Machine Learning
Algorithms that learn patterns from data to make predictions and decisions
Types of Machine Learning
Supervised Learning
Learning from labeled training data to make predictions
Common Applications:
Classification
Regression
Time Series Forecasting
Algorithms:
Linear Regression
Decision Trees
Random Forest
SVM
Neural Networks
Unsupervised Learning
Finding patterns in data without labeled examples
Common Applications:
Clustering
Dimensionality Reduction
Anomaly Detection
Algorithms:
K-Means
Hierarchical Clustering
PCA
t-SNE
Autoencoders
Reinforcement Learning
Learning through interaction with environment via rewards
Common Applications:
Game Playing
Robotics
Autonomous Vehicles
Algorithms:
Q-Learning
Policy Gradient
Actor-Critic
Deep Q-Networks
Key Concepts
Training vs Testing
Splitting data to train models and evaluate performance on unseen data
Overfitting vs Underfitting
Balance between model complexity and generalization ability
Bias-Variance Tradeoff
Fundamental tradeoff in ML between bias and variance in predictions
Cross-Validation
Technique to assess model performance and select hyperparameters
Feature Engineering
Process of selecting and transforming variables for ML models
Model Evaluation
Metrics and methods to assess model performance and quality