Machine Learning : Advanced (3 Day) 

Course Introduction


This course caters to those who have basic knowledge of machine learning and who would like to get introduced to and master some of the advanced techniques. The course duration is three days.The course takes a practical and "hands-on"approach to teaching Machine Learning. Concepts will be introduced through practical examples and participants will be given exercises to be completed within and outside of contact hours. Use of Python programming language to build machine learning models.Advanced machine learning topics will be covered including: feature selection and reduction, cross validation, advanced regression techniques, decision trees and SVM.

Training Highlights


  • The course takes a practical and "hands-on"approach to teaching Machine Learning.
  • Concepts will be introduced through practical examples and participants will be given exercises to be completed within and outside of contact hours. Use of Python programming language to build machine learning models.

What One Will Learn


Advanced machine learning topics will be covered including: feature selection and reduction, cross validation, advanced regression techniques, decision trees and SVM.

Prerequisite


  • Good knowledge of "R" or Python programming languages is essential.
  • Good understanding of relevant statistical concepts is a must and knowledge of basic machine learning techniques (supervised and unsupervised) is an absolute must.

Course Curriculum


Module 1 : Course Overview and Pedagogy

Module 2 : Cross Validation

  • Concepts
  • Examples and Exercises

Module 3 : Feature Selection, Dimension Reduction

  • Concepts
  • Exercises

Module 4 : Advanced Regression Techniques

  • Polynomial Regression
  • Non-Linear Regression
  • Exercises

Module 5 : Decision Trees: Basic

  • Regression Trees
  • Classification Trees
  • Exercises

Module 6 : Decision Trees: Advanced

  • Bagging
  • Boosting
  • Random Forests

Module 7 : Support Vector Machines

  • Concepts and Applications
  • Exercises

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