Machine Learning Fundamentals

Study Notes

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.

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.

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.

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.

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

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.

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.