A repository of quick reference guides and walkthroughs to the most popular machine learning algorithms, that are worth knowing about.
Machine Learning Algorithm categories:
a) Supervised Learning: Data-driven systems that rely on example inputs to produce a prediction or classification
b) Unsupervised Learning: Data-driven systems that try to identify existing patterns by discovering natural groupings in the data
c) Reinforcement Learning: An AI system that tries to select actions based on estimated long-term rewards, as applied to cryptocurrency trading
d) Semi-Supervised Learning – Combines supervised learning with unsupervised learning and reinforcement learning
e) Active Beta Models – The estimation of models where we set subjective priors over the distributions of the model parameters; typically our priors are informed opinions about what the best parameter values are likely to be
f) Bootstrap Aggressive Coordinate Descent (BACD): A coordinate descent algorithm which does not require performing
Machine Learning is one of the major applications of Artificial Intelligence. It’s technology encompassing all the other types of Artificial Intelligence like deep learning, neural networks, etc.
The Machine Learning Algorithms Cheat Sheet is an interactive resource created by Mario Robles (full AI enthusiast) in order to provide a quick reference to the fundamental concepts and things you should learn about Machine Learning algorithms for totally self-driving development
Each cell in this machine learning cheat sheet helps you find out whether a desired property does or does not belong to that algorithm
The materials from this cheat sheet cover everything from back propagation, grid search, samples, weight initialization and stopping criteria.
The Machine Learning algorithms cheat sheet contains common algorithms that are used in machine learning applications.