This course builds a foundation for students interested in how design computer algorithms that improve automatically through experience, in other words trough data and in which are the main problem types to solve

Audience (and prerequisites)

Anyone with a basic knowledge as developer who wants to explore machine learning applications with Python and R

Approaches (Objective)

Introduction

• What is learning
• Learning curves
• Training, validation and test

Supervised Learning

• Simple and multiple linear regression
• Polynomial regression
• Logistic regression
• Support Vector Machine
• Decision Tree
• Random Forest
• Neural Network

Unsupervised learning

• Similarity and distances
• Association rules
• Clustering
• Cluster quality metrics
• Feature engineering