Course Overview

  • Machine Learning is an exciting sub-area of Artificial Intelligence dealing with designing systems which can learn and improve their performance from experience.

  • Machine Learning plays a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. Machine Learning has introduced superior solutions in many domains and is set to be a strong pillar of future technology.

  • This course introduces the learner to the key algorithms and theory that forms the core of Machine Learning. The course covers the major approaches to learning namely, supervised, and unsupervised. The assignments in the course are using Python as the programming language.

  • The course starts with building the fundamentals – various algorithms (linear, quadratic, logarithmic, neural networks etc. It covers the learning and error functions like gradient descent, maximum likelihood etc. Using these it builds on various techniques used in Machine Learning like Regression, Decision Trees, Support Vector Machines, Neural Networks, Bayesian Learning, Genetic Algorithms etc.

  • The course requires learners to have a good knowledge of Mathematics, Statistics and Programming.

  • This 6 Weeks Course runs for approx. 35 Hours, comprising of Instructor-Led Live Online Sessions, Hands-on Assignments & Assessments.

  • The Live Instructor Led sessions are conducted on weekends for the benefit of working professionals. The practical assignments can be done by learners as per their convenience and submitted before the specified deadline. Refer to Session Plan below

  • The course fee is INR 35,000(incl. 18% tax) to be paid in full before the last date of registration.

Course Objectives

Following are the key objectives of this course:

  • To introduce learners to the basic concepts and techniques of Machine Learning.

  • To gain experience of doing independent study and research.

  • To develop skills of using Python for solving practical problems.

Platform and Tools

The technology used enables interactive and practical sessions.

  • Live Instructor Led sessions are conducted using Microsoft Teams Communication and Collaboration Platform. Learners will need to attend the live sessions from their laptops through Microsoft Teams. No MS Teams license required by Learners.

  • Online Assessments are hosted on Thinkific, a cloud based Learning Management Tool . The access to Thinkific Acadepro site will be available to registered Learners from the beginning of the course till 9 months after course completion

  • Hands-On Assignments would be done by learners on their own laptops on Anaconda platform using Python. Instructions to install and use the platform would be provided upon registration.

Course Outcomes

The training curriculum is designed for following outcomes. The learners will have an understanding of :

  • Mathematical principles of Machine Learning: Probability theory, Bayes Theory, Matrix mathematics, Calculus and Decision Theory.

  • Fundamental algorithms of Machine Learning: Linear, Logistic, Non-linear models for Regression and Classifications.

  • Machine Learning Techniques: Bayesian, Decision trees and Neural networks.

  • Instance Based Learning: K-Nearest Neighbor, Locally Weighted Regression and Case Based Regression.

  • Support Vector Machine: Theory, Dimension, Linear and Non-Linear Separable data.

  • Unsupervised & Supervised Learning: Mixture and K-Means Clustering.

  • Generic Algorithms: Theory, Problem Solving, Hypothesis Development and Population evolution.

  • Practical application using Python

Session Plan

Each session is 1.5 hrs Instructor-led Live Online Session . Timings will be notified before program commencement.

Date Lesson
0 Pre-Assessment 05/09/2020 Pre-Assessment on Thinkfic Platform
0 Orientation Session 06/09/2020 Instructor briefs learners on program flow
 1 1 12/09/2020 Introduction to Machine Learning
1 2 13/09/2020 Mathematical Basics & Bayesian Learning
2 3 19/09/2020 Linear Regression & Linear Classification
2 4 20/09/2020 Non-linear Models: Non-linear selection & Bayesian Learning Techniques
3 5 26/90/2020 Instance Based Learning: K-Nearest Neighbors
3 6 27/09/2020 Instance Based Learning: Case based Learning/Reasoning
4 7 & Mid-Assessment 03/10/2020 Support Vector Machine: Linearly Separable Data ; Mid-term Assessment on Thinkific Platform

8 04/10/2020 Support Vector Machine: Non-linearly Separable Data
5 9 10/10/2020 Basic Genetic Algorithm Operators
5 10 11/10/2020 Hypothesis Search Space, Population Evolution, Schema Theorem
6 11 17/10/2020 Unsupervised Learning:Mixture Models & K-means Clustering
6 12 18/10/2020  Questions & Doubt Clearing
7 Post-Assessment 25/10/2020 Post-Assessment on Thinkific Platform

Jagdish Prasad


  • Mr. Jagdish Prasad is an accomplished senior IT professional with 30+ years of experience in Strategy, Outsourcing Advisory & Execution, Product Development, Enterprise Architect, consulting and managing Global multi-cultural teams.

  • Mr. Prasad has designed and implemented multiple large & complex applications using variety of technologies for Fortune 500 customers.

  • Mr. Prasad has significant expertise in using new technology to provide business transformations solutions. He has used Machine Learning to design effective solutions for critical areas like fraud detection, cyber security, customer service and process automation.

Course starts 5th Sep 2020

Registration Closes In

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Learner Support

To enhance learner experience, this course offers additional support during and post the course.

  • Query Handling

    Learners can post their queries or doubts to the Instructor's personal email and expect the response within 3 working days. This facility is available during the course and upto 3 months post the course.

  • Bonus Material

    Learners will have access to the course material used by the Instructor during the course and upto 9 months after the course. This will be available on the Acadepro Learning Site.

  • Certificate of Completion

    Learners will be awarded Certificate of Completion upon completing all the course requirements i.e. attending all sessions, assessments and assignments

Assignments & Assessments

The assessments in this Machine Learning Course test the knowledge and understanding of learners and assignments offer practical application of concepts.

  • Pre and Post Course Assessment to ascertain the K-Gain( Knowledge Gain) of learners

  • Mid-Course Assessment to assess the progress

  • 15 Hours for solution design and implementation using Python including assignment discussion