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Machine Learning (ML)

Last updated Mar 30, 2023 Edit Source

The use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data

# Resources

# Cheatsheets and notes

# Naive/homemade implementations

# Open datasets (for ML, DL and DS)

See AI/DS and DataEng/Open ML data

# Books

# Courses

# Code

# References

# Subtopics

# Feature selection

See AI/Supervised Learning/Feature selection

# Feature learning

See AI/Feature learning

# Anomaly and Outlier Detection

See AI/Anomaly and Outlier Detection

# Time Series analysis and forecasting

See AI/Time Series analysis and AI/Forecasting

# AutoML

See AI/AutoML

# Deep Learning

See AI/Deep learning/DL

# Reinforcement learning

See AI/Reinforcement learning

# Unsupervised learning

See AI/Unsupervised learning/Unsupervised learning

# Supervised learning

See AI/Supervised Learning/Supervised learning

# Weakly-supervised learning

See AI/Weakly-supervised learning. It includes these topics: AI/Semi-supervised learning, AI/Active learning and AI/Transfer learning

# One, few-shot learning

See AI/One, few-shot learning

# Self-supervised learning

See AI/Self-supervised learning

# Learning to rank and ordinal regression

See AI/Learning to rank

# Multi task learning

See AI/Multi-task learning

# Generative modelling

See AI/Deep learning/Generative modelling

# Explainable AI

See AI/XAI

# Federated learning

See AI/Federated learning

# Quantum ML

See AI/QML