" - Keras's website
• NLTK
• Pattern
• IPython
• Orange
• Theano
• CatBoost: Yandex's implementation of Gradient Boosting on Decision Trees, It supports categorical features out of the box
• XGboost: Original XGBOOST library, A very efficient Gradient Boosting library with extra regularization
• Mlxtend: A great Data Mining and Machine Learning library with
• NetworKit: A very high performance graph processing and analysis toolkit, written in C++ and uses OpenMP so it is very fast on multicore computers
• Eli5
• Pandasql
• Dask: A fast data manipulation library with out-of-core handling of the data, Suited for a distributed environment, Its API is (exactly)compatible with Pandas' API
• MLBox
• Gensim
• Scikit-learn-Contrib/Imbalanced-learn: An extension library for Scikit-learn for handling imbalanced datasets
• Patsy: "Kamelot!!! ... It's just a model ... Shhhh!"
• Statsmodels: A Python package for building various statistical models
• Seaborn: A high-level visualization library for Python
• Pandas-profiling
• Blaze
• Altair
• Numba
• BigARTM
• GYM
• PyBrain: A Machine Learning library for Python with emphasis on modeling via many types of neural network architectures
• Sklearn-pandas
• Auto-ML
• Scikit-Learn Contrib/Lightning: An extension library to Scikit-learn for large-scale linear classification, regression and ranking problems
• GPLearn
• Nengo
• Scikit-learn Contrib/*: A collection of extension libraries for Scikit-learn adding new (missing) functionalities to it
• Koolmogorov: A Python library for hierarchical clustering and visualization
• Lime: A tool for exploring and explaining the output of classifiers
• TreeInterpreter
• SNAP-Python: Python wrapper library for Stanford Network Analysis Platform (SNAP)
• Pycobra: A Python library implementing ensemble methods for regression, classification and visualization tools including Voronoi tessellations
• TF Learn: A library on top of TensorFlow providing a higher API than TensorFlow
• Featuretools: A Python library for automated feature engineering
• spaCy: NLP library with tons of features(like various CNN models)
• SymPy: Symbolic computation library for Python, Aiming to become a full-fledged CAS
• Uniform Manifold Approximation and Projection: A general non-linear dimensionality reduction algorithm implemented in Python
• Scikit-learn Contrib/HDBSCAN: A high performance implementation of HDBSCAN clustering, HDBSCAN is robust and easy-to-use clustering algorithm with very little parameters, Ideal for for exploratory data analysis; It works as an extension to Scikit-learn
• Turi Create: A fast tool/library for simplifying various ML tasks
• Scikit-learn-Contrib/Categorical-Encoding: An extension library for Scikit-learn that provides additional categorical feature encoding schemes(e.g. LeaveOneOut scheme)
• Optunity: A library for hyperparameter optimization
• Kmodes
• TF-Slim
• Pyro: "Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend" - Pyro's website
• GEM: A Python library that provides various graph embedding methods like 'node2vec' and 'locally linear embedding'
• DynamicGEM: A dynamic graph embedding library like GEM
• GraphSAGE: A graph embedding framework to generate low-dimensional vector representations for nodes, very useful if you need to use deep learning on graph data
• Horovd: A distributed training framework for TensorFlow, Keras, and PyTorch by Uber
• NetLSD: Python implementation of NetLSD, a scalable graph embedding algorithm for representing a graph via a low-dimensional vector

• SHAP: A tool for exploring and explaining outcome of an arbitrary model
• NLPre: Another cool Python NLP library
• GCN: Python implementation of graph convolutional networks in TensorFlow
• AllenNLP: "An open-source NLP research library, built on PyTorch" - AllenNLP's repository documentations
• TensorLy: A Python Library for efficient Tensor operations
• CuPy: A Python matrix library accelerated by Nvidia CUDA, it's also compatible with Numpy's API
• Scikit-Multiflow: A Python library for Stream Mining
• MLflow: A software toolbox to manage ML projects' workflow and life-cycle, it aims to make ML software projects easier to implement by providing various helper components for each steps
• pyGAM: A Python module for building Generalized Additive Models (GAMs)
• ggplot: "ggplot is a plotting system for Python based on R's ggplot2 and the Grammar of Graphics. It is built for making professionally looking, plots quickly with minimal code" - ggplot's website
