Machine Learning : Essentials (3 Day)

Course Introduction


This course is meant for participants who would like to get introduced to Machine Learning and the various applications. The course duration is over 21 hours.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.The course covers Statistics, which is so very essential for effective use and also for interpreting results of applying Machine Learning models. It covers the breadth of Machine Learning techniques typically classified into Supervised and Unsupervised learning, and also into Regression / Classification / Clustering. It also introduces participants to the "R" programming language.

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.

What One Will Learn


  • The course covers Statistics, which is so very essential for effective use and also for interpreting results of applying Machine Learning models.
  • It covers the breadth of Machine Learning techniques typically classified into Supervised and Unsupervised learning, and also into Regression / Classification / Clustering.
  • It also introduces participants to the "R" programming language.

Prerequisite


Basic knowledge of Statistics, proficiency in a programming language like Python, basic mathematical concepts related to vectors and matrices.

Course Curriculum


Module 1 : Course Overview and Pedagogy

Module 2 : Essential Tools – “R”

  • Introduction
  • Exercises

Module 3 : Essentials of Statistics - I

  • Distributions
  • Hypothesis
  • Exercises

Module 4 : Essentials of Statistics - II

  • Various Tests
  • Interpretations
  • Bias Variance Tradeoff
  • Exercises

Module 5 : Machine Learning Overview

  • What is machine learning?
  • Terminology
  • Types
  • Applications

Module 6 : Machine Learning Overview

  • Distributions
  • Hypothesis
  • Exercises

Module 7 : Linear Regression

  • Concepts & Techniques
  • Interpretation
  • Exercises

Module 8 : Logistic Regression

  • Concepts & Techniques
  • Exercises

Module 9 : Clustering

  • Concepts & Techniques
  • Exercises

Module 10 : From Data to Models: Steps

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