Machine Learning Fundamentals

Artificial Intelligence

Machine Learning

• Definition: A subset of AI that enables machines to learn from data and improve their performance on a task without being explicitly programmed.
• Types of Machine Learning:
  • Supervised Learning: The machine is trained on labeled data to learn a mapping between input and output.
  • Unsupervised Learning: The machine is trained on unlabeled data to discover patterns and relationships.
  • Reinforcement Learning: The machine learns by interacting with an environment and receiving rewards or penalties for its actions.
• Machine Learning Algorithms:
  • Decision Trees: A tree-based model for classification and regression tasks.
  • Neural Networks: A model inspired by the structure and function of the human brain, used for tasks such as image recognition and natural language processing.
  • Support Vector Machines: A model that finds the hyperplane that maximally separates classes in the feature space.

Natural Language Processing (NLP)

• Definition: A subfield of AI that deals with the interaction between computers and human language.
• NLP Tasks:
  • Tokenization: Breaking down text into individual words or tokens.
  • Part-of-Speech (POS) Tagging: Identifying the grammatical category of each word (e.g., noun, verb, adjective).
  • Named Entity Recognition (NER): Identifying named entities in text (e.g., people, organizations, locations).
  • Sentiment Analysis: Determining the emotional tone or sentiment of text (e.g., positive, negative, neutral).
• NLP Applications:
  • Language Translation: Translating text from one language to another.
  • Text Summarization: Automatically summarizing large documents or articles.
  • Chatbots: Computer programs that simulate human-like conversations with users.

Artificial Intelligence

Machine Learning

• Machine learning is a subset of AI that enables machines to learn from data and improve their performance on a task without being explicitly programmed.
• There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
• Supervised learning involves training on labeled data to learn a mapping between input and output.
• Unsupervised learning involves training on unlabeled data to discover patterns and relationships.
• Reinforcement learning involves learning by interacting with an environment and receiving rewards or penalties for actions.
• Decision trees are a type of machine learning algorithm used for classification and regression tasks.
• Neural networks are a type of machine learning algorithm inspired by the structure and function of the human brain, used for tasks such as image recognition and natural language processing.
• Support vector machines are a type of machine learning algorithm that finds the hyperplane that maximally separates classes in the feature space.

Natural Language Processing (NLP)

• NLP is a subfield of AI that deals with the interaction between computers and human language.
• Tokenization is the process of breaking down text into individual words or tokens.
• Part-of-speech (POS) tagging is the process of identifying the grammatical category of each word (e.g., noun, verb, adjective).
• Named entity recognition (NER) is the process of identifying named entities in text (e.g., people, organizations, locations).
• Sentiment analysis is the process of determining the emotional tone or sentiment of text (e.g., positive, negative, neutral).
• Language translation is an application of NLP that involves translating text from one language to another.
• Text summarization is an application of NLP that involves automatically summarizing large documents or articles.
• Chatbots are computer programs that simulate human-like conversations with users, and are an application of NLP.

Whole Numbers

Definition

• A whole number is a non-negative integer, including 0, without a fractional part.
• Whole numbers are also known as non-negative integers.

Properties

• Whole numbers are closed under addition and multiplication.
• The result of adding or multiplying two whole numbers is always a whole number.
• Whole numbers are not closed under subtraction, as the result can be a negative integer.
• Whole numbers are not closed under division, as the result can be a fraction.

Examples

• 0, 1, 2, 3,... are all whole numbers.
• The set of whole numbers is often denoted as W or ℤ₀₊.

Operations

• Addition: Result is always a whole number.
• Multiplication: Result is always a whole number.
• Subtraction: Result can be a negative integer or a whole number.
• Division: Result can be a fraction or a whole number.

Real-World Applications

• Counting objects: Whole numbers are used to count objects, people, or items.
• Measurement: Whole numbers are used to measure quantities, such as length, weight, or time.
• Finance: Whole numbers are used to represent amounts of money, quantities of goods, or numbers of people.