Machine Learning Basics

Definition: A subset of Artificial Intelligence (AI) that enables machines to learn from data without being explicitly programmed.
Types:
- Supervised Learning: Models learn from labeled data to make predictions.
- Unsupervised Learning: Models discover patterns in unlabeled data.
- Reinforcement Learning: Models learn from trial and error through rewards or penalties.
Key Concepts:
- Training Data: Data used to train the model.
- Model: The algorithm or system that makes predictions.
- Overfitting: When a model is too complex and performs well on training data but poorly on new data.

Definition: A subfield of AI that deals with the interaction between computers and humans in natural language.
Key Concepts:
- Tokenization: Breaking down text into individual words or tokens.
- Named Entity Recognition: Identifying named entities (e.g., people, places, organizations) in text.
- Sentiment Analysis: Determining the emotional tone or sentiment of text.
Applications:
- Chatbots: Conversational interfaces that use NLP to understand and respond to user input.
- Language Translation: Computer-aided translation of languages.

Definition: A subset of Machine Learning that uses neural networks with multiple layers to analyze data.
Key Concepts:
- Neural Networks: Models composed of interconnected nodes (neurons) that process data.
- Activation Functions: Mathematical functions used to introduce non-linearity into neural networks.
- Backpropagation: An optimization algorithm used to train neural networks.
Applications:
- Image Recognition: Identifying objects within images using convolutional neural networks (CNNs).
- Speech Recognition: Transcribing spoken language into text using recurrent neural networks (RNNs).

Definition: A machine learning model inspired by the structure and function of the human brain.
Components:
- Input Layer: Receives input data.
- Hidden Layers: Process and transform input data.
- Output Layer: Produces the predicted output.
Types:
- Feedforward Networks: Data flows only in one direction, from input to output.
- Recurrent Neural Networks (RNNs): Data can flow in a loop, allowing the model to keep state.

Definition: A type of AI that generates new, original content, such as images, music, or text.
Techniques:
- Generative Adversarial Networks (GANs): Two neural networks work together to generate new content.
- Variational Autoencoders (VAEs): Neural networks that learn to compress and reconstruct data.
Applications:
- Art Generation: Creating original artwork using AI algorithms.
- Text Generation: Generating human-like text, such as chatbot responses or article writing.

A subset of Artificial Intelligence (AI) that enables machines to learn from data without being explicitly programmed.
Types of Machine Learning:
- Supervised Learning: Models learn from labeled data to make predictions.
- Unsupervised Learning: Models discover patterns in unlabeled data.
- Reinforcement Learning: Models learn from trial and error through rewards or penalties.
Key Concepts:
- Training Data: Data used to train the model.
- Model: The algorithm or system that makes predictions.
- Overfitting: When a model is too complex and performs well on training data but poorly on new data.

A subfield of AI that deals with the interaction between computers and humans in natural language.
Key Concepts:
- Tokenization: Breaking down text into individual words or tokens.
- Named Entity Recognition: Identifying named entities (e.g., people, places, organizations) in text.
- Sentiment Analysis: Determining the emotional tone or sentiment of text.
Applications:
- Chatbots: Conversational interfaces that use NLP to understand and respond to user input.
- Language Translation: Computer-aided translation of languages.

A subset of Machine Learning that uses neural networks with multiple layers to analyze data.
Key Concepts:
- Neural Networks: Models composed of interconnected nodes (neurons) that process data.
- Activation Functions: Mathematical functions used to introduce non-linearity into neural networks.
- Backpropagation: An optimization algorithm used to train neural networks.
Applications:
- Image Recognition: Identifying objects within images using convolutional neural networks (CNNs).
- Speech Recognition: Transcribing spoken language into text using recurrent neural networks (RNNs).

A machine learning model inspired by the structure and function of the human brain.
Components:
- Input Layer: Receives input data.
- Hidden Layers: Process and transform input data.
- Output Layer: Produces the predicted output.
Types:
- Feedforward Networks: Data flows only in one direction, from input to output.
- Recurrent Neural Networks (RNNs): Data can flow in a loop, allowing the model to keep state.

A type of AI that generates new, original content, such as images, music, or text.
Techniques:
- Generative Adversarial Networks (GANs): Two neural networks work together to generate new content.
- Variational Autoencoders (VAEs): Neural networks that learn to compress and reconstruct data.
Applications:
- Art Generation: Creating original artwork using AI algorithms.
- Text Generation: Generating human-like text, such as chatbot responses or article writing.