Implementing Machine Learning Principle Using Python

PT16789
Summary
The course deals with the basic principles needed to understand and apply Machine Learning models and methods. The topics include Supervised and Unsupervised Learning, Bayesian Decision Theory, Parametric Methods, Tuning Model Complexity, Dimensionality Reduction, Clustering, Nonparametric Methods, Decision Trees, Comparing and Combining Algorithms, as well as a few other applications of these methods.
Prerequisites
There are no prerequisites for this course.
Duration
4 Days/Lecture & Lab
Audience
This course is designed for those wanting to learn the basic principles needed to understand and apply Machine Learning models and methods.
Topics
Fundamentals of Python
  • Working with Statistics and Probability using Python
  • Machine Learning with Python Part – 1
  • Machine Learning with Python Part – 2

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