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Course Overview

  • Gain theoretical knowledge regarding the different Machine Learning algorithms
  • Learn the concepts of SVM and SVR in this Machine Learning Course
  • Learn about supervised and unsupervised learning concepts and clustering

Who should attend this Machine Learning Training Course?

The Machine Learning Course is an intensive and comprehensive course designed to provide a deep dive into the fundamental concepts and applications of Machine Learning. The following are some professionals who can benefit greatly from this course:

  • Data Scientists
  • Data Analysts
  • Software Engineers
  • Business Analysts
  • Operations Managers
  • HR Professionals
  • Project Managers
  • Customer Service Managers

    Prerequisites of the Machine Learning Training Course

    Delegates must have a basic understanding of Python Programming and Statistics.

    What’s included in this Machine Learning Training Course?

    • World-Class Training Sessions from Experienced Instructors 
    • Machine Learning Certificate 
    • Digital Delegate Pack

    Course Outline:

    Module 1: Machine Learning – Introduction

    • What is Machine Learning?
    • Main Elements of Machine Learning
    • Traditional Programming Vs Machine Learning
    • Real Time Applications of Machine Learning

    Module 2: Importance of Machine Learning and its Techniques

    • Importance of Machine Learning
    • Types of Machine Learning
    • How Machine Learning Works?

    Module 3: Machine Learning Mathematics

    • What is Machine Learning Mathematics?
    • Why Mathematics is Significant for Machine Learning?

    Module 4: Data Pre-Processing

    • What is Data Pre-Processing?
    • Way to Handling Missing Values

    Module 5: Supervised Learning

    • Introduction to Supervised Learning

    Module 6: Classification

    • Introduction to Classification
    • Types of Learners
    • Support Vector Machines (SVM)
    • How does SVM Work?
    • Discriminant Analysis
    • Naive Bayes
    • Nearest Neighbour

    Module 7: Regression

    • Introduction to Regression
    • Regression Models
    • Linear Regression and GLM
    • SVR
    • Decision Tree
    • Neural Networks

    Module 8: Unsupervised Learning

    • What is Unsupervised Learning?
    • Difference Between Supervised and Unsupervised Learning

    Module 9: Clustering

    • Introduction to Clustering
    • K-Means
    • K-Medoids
    • Fuzzy
    • Hierarchal
    • Gaussian Mixture
    • Hidden Markov Model

    Module 10: Deep Learning

    • Introduction to Deep Learning
    • Importance of Deep Learning
    • How Deep Learning Works?


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