Machine Learning for Business Managers


Given the current state of advancing in technology and its absorption in the business it is clear that next few years will witness far reaching transformations in every area. Yesteryear’s bleeding edge technologies such as SMAC and Digital are now considered as given. We expect spectacular changes in our lives and in businesses with the advent of IoT, Artificial Intelligence, Machine Learning and Robotics. This course addresses issues relating to AI/ML and their impact on businesses.

Machine learning being at the core artificial intelligence we commence with ML concepts and the hands-on practicals. Understanding and solving business problems will be the key here. We will build ML models using various datasets. Having gotten grip of ML we move into the field of AI to discuss some of the implementations and business impact.

This course aims at getting the first-hand experience in Machine Learning using cloud based tools as well as desktop tools. Focus will be on understanding concepts and principles. Although the tools are not central to the learning they will aid in this process of clarifying basics. There will be multiple hands-on exercises. Concepts and usability of Machine Learning, Deep Learning in business will be discussed. 

Examples of application of Machine Learning across industries and functional areas :

Machine learning is not limited to IT and is used extensively across all functional areas, HR, Finance, Sales, Operations, etc. 

HR Managers

HR has vast amounts of data on all aspects of employee activity, this data can be analyzed and used to predict the probability of an employee leaving the company, it may go even beyond in identifying the factors in order of importance related to attrition. With such predictions, measures can be taken to curb the attrition rate. Similarly, it can be used in hiring, employee engagement, assessments, efficiency improvements, performance development, and so on.

Sales Managers

Sales forecasting is an important responsibility of any sales manager, which can be accurately achieved through Machine Learning. Feeding relevant historical selling, buying, and pricing data can predict future buying patterns. This will enable them to set achievable sales targets and devise sales strategies. ML can further benefit in areas of customer engagement, conversion rate, lead quality, purchase cycle, etc.

Finance Managers

With the vast consumer data such as age, income, credit rating, occupation, lifestyle, etc. an underlying trend can be predicted that might influence future lending. Similarly, data can be used for predicting; underwriting, trading, spending patterns, fraud detection, risk management, etc.

Operations Managers

With the correct data, errors in the process can be predicted which increases the success percentage. Machine learning can further be used to automate mundane work, floor management, resource deployment, etc.

This program aims at developing an overall understanding of ML through hands-on practice i.e. experiential learning. After the programme you can deploy the knowledge gained in your function based on the ML model with your own data.

Course Learning Outcomes:

By the end of this course participants will be able to understand:

  • ML concepts and the hands-on practical.
  • Building ML models
  • Understanding and solving business problems

Completely hands-on program

  • Duration: 4 half days
  • No prior coding knowledge required, will use cloud based GUI tool for doing exercises.
  • Participants need a computer and reasonable connection to Internet
  • Participants will need to download open source software, as required.
  • Note: This program does not include learning any of the languages.




Topic 1

Artificial intelligence & Machine Learning

  • Concepts
  • Relationship between AI & ML

Topic 2

ML Introduction

  • Overview – Applied ML
  • Terminology
  • Processes
  • Concept of Model & Algorithm
  • Classification, Regression, Clustering
  • Tools – Cloud, Orange

Topic 3


  • Introduction
  • Concepts
  • Types

 Topic 4


  • Metrics
  • Model evaluation


  • Features – data transformation
  • Feature selection, engineering
  • Feature extraction

Topic 6

Deep Learning

  • Neural networks
  • Image processing

Topic 7

Orange 3

  • Introduction – panes and help available
  • Classification, regression, Clustering


Contact Us

Bhavan's Campus
Munshi Nagar | Dadabhai Road,
Andheri West | Mumbai - 400 058, India
Reception: +91 22 61454200
Delhi Centre
Bharatiya Vidya Bhavan Campus, 
3rd Floor Gate No. 4, Copernicus Lane
Kasturba Gandhi Marg, New Delhi-110001
Telephone: 011-23073121, 011-23006871
Direct Line: 011- 23383563

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