Applied Machine Learning course using Python

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 collaboration tools. Upon completion, the student will receive an accrediting diploma.

The training is carried out through our Virtual Campus, with this modality you will have all the didactic content on the course platform and it will be accessible, from the start day of the course, 24 hours a day, every day of the week. The student will also have participation forums, as well as continuous tutoring.

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. Guys
• Biclustering
• Manifolds. Dimensionality reduction
• Basket analysis.

Final evaluation

Quality Questionnaire.

Previous requirements

No prior technical requirements are necessary to take this course. However, basic computer skills and knowledge of environments related to Information Technology are recommended.