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Intel AI Academy – Machine Learning


This course provides an overview of machine learning fundamentals on modern IntelĀ® 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).

Prior Knowledge

  • Python programming
  • Calculus
  • Linear algebra
  • Statistics
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