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Applied Machine Learning course using Python
This course is delivered online and consists of three units. The approximate duration of the course depends on the student's level and whether they repeat the exercises between the course content and the collaboration tools. Upon completion, the student will receive a certificate of completion.
The training is delivered through our Virtual Campus. With this format, you will have access to all the course materials on the platform, available 24/7 from the start date. Students will also have access to discussion 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 qualifications are required to take this course. However, basic computer skills and familiarity with Information Technology environments are recommended.
