Introduction to Machine Learning

Machine Learning

• Definition: Machine learning is a subset of Artificial Intelligence (AI) that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed.
• Types of Machine Learning:
  • Supervised Learning: The algorithm is trained on labeled data to learn the relationship between input and output.
  • Unsupervised Learning: The algorithm is trained on unlabeled data to discover patterns or relationships.
  • Reinforcement Learning: The algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties.
• Key Concepts:
  • Model: A mathematical representation of the relationship between input and output.
  • Training: The process of feeding data to the algorithm to learn from it.
  • Testing: The process of evaluating the performance of the model on unseen data.
  • Overfitting: When the model becomes too complex and performs well on the training data but poorly on new data.
  • Underfitting: When the model is too simple and fails to capture the underlying patterns in the data.

Natural Language Processing (NLP)

• Definition: NLP is a subfield of AI that deals with the interaction between computers and human language.
• Key Concepts:
  • Tokenization: The process of 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 and categorizing named entities (e.g. people, organizations, locations).
  • Sentiment Analysis: Determining the emotional tone or sentiment of text (e.g. positive, negative, neutral).
• NLP Applications:
  • Text Classification: Classifying text into categories (e.g. spam vs. non-spam emails).
  • Language Translation: Translating text from one language to another.
  • Chatbots: Computer programs that simulate human-like conversations.
  • Speech Recognition: Recognizing spoken language and transcribing it into text.

Machine Learning

• Machine learning is a subset of Artificial Intelligence (AI) that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed.
• There are three types of machine learning:
  • Supervised Learning: algorithm is trained on labeled data to learn the relationship between input and output.
  • Unsupervised Learning: algorithm is trained on unlabeled data to discover patterns or relationships.
  • Reinforcement Learning: algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties.
• A model is a mathematical representation of the relationship between input and output.
• Training involves feeding data to the algorithm to learn from it.
• Testing evaluates the performance of the model on unseen data.
• Overfitting occurs when the model becomes too complex and performs well on the training data but poorly on new data.
• Underfitting occurs when the model is too simple and fails to capture the underlying patterns in the data.

Natural Language Processing (NLP)

• NLP is a subfield of AI that deals with the interaction between computers and human language.
• Tokenization involves breaking down text into individual words or tokens.
• Part-of-Speech (POS) Tagging identifies the grammatical category of each word (e.g. noun, verb, adjective).
• Named Entity Recognition (NER) identifies and categorizes named entities (e.g. people, organizations, locations).
• Sentiment Analysis determines the emotional tone or sentiment of text (e.g. positive, negative, neutral).
• NLP applications include:
  • Text Classification: classifying text into categories (e.g. spam vs. non-spam emails).
  • Language Translation: translating text from one language to another.
  • Chatbots: computer programs that simulate human-like conversations.
  • Speech Recognition: recognizing spoken language and transcribing it into text.