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Applied Machine Learning course using Python
This course is taught online and consists of three units. The approximate length of the course depends on the student's level and whether they repeat the practice between the course content and the collaboration tools. Upon completion, students will receive a certificate.
Training is provided through our Virtual Campus. With this option, you'll have access to all the course content on the course platform, accessible 24/7 from the start of the course. Students will also have access to participation forums and ongoing 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.
- Model Combination. Random Forest.
Unit 3. Unsupervised learning
- Definition and applications.
- Performance measures.
- Clustering. Types
- Biclustering
- Manifolds. Dimensionality Reduction
- Basket analysis.
Final evaluation
Quality Questionnaire.
Prerequisites
No prior technical requirements are required to take this course. However, basic computer skills and knowledge of IT-related environments are recommended.
