Natural Language Processing (NLP) Fundamentals

Subfield of Artificial Intelligence (AI) that deals with the interaction between computers and human language
Enables computers to understand, interpret, and generate human language

Language Understanding: enable computers to comprehend human language, including semantics, syntax, and pragmatics
Language Generation: enable computers to generate human-like language, including text and speech

Tokenization: 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 named entities in text, such as people, places, and organizations
Sentiment Analysis: determining the emotional tone or sentiment of text, such as positive, negative, or neutral

Rule-based Approach: using pre-defined rules to analyze and generate language
Statistical Approach: using machine learning algorithms to analyze and generate language based on statistical patterns
Deep Learning Approach: using neural networks to analyze and generate language

Chatbots: computer programs that converse with humans using natural language
Language Translation: translating text or speech from one language to another
Sentiment Analysis: analyzing customer feedback, reviews, and social media posts to determine sentiment
Text Summarization: automatically summarizing large documents or articles

Ambiguity: dealing with ambiguous words or phrases that have multiple meanings
Contextual Understanding: understanding the context in which language is being used
Common Sense: lacking common sense and real-world knowledge that humans take for granted
Explainability: difficulty in explaining the decisions made by AI models in NLP

NLP is a subfield of Artificial Intelligence (AI) that deals with the interaction between computers and human language.
Enables computers to understand, interpret, and generate human language.

Language Understanding: comprehend human language, including semantics, syntax, and pragmatics.
Language Generation: generate human-like language, including text and speech.

Tokenization: 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 named entities in text, such as people, places, and organizations.
Sentiment Analysis: determining the emotional tone or sentiment of text, such as positive, negative, or neutral.

Rule-based Approach: using pre-defined rules to analyze and generate language.
Statistical Approach: using machine learning algorithms to analyze and generate language based on statistical patterns.
Deep Learning Approach: using neural networks to analyze and generate language.

Chatbots: computer programs that converse with humans using natural language.
Language Translation: translating text or speech from one language to another.
Sentiment Analysis: analyzing customer feedback, reviews, and social media posts to determine sentiment.
Text Summarization: automatically summarizing large documents or articles.

Ambiguity: dealing with ambiguous words or phrases that have multiple meanings.
Contextual Understanding: understanding the context in which language is being used.
Common Sense: lacking common sense and real-world knowledge that humans take for granted.
Explainability: difficulty in explaining the decisions made by AI models in NLP.

Encompasses a subset of Artificial Intelligence (AI) that enables machines to learn from data and improve performance on a task without being explicitly programmed
Includes three primary types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning

Subfield of AI that deals with the interaction between computers and humans in natural language
Goals include:
- Language understanding: machine comprehension of human language
- Language generation: machine production of human-like language
Techniques include:
- Tokenization: breaking down text into individual words or tokens
- Part-of-speech tagging: identifying grammatical categories of words
- Named Entity Recognition (NER): identifying named entities in text
Applications include:
- Sentiment analysis and opinion mining
- Language translation and localization
- Chatbots and virtual assistants

Subfield of AI that focuses on enabling computers to interpret and understand visual information from the world
Goals include:
- Image classification: identifying objects within images
- Object detection: locating objects within images
- Image segmentation: separating objects from the background
Techniques include:
- Convolutional Neural Networks (CNNs): using neural networks to analyze images
- Edge detection: identifying boundaries between objects
- Optical character recognition (OCR): recognizing text within images
Applications include:
- Image recognition and classification
- Object recognition and tracking
- Autonomous vehicles and robotics

Computational models inspired by the structure and function of the human brain
Components include:
- Artificial neurons (nodes): process and transmit information
- Connections (edges): transmit signals between neurons
Types include:
- Feedforward Networks: information flows only in one direction
- Recurrent Neural Networks (RNNs): information flows in a loop
Applications include:
- Function approximation and regression
- Pattern recognition and classification
- Time series forecasting and prediction

Subset of Machine Learning that uses Neural Networks with multiple layers to analyze data
Characteristics include:
- Multiple layers of abstraction: learning complex patterns and representations
- Automatic feature learning: extracting relevant features from data
Techniques include:
- Backpropagation: training neural networks using gradient descent
- Activation functions: introducing non-linearity into neural networks
- Regularization techniques: preventing overfitting
Applications include:
- Image recognition and classification
- Natural Language Processing (NLP)
- Speech recognition and synthesis