Introduction to Machine Learning

Study Notes

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.

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 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.

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.

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