Machine Learning (ML)

A computer system that learns and adapts without explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.

• AI and Data Scientist Roadmap
• Standford Recommender Systems Chapter 9
• Data Preparation and Feature Engineering in ML  |  Machine Learning  |  Google for Developers
• Data Preparation

Different Types of ML

Some types of machine learning

• Supervised Learning
• Unsupervised Learning
• Semi-Supervised Learning
• Reinforcement Learning
• Inductive Inference
• Active Machine Learning
• Online Machine Learning
• Transfer Machine Learning

— From 7. Machine Learning Tasks and Types

ML Metrics

• Accuracy, Precision & Recall, F1-Score
• Sensitivity (percentage correctly found)
• Specificity (percentage correctly rejected)

Source: MIT - ML Introduction

ML Model Evaluation

• Dataset Splitting

ML Models

• Convolutional Neural Network
• Generative Adversarial Network
• Transformer
• Variational Autoencoder

ML Problems

• Underfitting
• Data drift
• Catastrophic interference
• Reproducibility Crisis
• ML Reproducability