Machine Learning: Supervised Learning

Definition: A type of machine learning where the model is trained on labeled data to learn the relationship between input data and output labels.
Goal: The model learns to predict the output label for new, unseen input data.
Types of problems:
- Regression: Predicting continuous values (e.g., stock prices, temperatures).
- Classification: Predicting categorical labels (e.g., spam/not spam emails, product categories).
Popular algorithms:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVMs)

Definition: A subfield of machine learning that uses neural networks with multiple layers to learn complex patterns in data.
Key concepts:
- Artificial neural networks
- Deep neural networks
- Convolutional Neural Networks (CNNs) for image processing
- Recurrent Neural Networks (RNNs) for sequential data
Applications:
- Image recognition and classification
- Natural Language Processing (NLP)
- Speech recognition
- Game playing (e.g., AlphaGo)

Definition: A type of machine learning where the model learns to take actions in an environment to maximize a reward signal.
Goal: The model learns to make decisions that lead to the highest cumulative reward over time.
Key concepts:
- Agent: The decision-making entity
- Environment: The external world that responds to the agent's actions
- Actions: The decisions made by the agent
- Reward: The feedback received from the environment
- Policy: The strategy learned by the agent
Applications:
- Robotics
- Game playing (e.g., Go, poker)
- Autonomous vehicles

Definition: A subfield of artificial intelligence that focuses on the interaction between computers and human language.
Key concepts:
- Text preprocessing
- Tokenization
- Part-of-speech tagging
- Named entity recognition
- Sentiment analysis
Applications:
- Sentiment analysis
- Language translation
- Text summarization
- Chatbots
- Speech recognition

Definition: A type of machine learning where the model is trained on unlabeled data to discover patterns or structure.
Goal: The model learns to identify clusters, dimensions, or anomalies in the data.
Types of problems:
- Clustering: Grouping similar data points into clusters.
- Dimensionality reduction: Reducing the number of features in the data while preserving information.
- Anomaly detection: Identifying outliers or unusual data points.
Popular algorithms:
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- t-SNE (t-Distributed Stochastic Neighbor Embedding)

Trained on labeled data to learn the relationship between input data and output labels.
Goal is to predict the output label for new, unseen input data.
Used for regression and classification problems.
Regression predicts continuous values, such as stock prices or temperatures.
Classification predicts categorical labels, such as spam/not spam emails or product categories.
Popular algorithms include Linear Regression, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVMs).

A subfield of machine learning using neural networks with multiple layers to learn complex patterns in data.
Key concepts include artificial neural networks, deep neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
CNNs are used for image processing, while RNNs are used for sequential data.
Applications include image recognition and classification, natural language processing (NLP), speech recognition, and game playing.

A type of machine learning where the model learns to take actions in an environment to maximize a reward signal.
Goal is to make decisions that lead to the highest cumulative reward over time.
Key concepts include agent, environment, actions, reward, and policy.
Agent is the decision-making entity, environment is the external world that responds to actions, and policy is the strategy learned by the agent.
Applications include robotics, game playing, and autonomous vehicles.

A subfield of artificial intelligence that focuses on the interaction between computers and human language.
Key concepts include text preprocessing, tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis.
Applications include sentiment analysis, language translation, text summarization, chatbots, and speech recognition.

A type of machine learning where the model is trained on unlabeled data to discover patterns or structure.
Goal is to identify clusters, dimensions, or anomalies in the data.
Used for clustering, dimensionality reduction, and anomaly detection.
Clustering groups similar data points into clusters, while dimensionality reduction reduces the number of features in the data while preserving information.
Popular algorithms include K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE (t-Distributed Stochastic Neighbor Embedding).