Machine Learning is a branch of artificial intelligence that focuses on building systems that can learn from data. This introduction course will provide a comprehensive overview of the field, covering key concepts, techniques, and applications.
Key Concepts
- Supervised Learning: A type of machine learning where the model learns from labeled training data.
- Unsupervised Learning: A type of machine learning where the model learns from unlabeled data.
- Reinforcement Learning: A type of machine learning where the model learns by performing actions and receiving feedback.
Techniques
- Neural Networks: A series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
- Support Vector Machines (SVM): A supervised learning model that can be used for classification or regression tasks.
- Decision Trees: A flowchart-like tree structure where an internal node represents a feature or attribute, the branch represents a decision rule, and each leaf node represents an outcome.
Applications
- Natural Language Processing (NLP): Using machine learning to process and analyze large amounts of natural language data.
- Computer Vision: Using machine learning to enable computers to interpret and understand visual information from the world.
- Predictive Analytics: Using machine learning to analyze current and historical data to make predictions about future events.
Machine Learning
For more information on machine learning, check out our Introduction to Deep Learning course.