Applied Machine Learning using Python - SCORM

Applied Machine Learning course using Python - SCORM

Course contents

This course is taught online and consists of 3 units. The approximate duration of the course depends on the student's level and whether they repeat the practices between the course content and the collaboration tools. Upon completion, the student will receive a certificate of completion.

Index

Unit 1. Introduction to the course

• Introduction to Python
• Python library for Machine Learning.
• Machine Learning. Introduction.

Unit 2. Supervised learning

• Definition and applications.
• Performance measures.
• Linear models
• Supervised ML models: trees, SVM, neural networks.
• Combination of models. Random Forest.

Unit 3. Unsupervised learning

• Definition and applications.
• Performance measures.
• Clustering. Types
• Biclustering
• Manifolds. Dimensionality reduction
• Basket analysis.

Final evaluation

Quality Questionnaire.

Prerequisites

There are no technical prerequisites required to take this course. However, basic computer skills and knowledge of environments related to Information Technology are recommended.