Machine Learning Fundamentals

Definition: A subset of AI that involves training machines to learn from data and make predictions or decisions without being explicitly programmed.
Types:
- Supervised Learning: The machine is trained on labeled data to learn the relationship between input and output.
- Unsupervised Learning: The machine is trained on unlabeled data to discover patterns and relationships.
- Reinforcement Learning: The machine learns through trial and error by receiving rewards or penalties for its actions.
Algorithms:
- Linear Regression: A linear model that predicts a continuous output variable.
- Decision Trees: A tree-based model that splits data into subsets based on features.
- Neural Networks: A model inspired by the human brain that uses interconnected nodes to learn complex patterns.
Applications:
- Image Classification: Classifying images into categories based on their features.
- Speech Recognition: Recognizing spoken words and phrases to transcribe or take action.
- Recommendation Systems: Suggesting personalized products or services based on user behavior.

Definition: A subfield of AI that deals with the interaction between computers and human language.
Tasks:
- Language Translation: Translating text from one language to another.
- ** Sentiment Analysis**: Analyzing text to determine the sentiment or emotion behind it.
- Text Summarization: Summarizing large documents or articles into concise summaries.
Techniques:
- Tokenization: Breaking down text into individual words or tokens.
- Part-of-Speech Tagging: Identifying the grammatical category of each word.
- Named Entity Recognition: Identifying and categorizing named entities in text.
Applications:
- Chatbots: Conversational interfaces that use NLP to understand and respond to user input.
- Language Translation Software: Software that translates text and speech in real-time.
- Speech-to-Text Systems: Systems that transcribe spoken words into written text.

Definition: A field of AI that focuses on enabling computers to interpret and understand visual information from the world.
Tasks:
- Image Recognition: Identifying objects, people, and scenes within images.
- Object Detection: Detecting and locating specific objects within images.
- Image Segmentation: Dividing images into their constituent parts or objects.
Techniques:
- Convolutional Neural Networks (CNNs): A type of neural network that excels in image recognition tasks.
- Feature Extraction: Extracting relevant features from images to aid in recognition.
- Edge Detection: Identifying the boundaries between objects in an image.
Applications:
- Self-Driving Cars: Using computer vision to detect and respond to the environment.
- Image Search Engines: Searching for images based on their content and features.
- Facial Recognition Systems: Identifying individuals based on their facial features.

Definition: 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:
Supervised Learning: Trained on labeled data to learn the relationship between input and output.
Unsupervised Learning: Trained on unlabeled data to discover patterns and relationships.
Reinforcement Learning: Learns through trial and error by receiving rewards or penalties for its actions.
Algorithms:
Linear Regression: Predicts a continuous output variable using a linear model.
Decision Trees: Splits data into subsets based on features using a tree-based model.
Neural Networks: Uses interconnected nodes to learn complex patterns, inspired by the human brain.
Applications:
Image Classification: Classifies images into categories based on their features.
Speech Recognition: Recognizes spoken words and phrases to transcribe or take action.
Recommendation Systems: Suggests personalized products or services based on user behavior.

Definition: A subfield of AI that deals with the interaction between computers and human language.
Tasks:
Language Translation: Translates text from one language to another.
Sentiment Analysis: Analyzes text to determine the sentiment or emotion behind it.
Text Summarization: Summarizes large documents or articles into concise summaries.
Techniques:
Tokenization: Breaks down text into individual words or tokens.
Part-of-Speech Tagging: Identifies the grammatical category of each word.
Named Entity Recognition: Identifies and categorizes named entities in text.
Applications:
Chatbots: Conversational interfaces that use NLP to understand and respond to user input.
Language Translation Software: Software that translates text and speech in real-time.
Speech-to-Text Systems: Systems that transcribe spoken words into written text.

Definition: A field of AI that focuses on enabling computers to interpret and understand visual information from the world.
Tasks:
Image Recognition: Identifies objects, people, and scenes within images.
Object Detection: Detects and locates specific objects within images.
Image Segmentation: Divides images into their constituent parts or objects.
Techniques:
Convolutional Neural Networks (CNNs): A type of neural network that excels in image recognition tasks.
Feature Extraction: Extracts relevant features from images to aid in recognition.
Edge Detection: Identifies the boundaries between objects in an image.
Applications:
Self-Driving Cars: Uses computer vision to detect and respond to the environment.
Image Search Engines: Searches for images based on their content and features.
Facial Recognition Systems: Identifies individuals based on their facial features.