Artificial Intelligence: Practical Essentials for Management
4.4 (25 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
89 students enrolled

Artificial Intelligence: Practical Essentials for Management

Your Go-to-guide to Successfully Use & Manage AI at Work without Having to Code
4.4 (25 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
89 students enrolled
Created by Malay Upadhyay
Last updated 6/2020
Current price: $58.99 Original price: $84.99 Discount: 31% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 2.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • 7 Principles to lead an AI journey in an organization
  • Key techniques of Machine Learning & Deep Learning
  • How to become data ready for AI - TUSCANE© method
  • How to decide whether to use AI at all, & whether to Build or Buy a solution - FAB4© Approach
  • How to measure AI performance & lead AI adoption in the organization
  • How to identify strong vs. weak AI solutions, and ensure desired results
  • How to frame strategic policy & manage risks associated with AI
  • None

Why this course: Most AI initiatives in organizations fail today not because of a lack of good AI solutions but because of a lack of understanding of AI among the managers - the end users, decision makers and investors in AI. Recruiters & Executives are struggling to find business professionals with a practical understanding of AI. This course is designed specifically to teach current and future managers all that they need to know to lead and manage AI use, without having to code and build AI models. You will

1) Learn both technical & managerial aspects of AI 

2) Identify the right AI solution & learn rare managerial frameworks to lead AI journeys successfully in an organization.

3) Get recognized in the use and management of AI/ML/DL by getting certified.

4) Gain competitive advantage in a hiring process or at work by being AI ready.


What this course covers:

  1. What is AI, Machine Learning & Deep Learning, and how are they different from BI

  2. How to make a business problem solvable by AI 

  3. How to decide whether the problem needs AI at all

  4. If AI is needed, how to become data ready – Tuscane© Approach

  5. Once ready, how to decide whether to build or buy a solution as well as what the risks really are with doing AI incorrectly – Fab-4© Approach

  6. How to tell whether a software uses AI, or what kind of AI it uses

  7. How to differentiate powerful AI solutions from weaker ones 

  8. How to determine which AI technique(s) can solve your specific business or organizational problem 

  9. How AI analyzes different types of information, how it makes predictions, how it recognizes images, how it communicates with your customers, or how a robot learns to behave like humans! This will involve some of the most popular Machine Learning and Deep Learning techniques that are being used today: Classification, Regression, Decision Trees, Ensemble Learning, Clustering, Association Rules, Search Algorithm, Reinforcement Learning, Natural Language Processing, ANN & CNN

  10. How to measure AI’s performance 

  11. How to get others to use it in the organization 

  12. How to implement it successfully so that you don't waste thousands of dollars and hours of effort 

  13. How to estimate the financial value of an AI solution

  14. How to minimize risks associated with AI for an individual and the organization


Ideal for: The classes have been designed to provide in-depth practical knowledge on AI, Machine Learning & Deep Learning if you:

1) Are interested in AI/ML/DL to advance your careers or to use it effectively to accomplish tasks at work

2) Have limited time but would like to get a thorough managerial understanding of AI/ML/DL

3) Do not need or are not interested in learning how to code AI/ML/DL


Bonus: At the end of each lesson, you will also get access to reports on some of the amazing & scary ways in which AI is being applied on you. I'll be updating the course with newer articles as and when they appear, which you will have free lifetime access to.

Special Thanks: Wife, Partner & Creative Support, Pooja Chitnis.

Who this course is for:
  • Executive Leaders
  • Managers
  • Administrators
  • New Graduates
  • University / College Students
Course content
Expand all 21 lectures 02:41:14
+ Introduction to AI
4 lectures 30:30
  • Defining Artificial Intelligence

  • Everyday examples of AI: It's more than robots

  • Why is AI becoming so important

Preview 10:19
  • Difference between AI and ML

  • Introduction to Deep Learning

  • Difference between AI and BI

Artificial Intelligence (AI) vs. Machine Learning (ML) vs. Deep Learning (DL)
AI Basics
2 questions
  • Popular platforms used

  • How is AI created

  • Types of Machine Learning algorithms

How do "they" create AI?
AI Algorithms
2 questions
+ AI Techniques & Use Cases
11 lectures 01:14:10
  • Overview of popular AI Techniques

Preview 03:09

Technique: Classification Techniques

Problem it solves: Predict unknown categorical value based on known data (i.e. which category something belongs to)


  • Would Maya like tea or water today?