• Linkpred: A Python package for link prediction on graphs
• SparklingGraph: A Python library to process large scale graphs using Spark and GraphX in a distributed manner
• OpenNE: An opensource network embedding library
• Galry: A high-performance visualization library in Python
• Dedupe: A Python library for fuzzy entity-resolution and record deduplication
• PyText: A deep-learning based NLP modeling framework built on top of PyTorch
• flair: A sate-of-the-art NLP framework in Python from Zalando
• NearPy: "A Python framework for fast (approximated) nearest neighbour search in large, high-dimensional data sets using different locality-sensitive hashes" according to its descriptions
• fastchunking: A (fast) text chunking algorithm implemented in C++ and Python
• Vaex: Vaex is a data manipulation library much like Pandas and Dask with a lazy out-of-core approach to handling the data so you can work with very huge tables with it
• openTSNE: An extensible, parallel implementation of t-SNE
• Faust: A stream processing library for Python
• Active Semi-Supervised Clustering: An extension library for scikit-learn that implements a collection of useful active semi-supervised clustering algorithms
• TextDistance: A Python library for calculating and comparing the distance between two sequences (such as text documents) with many algorithms
• Ray: A scalable. high-performance distributed execution framework for executing arbitrary Python functions on multiple machines, suitable for many ML workloads
• Pyitlib: An opensource library for calculating a useful collection of information-theoretic measures (i.e. Entropy) for discrete random variables
• KDEpy: A collection of useful kernel density estimators in Python 3.5+
• Tsfresh: A Python library for (automatic) feature extraction and engineering on time-dependent data
• GPy: A Python library for working with Gaussian processes
• Tslearn: A machine learning library dedicated to working with time-dependent data
• Ludwig: "Ludwig is a toolbox that allows to train and test deep learning models without the need to write code" - Ludwigs's website
• Record Linkage Toolkit: A Python software toolkit for record deduplication and linkage
• PyJanitor: Python port of R's janitor package, for data cleansing and manipulation
• FastText: A library for fast and efficient text embedding and classification
• Mimesis: A fast and useful fake data generation library
• PyOD: A Python software toolbox for scalable Outlier Detection (aka Anomaly Detection)
• Creme: A Python library for Online Learning and building incremental models
• vg: A linear algebra library much like Numpy with a more human-friendly interface
• GraphKernels: A fast library for calculating various graph kernels
• GraKeL: A graph kernel calculation library that is using scikit-learn's api so it can be used with other functionalities and routines already present in scikit-learn without much hassle
• Graphsim: A graph similarity extention libraries for NetworkX
• Textract: A general text extracttion tool from many file formats

• PyPy Python Implementation
• Useful Metrics
• XGboost Benchmarks
• Franchise Notebook
• Orange
• Weka: The famous Data Mining tool from where Kiwis live
• ELKI: A Data Mining software framework in Java
• Julia Programming Language: New language for Scientific Computing and HPC
• SQL Notebook
• IPython: An augmented Python shell with lots of features
• Incanter: A statistical analysis environment for a Lisp(for Clojure to be exact)
• Torch: Scientific Computing framework running on top of Lua's Just in Time compiler, brilliant idea!
• BPython: An advanced Python shell
• RAnalyticFlow: Great environment for Data Flow Programming in R
• SPMF: A Java Data Mining library with tons of cool algorithms
• SageMath: Open source math software system, a complete math environment for everyone
• H2O AI Platform: A software tool for Big Data Analysis, could be used for both Data Mining or Machine Learning tasks, It has tons of features
• Various ML Cheat Sheets
• OpenRefine: An open source data cleansing and refinement tool
• Deep Learning Papers
• Apache Mxnet: A high performance and scalable ANN framework for Deep Learning
• Material for the book 'Python for Data Analysis'
• Encog Machine Learning Framework: An ML library for Java and .NET with focus on ANN algorithms
• Apache Spark MLib: An ML library on top of your spark cluster!
