Machine Learning: Supervised Learning

Type of machine learning where the model is trained on labeled data
Goal is to learn a mapping between input data and output labels
Supervised learning tasks:
- Regression: predict continuous output variable (e.g. house prices)
- Classification: predict categorical output variable (e.g. spam/not spam emails)
Common algorithms:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVMs)

Subfield of machine learning that involves neural networks with multiple layers
Inspired by structure and function of the human brain
Capabilities:
- Image recognition
- Speech recognition
- Natural Language Processing (NLP)
- Game playing (e.g. Go, Poker)
Common deep learning algorithms:
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) networks
- Generative Adversarial Networks (GANs)

Subfield of artificial intelligence that deals with human-computer interaction
Focus on understanding, generating, and processing human language
NLP tasks:
- Sentiment Analysis
- Language Translation
- Text Summarization
- Named Entity Recognition
Common NLP techniques:
- Tokenization
- Part-of-Speech (POS) tagging
- Named Entity Recognition (NER)
- Dependency Parsing

Type of machine learning where the model is trained on unlabeled data
Goal is to discover patterns or structure in the data
Unsupervised learning tasks:
- Clustering: group similar data points together
- Dimensionality Reduction: reduce number of features in the data
- Anomaly Detection: identify unusual data points
Common algorithms:
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)

Type of machine learning where the model learns through trial and error
Goal is to maximize reward signal in a complex, uncertain environment
Reinforcement learning tasks:
- Game playing (e.g. Atari games, Go)
- Robotics: control robots to perform tasks
- Recommendation systems: personalize recommendations
Common algorithms:
- Q-Learning
- SARSA
- Deep Q-Networks (DQN)
- Policy Gradient Methods

Trained on labeled data, the model learns to map inputs to outputs
Goal: learn a mapping between input data and output labels
Tasks:
- Regression: predict continuous output variable (e.g., house prices, stock prices)
- Classification: predict categorical output variable (e.g., spam/not spam emails, cancer diagnosis)
Algorithms:
- Linear Regression: linear relationship between input and output variables
- Logistic Regression: binary classification, outputs probability of an event
- Decision Trees: tree-based model, splits data into subsets based on features
- Random Forest: ensemble of decision trees, improves accuracy and reduces overfitting
- Support Vector Machines (SVMs): finds hyperplane that separates classes with maximum margin

Involves neural networks with multiple layers, inspired by the human brain
Capabilities:
- Image recognition: objects, scenes, and activities
- Speech recognition: transcribe spoken words into text
- Natural Language Processing (NLP): language understanding, generation, and processing
- Game playing: Go, Poker, and other complex games
Algorithms:
- Convolutional Neural Networks (CNNs): image recognition, object detection
- Recurrent Neural Networks (RNNs): sequential data, language modeling, and machine translation
- Long Short-Term Memory (LSTM) networks: handles vanishing gradients, better for long-term dependencies
- Generative Adversarial Networks (GANs): generate new data samples, images, and videos

Deals with human-computer interaction, understanding, generating, and processing human language
Tasks:
- Sentiment Analysis: determine sentiment (positive, negative, neutral) of text
- Language Translation: translate text from one language to another
- Text Summarization: summarize long documents, extracting key points
- Named Entity Recognition: identify named entities (people, places, organizations)
Techniques:
- Tokenization: split text into individual words or tokens
- Part-of-Speech (POS) tagging: identify grammatical categories (noun, verb, adjective)
- Named Entity Recognition (NER): identify and classify named entities
- Dependency Parsing: analyze sentence structure, identify subject, object, and modifiers

Trained on unlabeled data, the model discovers patterns or structure
Goal: identify hidden patterns, group similar data points, or reduce dimensionality
Tasks:
- Clustering: group similar data points together (customer segmentation, gene expression)
- Dimensionality Reduction: reduce number of features in the data (e.g., PCA, t-SNE)
- Anomaly Detection: identify unusual data points (fraud detection, network intrusion)
Algorithms:
- K-Means Clustering: partition data into K clusters based on similarity
- Hierarchical Clustering: build a hierarchy of clusters, visualize relationships
- Principal Component Analysis (PCA): reduce dimensionality, retain most information
- t-Distributed Stochastic Neighbor Embedding (t-SNE): non-linear dimensionality reduction, better for complex data

Trained through trial and error, the model learns to maximize a reward signal
Goal: learn to take actions in a complex, uncertain environment
Tasks:
- Game playing: learn to play games like Atari, Go, or Poker
- Robotics: control robots to perform tasks, learn from experience
- Recommendation systems: personalize recommendations, maximize user engagement
Algorithms:
- Q-Learning: updates action-value function, learns to predict expected rewards
- SARSA: updates state-value function, learns to predict expected rewards and next state
- Deep Q-Networks (DQN): combines Q-learning with neural networks, handles large state-action spaces
- Policy Gradient Methods: learns the optimal policy, directly updates policy parameters