Machine Learning Fundamentals

Artificial Intelligence

Machine Learning

• A subset of AI that involves training machines to learn from data and make predictions or decisions without being explicitly programmed
• Types of Machine Learning:
  1. Supervised Learning: Training data is labeled and the algorithm learns to map inputs to outputs
  2. Unsupervised Learning: Training data is unlabeled and the algorithm finds patterns or relationships
  3. Reinforcement Learning: Algorithm learns through trial and error by receiving rewards or penalties
• Machine Learning applications:
  • Image and speech recognition
  • Natural Language Processing (NLP)
  • Predictive analytics
  • Robotics

Natural Language Processing (NLP)

• A subset of AI that deals with the interaction between computers and humans in natural language
• NLP Tasks:
  1. Tokenization: Breaking down text into individual words or tokens
  2. Part-of-Speech (POS) Tagging: Identifying the grammatical category of each word (e.g. noun, verb, adjective)
  3. Named Entity Recognition (NER): Identifying specific entities such as names, locations, and organizations
  4. Sentiment Analysis: Determining the emotional tone or sentiment behind text
• NLP Applications:
  • Language Translation
  • Text Summarization
  • Sentiment Analysis
  • Chatbots and Virtual Assistants

Artificial Intelligence

Machine Learning

• Machine learning is a subset of AI that enables machines to learn from data and make predictions or decisions without being explicitly programmed
• Supervised learning involves training data that is labeled, allowing the algorithm to learn to map inputs to outputs
• In unsupervised learning, the training data is unlabeled, and the algorithm finds patterns or relationships
• Reinforcement learning involves an algorithm learning through trial and error by receiving rewards or penalties
• Machine learning is used in image and speech recognition, natural language processing, predictive analytics, and robotics

Natural Language Processing (NLP)

What is NLP?

• NLP is a subset of AI that deals with the interaction between computers and humans in natural language

NLP Tasks

• Tokenization is the process of breaking down text into individual words or tokens
• Part-of-Speech (POS) tagging involves identifying the grammatical category of each word, such as noun, verb, adjective
• Named Entity Recognition (NER) involves identifying specific entities such as names, locations, and organizations
• Sentiment analysis involves determining the emotional tone or sentiment behind text

NLP Applications

• Language translation is a key application of NLP
• Text summarization is another application of NLP
• Sentiment analysis is used to determine the emotional tone of text
• Chatbots and virtual assistants rely on NLP to understand and respond to user queries

Learn about the basics of machine learning, including supervised, unsupervised, and reinforcement learning types. Understand how machines learn from data and make predictions or decisions.