• Awesome-Python: A very thorough list of Pythonic resources (libraries, frameworks, etc)
• GATE: A mature text processing toolkit in Java
• MALLET: "MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text." - MALLET's website
• MLPack: A fast ML library written in C++ with bindings to Python
• t-SNE: Implementation of famous t-distributed stochastic neighbor embedding algorithm for various languages
• Caffe
• Apache Singa
• CompLearn
• SNAP
• Apache PredictionIO
• JGraphT: A Java library for working with graphs with tonnes of features
• JGaphX: A Java library for diagramming and visualizing graphs
• Microsoft Distributed Machine Learning Toolkit
• Microsoft Cognitive Toolkit
• BIDMat: A both CPU and GPU-accelerated matrix library for data mining tasks
• BIDMach
• Apache SystemML
• Apache Mahout
• Accord.NET: Accord.NET is a Machine Learning framework written in C#, its API is available for .NET, it also comes combined with some audio and image processing libraries completely written in C#
• BitMAGIC Library
• Cassovary
• Dex: A nice Java based tool for Data Analysis and Data Mining
• Apache OpenNLP
• OpenNN: A C++ library to build complex neural network models
• MOA: A tool for mining stream data, by people who also created Weka
• MLPACK: C++ Machine Learning library for scalability, speed, and ease-of-use
• MOSES: "Moses is a statistical machine translation system that allows you to automatically train translation models for any language pair." - Moses's website
• Parallel Python: A Python module for parallel execution of code on SMP and Cluster environment
• BeautifulSoup: A handy Python library to digest almost anything from World Wild Web
• Wordbatch: A library for parallel feature extraction on textual data(and potentially other complex data types)
• Mypy: Static typing facilities for Python
• SKIL
• An unofficial Python extension package repository for Windows
• LIBOL: An online learning library
• Smile: "Smile is a fast and comprehensive machine learning system"- Smile's website
• Tablesaw: A daydreamer and visualization library for Java
• TensorFlow Models: A repository of models and examples built with TensorFlow
• Curated list of graph embedding methods: A collection of of paper-code pairs for state of the art graph embeddings(a.k.a network representational learning) algorithms
• Curated list of resources for Recommender Systems
• Pegasus: An open-source system for analyzing very large graphs, It seems it is not being developed or maintained for a long time
• Dataset: A handy tool to simplify the task of reading and writing to relational databases
• Twython: A Twitter API library in pure Python with tonnes of features
• Apache TinkerPop: A cool graph storage and computation framework, it can be used both as a graph analytics platform and a graph database system, love the little gremlins!
• Graphexp: Graphexp is a visual graph explorer with D3.js for TinkerPop
• Scilab: An open-source numerical computation language and environment, great Matlab alternative
• Glow: A compiler for Neural Network hardware accelerators for various hardware
• GraphJet: A real-time graph processing library in Java
• GraphDrawing: A very nice graph analysis and drawing library in Java
• Sketch Library: A C++ library for data summarization
• The Lemur Project: A collection of search engine, text processing and Data Mining tools and libraries in C++ and Java like RankLib for ranking
• VisPy: A Python library for interactive scientific visualization that is designed to be fast, scalable and easy to use
• Awesome Machine Learning: A curated list of awesome Machine Learning frameworks, libraries and software, etc
• MOA Framework: A fantastic Java software environment and framework for Stream Mining
• MEKA: A multi-label classification tool, it works on top of Weka
• Mulan: A Java library for learning on multi-label data
• Dlib: A fast Machine Learning library implemented in C++ for solving real-world data problems
• MITE: A library and tool for information extraction on text data, it's built on top of Dlib with binding for languages like Java and Python
• GraphStream: GraphStream is a Java library for analyzing and visualizing dynamic graphs
• Cytoscape: A complex network (graph) visualization tool in Java
• Gephi: A network visualization and analysis tool in Java
• SocNetV: A very handy social network visualization tool
• Visone: Yet another handy social network analysis and visualization tool
• Flashlight: A fast Machine Learning library in C++
• Machine Learning with Python: A collection of ML algorithms and their sample use-cases implemented in Python
