Intel-Machine-Learning-Course

This course provides an overview of machine learning fundamentals on modern Intel® architecture. Topics covered include: Reviewing the types of problems that can be solved Understanding building blocks Learning the fundamentals of building models in machine learning Exploring key algorithms By the end of this course, students will have practical knowledge of: Supervised learning algorithms Key concepts like under- and over-fitting, regularization, and cross-validation How to identify the type of problem to be solved, choose the right algorithm, tune parameters, and validate a model The course is structured around 12 weeks of lectures and exercises. Each week requires three hours to complete. The exercises are implemented in Python*, so familiarity with the language is encouraged (you can learn along the way).