My curated list of Machine Learning resources, including books, papers, softwares, 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 (2nd 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

• 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!

• 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
• Reddit Machine Learning Community
• CrowdAI
• Quora
• Github.com: Github contains many useful resources such as code for many algorithms
• Apache Projects
• Stanford Machine Learning Course(Have a look at the project section!)
• NIPS Website
• 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

• 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

• 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 emphesis 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 tesselations
• 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
• GEM: A Python library that provides various graph embedding methods like 'node2vec' and 'locally linear embedding'
• 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
• PyGraphhistry: A Python library to extract, transform, and visually explore big graphs
• 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

• 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 scalbale 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
• 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
• Scilab: An open-source numerical computation language and environment, great Matlab alternative
• Glow: A compiler for Neural Network hardware accelerators for various hardwares
• 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

• 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
• A list of ML Conferences on 2018
• 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)