We'll start with describing what machine learning is. While introducing one simple machine learning method, linear regression + gradient descent, we'll introduce the main components of learning: representation, evaluation and optimization. Thereafter, we'll also describe various sub-types of machine learning problems (supervised vs unsupervised) and other important concepts (overfitting). This section ends with a visual review of these concepts and a note about a lot of folk wisdom that people have learnt over the years.
- An introduction to Machine Learning
- Algorithms for Supervised Learning
- Neural Networks and Deep Learning
- Algorithms for Unsupervised Learning
- Ensemble methods, and methods for small datasets