Resources to kick start your machine learning project. Prefered are free / open source, practical resources. The list is slighlty biased towards c# / .NET Framework.

Playgrounds:

• Tensor Flow playground
• Google AI Experiments
• Microsoft AI Experiments

Infographics:

• Neural Network Zoo
• A visual introduction to machine learning

Frameworks:

JS libraries

• brain.js
• convnetjs
• synaptic
• brain

APIs:

• Microsoft Cognitive Services
• Google ai solutions
• Google Cloud Machine Learning Engine
• Google Cloud Natural Language API
• IBM watson
• Azure Machine Learning Studio
• Clarifai
• Cloudsight

Comparisions:

• Comparison of deep learning software
• CUDA enabled deep learning frameworks
• Neural Net in 10 Frameworks
• The Deep Learning Framework Showdown: TensorFlow vs CNTK

Tutorials:

• Google's ML crash course Level: beginner. Cost: free
• Andrew Ng on Coursera Level: beginner. Cost: free
• Google ML tutorials Level: beginner. Cost: free
• Intro to AI Level: beginner. Cost: free
• Intro to Machine Learning Level: beginner. Cost: free
• Statistics and probability on Khan Academy Level: beginner. Cost: free
• Top 10 of ML algorithms
• The 10 Statistical Techniques Data Scientists Need to Master Short article on statistical learning
• Introduction to Machine Learning with ENCOG 3 Level: beginner. Cost: Pluralsight subsription
• Deep Learning with Keras Level: beginner. Cost: Pluralsight subsription
• TensorFlow: Getting Started Level: beginner. Cost: Pluralsight subsription
• Neural Networks for Machine Learning Level: beginner/advanced. Cost: free
• Learn Watson API on codeacademy Level: beginner/advanced. Cost: free
• UC Berkeley Intro to AI Level: beginner/advanced. Cost: free
• Hal Daumé III - A Course in Machine Learning Level: beginner/advanced. Cost: free
• Machine Learning by Georgia Tech Level: beginner/advanced. Cost: free
• Machine Learning for Trading by Georgia Tech Level: intermidiate. Cost: free
• Advanced Machine Learning with ENCOG Level: beginner/advanced. Cost: Pluralsight subsription
• Advanced Machine Learning with ENCOG - Part 2 Level: beginner/advanced. Cost: Pluralsight subsription
• Columbia University course Advanced, free course covering only ML algorithms (no ANN)
• Deep Learning byGoogle Advanced, deep-l;earning focused course
• MIT lectures "Deep Learning for Self-Driving Cars" Level: advanced. Cost: free
• Reinforcement learning by Georgia Tech Level: advanced. Cost: free
• Unsupervised Learning by Georgia Tech Level: advanced. Cost: free
• TensorFlow tutorials and best practices Level: advanced. Cost: free
• youtube playlist on Machine Learning and ANN Level: various. Cost: free
• Nvidia webinars
• Supervised Learning - Comprehensive Tutorial In depth, python based tutorial
• TensorFlow Tutorial: 10 minutes Practical TensorFlow lesson for quick learners
• An overview of gradient descent optimization algorithms Expert level, free

Example projects:

• Financial Predictor in Encog.Net
• Creator of AForge.NET (Accord.NET ANN core) sample applications
• Neural Network Back-Propagation Using C# succint example of Back-Propagation algorithm
• Deep Neural Networks: A Getting Started Tutorial code driven (c#) tutorial
• Back-Propagation Using C#
• NeuronDotNet library written in c# from scratch
• Convolutional Neural Network for Recognition of Handwritten Digits in C++
• CoLabolatory - TensorFlow Python hands-on It's not only project, but also free cloud, read what's great about it on hackernoon and towardsdatascience
• Yet another introduction to Neural Networks: neural network from scratch in Python
• Sentiment Analysis with the Naive Bayes Classifier in Python
• Logistic Regression in Python
• Perceptron in Python
• Time Series prediction with LSTM using C#
• A Simple C# Genetic Algorithm

Books:

• Machine Learning Using C# free
• Neural Networks Using C# free
• Clever Algorithms (Ruby) free
• Machine Learning Mastery Json's Brownlee page - blog is free, books are not. Though, the blog is immense source of knowledge, check out ex. beginners R tutorial
• Exploring Neural Networks with C#. Free edition in Polish
• Machine Learning For Hackers (R). Code for the book
• Metody i Techniki Sztucznej Inteigencji (Polish)

Hardware:

• GPU for deep learning
• DIGITS DevBox
• Neurogrid

Similar lists:

• deeplearning.net
• datasciencecentral.com
• mbadry1/Top-Deep-Learning
• josephmisiti/awesome-machine-learning
• ujjwalkarn/Machine-Learning-Tutorials
• ZuzooVn/machine-learning-for-software-engineers
• arbox/machine-learning-with-ruby
• chasingbob/deep-learning-resources
• memo/ai-resources
• dhamaniasad/awesome-machine-learning
• off99555/machine-learning-curriculum
• aikorea/awesome-rl
• endymecy/awesome-deeplearning-resources
• wangruinju/Deep-Learning
• bt3gl/Machine-Learning-Resources
• guillaume-chevalier/Awesome-Deep-Learning-Resources
• h2oai/mli-resources
• CodingTrain/Machine-Learning
• GeorgeMcIntire/collection_free_DL_resources
• miguelsaddress/machine-learning-resources
• eyurtsev/ml-resources
• 20 ml projects

Pull requests will be accepted after successful review