Machine Learning Essentials (MLE)

Machine Learning is revolutionizing the world by allowing computers to learn as they progress forward with large data sets, overwriting overcoming all programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve.
When this technology powers Artificial Intelligence (AI) applications, the combination can be powerful. Smart robots can already be found around us doing all our jobs with more speed and accuracy, and continuously improving themselves at every step.

Upcoming Batches

Course Objective
Machine Learning Essentials (MLE) is designed to let participants acquire all the essential knowledge on Machine Learning.
Course Duration
8 hours / 1-Days

Course Outline
MODULE 1 Introduction to Machine Learning  

  • What is Machine Learning and its importance?
  • How Businesses Can Take Advantage of Machine Learning?
  • MODULE 2: Different Techniques in Machine Learning - Regression Method  

  • What is Regression Method?/li>
  • Case Study on Regression Methods
  • Application of Regression Method
  • Hands-On on the Application of Regression Method
  • MODULE 3:Different Techniques in Machine Learning - Logistic Regression  

  • What is a Logistic Regression?
  • Case Study on Logistic Regression
  • Application of Logistic Regression
  • Hands-On on the Application of Logistic Regression

  • Pre-Requisite
    NA
    Examination
    Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Digital Transformation based on the syllabus covered
    Certification
    Participants will be recognised as a CASUGOL CERTIFIED PROFESSIONAL and awarded a Certificate of Competency in Machine Learning Essentials (MLE)upon meeting the requirements and passing the examination.
    Who Will Benefit from the Course
    Machine Learning Essentials (MLE) is designed for anyone who have little or no understanding, knowledge of, or experience in Machine Learning and would like the opportunity to learn in a supportive and encouraging environment. Class is limited to 20 participants as hands-on sessions and real-time demonstration is expected.