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Fundamental Machine Learning

Categories Artificial Intelligence
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What I will learn?

  • Learning the basics of ML
  • Learning the theories behind ML, its concepts, and algorithms
  • Learning the obstacles of ML algorithms and overcoming them
  • Building ML models, evaluating them, and analyzing results

Course Curriculum

INTRODUCING MACHINE LEARNING AND 2 METHODS OF SUPERVISED LEARNING

PRESENTING ML OBSTACLES AND DIFFERENT WAYS OF ADDRESSING THEM

PRESENTING NEURAL NETWORKS AND THEIR LEARNING METHODS

PRESENTING KERNEL METHODS

INTRODUCES UNSUPERVISED LEARNING, ITS DIFFERENT ALGORITHMS, AND ITS PERFORMANCE EVALUATION

PRESENTING COMPUTATIONAL LEARNING THEORY AND MODEL EVALUATION METHODS

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  • Level
    Intermediate
  • Total Enrolled
    1

Material Includes

  • Video Lectures: Presenting Concepts, Algorithms, and Mathematics as the basis of Machine learning methods.
  • Reading Materials (Slides)
  • Virtual lab Projects (assignments):
  • Problem representation: representation of a (real-world) problem.
  • Hands-on (Python) experience: executing Machine Learning methods on the problem and analyzing results.
  • Quiz: to ensure the understanding of students, after each chapter, a few questions (objective questions or short answer questions) are designed based on the content of the materials.

Requirements

  • Statistics and Linear Algebra
  • Basic knowledge of Python programming
  • Access to the Anaconda environment
  • Google Colab

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Target Audience

  • Anyone