Artificial Intelligence in Data Science
Utilizing AI techniques to automate data analysis, uncover patterns, and enhance predictive modeling.
Machine Learning (ML)
A subset of AI that enables systems to learn from data and improve over time without being explicitly programmed. ML is commonly categorized into:
- Supervised Learning: Learns from labeled data to make predictions (e.g., classification, regression).
- Unsupervised Learning: Identifies hidden patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learns through trial and error by receiving rewards or penalties based on actions.
Deep Learning
A specialized branch of machine learning that uses multi-layered neural networks to handle complex tasks such as:
- Image and speech recognition
- Natural language understanding
- Autonomous systems
Data Analysis
The process of inspecting, cleaning, and modeling data to extract useful insights using statistical and computational methods.
Natural Language Processing (NLP)
An interdisciplinary field focused on enabling machines to understand, interpret, and generate human language. Applications include:
- Sentiment analysis
- Machine translation
- Chatbots and virtual assistants