• TANAGRA: "TANAGRA is a free DATA MINING software for academic and research purposes" its website
• KNIME: KNIME is an open source data analytics, reporting and data integration platform
• MG4J: An open-source high-performance full-text search engine written in Java
• WebGraph: A Java framework for working on very large graphs
• RTree: Reactive implementations of immutable in-memory R-tree and R*-tree in Java
• Recommender Systems: A useful repository of stuff all about the Recommender Systems (e.g. best practices to build Recommender Systems)
• Awesome-Graph: A curated list of resources (e.g., libraries, frameworks and databases) related to graphs
• Parallel Graph AnalytiX (PGX): A graph processing and analytics toolbox written in Java from Oracle
• ROOT: A scientific toolbox for data processing and analysis in C++
• Stanford Topic Modeling Toolbox (TMT): TMT is a nice Java toolkit for topic modeling on textual data
• Java Data Mining Package: An opensource Java package for mining massive datasets implementing a vast collection of algorithms (i.e. clustering, regression, classification and graphical models)
• ScalaNLP: A numerical computation and Data Mining library suite written in Scala, with an emphasis on NLP
• Vegas: A very flexible declarative data visualization library in Scala that works with Apache Spark right out of the box
• DeepLearning.scala: A simple Scala library for creating complex artificial neural networks by ThoughtWorks
• XAPIAN: An opensource search engine library with bindings to be used in many high-level programming languages, for example Python, Java, and Lua!
• DataMelt: "DataMelt is a free software for numeric computation, mathematics, statistics, symbolic calculations, data analysis and data visualization" - DataMelt's website
• Luna: A functional programming language to create data processing friendly programs in a WYSIWYG way
• NetLogo: A computational multi-agent development and simulation environment, very cool tool for investigating complex phenomena via implementing simple computational rules for agents!
• LabPlot: LabPlot is a lovely application for data analysis and plotting, it is part of KDE Project!
• Meta Toolkit: A fast software toolkit implementing many useful ML algorithms, it is written in C++
• Record Linkage Tools: A collection of useful resources for record deduplication and linkage
• Gunrock: A GPU based graph analytics and processing library, it works with CUDA
• Papers on Graph Analytics: A thorough list of publications related to graphs covering many interesting topics
• GraphIt: GraphIt - "A High-Performance Domain Specific Language for Graph Analytics" - GraphIt's website
• SMORe: A handy tool and library for fast weighted graph embedding in C++
• Warp-ctc: A fast parallel implementation of CTC, for both CPU and GPU
• Grew: Grew is a graph library and tool written in Ocaml with applications in NLP, it is a companion tool for the book Application of Graph Rewriting to Natural Language Processing
• ZVTM: A handy graph visualizatin library for Java
• mrJob: A Python library to create MapReduce jobs and run them on multiple machines (ie, in a cluster)

• Ali Rahimi's talk NIPS 2017: Good talk from someone inside the field
• Procrustes: How could we live without Wikipedia?
• Probably Approximately Correct
• Foundations of Machine Learning: A good book to start learning ML, A must for every ML enthusiast
• Scikit-Learn website: Scikit-learn's website itself is a great resource to learn!
• What Computers Still Can't Do: Some old and still valid criticisms of Strong AI!, Are AI and Alchemy the same?
• Readings in Database Systems(The Red Book): Really an enjoy to read, It's a little bit hard to follow at first for me but great many resources are mentioned at the end of each chapter and it gives great insights about the history, trends and future of DBMSs and Data Processing Platforms
• Kolmogorov Complexity: Let's compress everything!
• Machine Learning Meets Databases: A very informative and also easy to follow article, including a short introduction to Machine Learning and also describing its relation to Data Mining and Databases
• A gentle introduction to Tensors and their uses: An introduction to Tensors and their sample applications, Don't let the math scare you off!:0)
• Mining Massive Datasets: A very nice blend of theory and application for what can be done to data
• Networks, Crowds, and Markets: Reasoning About a Highly Connected World : Very insightful if like to know more about the interconnected world and networks