This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.
1. Introduction
2. Setting Up the Working Environment
3. Linear Regression with Sklearn
4. Linear Regression –Practical Example
5. Logistic Regression
6. Cluster Analysis
7. Cluster Analysis: Additional Topics
Basic coding skills in Python
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