  • Is a person with annual revenue >$100K more likely to walk or drive to work?

Predicting values & categories using AI – Part 1

Technique: Regression Techniques

Problem it solves: Predict unknown numeric or continuous values based on known data


  • When is Maya likely to come home today?

  • Based on the parameters that have led to past sales deals being won or lost, a software can predict the likely outcome of current sales deals

Predicting values & categories using AI – Part 2
Classification & Regression
1 question

Technique: Decision Trees, Ensemble Learning (Random Forest & Gradient Boosting)

Problem it solves: Predict unknown categorical or continuous values based on known data, but with greater depth, accuracy and rigour

Predicting values & categories using AI – Part 3
Ensemble Learning
2 questions

Technique: Clustering

Problem it solves: Identify the different types of things, people or situations that exist in a given population, & what characteristics differentiate these clusters/groups


  • What are the different types of drinks that Maya will choose from?

  • What are the major categories of consumers that a grocery store encounters, based on the purchase volume and timing of its customers

Identify Different Groups in a Population using AI

Technique: Association Rule Learning

Problem it solves: Determine what people may prefer if they preferred something else


  • If Maya has tea instead of water, is she more likely to also have a cookie with it?

  • If a consumer watches Lord of the Rings, how likely is she to also watch The Hobbit?

Predict Behaviour using AI
Clustering & Association Rules
2 questions

Technique: Search Algorithms & Monte Carlo Simulation

Problem it solves: Identify the risks/costs involved with a set of choices & find the optimal one


  • What are the robot's chances of being wrong in its choice of drink to serve?

  • Which are the best set of chess moves to play to have the maximum chances of winning in the quickest time?

Find the Optimal Solution from a Set of Solutions using AI

Technique: Reinforcement Learning

Problem it solves: Solve live, interacting problems to decide which action to take next on-the-go


  • How can the robot walk up to Maya to serve her drink?

  • Figure out the best marketing campaign out of 5 different options being shown to the audience on-the-go, by quickly learning which one is resonating the most

Teaching AI to learn on its own
Monte Carlo Simulation & Reinforcement Learning
2 questions

Technique: Natural Language Processing

Problem it solves: Read text, analyze it (to check errors, determine personality types of the writer, etc.) and respond


  • How to take voice commands directly from Maya

  • Based on the analysis of all emails sent by a customer/employee, determine how friendly she is, or whether her communication style is high or low context, etc.

Communicate using AI
Natural Language Processing (NLP)
4 questions

Technique: Deep Learning

  • Why is DL growing in importance?

  • What is the value it brings?

  • What is the underlying concept?

How AI starts to think like humans

Technique: Deep Learning

Problem it solves: Analyze large amounts of data to try to solve a problem the way human brain does

Example: Is it a picture of a dog, or a wolf that looks like a dog?

Recognize Images using AI
Deep Learning
3 questions
+ Using AI to Solve Business Problems
6 lectures 56:34

7 Lessons to keep in mind while driving AI Adoption in the organization

A Real-Life Example of AI Adoption
  • Why do organizations fail in effectively using AI

  • Understanding the problem first

  • How to determine if AI is needed to solve the problem

  • How to decide whether to build or buy an AI solution

  • How to identify the right solution

Preview 07:48
  • Importance of data

  • Is the data timely, usable, structured, complete, accurate, not biased and enough?

  • Data dictionary: How do you achieve data readiness

  • Data preprocessing techniques

Preparing Data for AI use
Qualifying AI & Data Readiness
3 questions
  • Preparing resources to be ready for AI introduction

  • Establishing processes to increase AI adoption and effectiveness

  • Establishing metrics to measure AI effectiveness

People, process, performance
  • Why a clear corporate strategy is must

  • Establishing processes, governance structures & policy guidelines

  • 3 types of risks with AI

  • Ethics & Responsible AI

Framing an Ethical AI Policy & Managing Risk
Responsible AI Adoption
2 questions
  • Other AI Courses to Take

  • Next Steps

  • Connecting with the Instructor for future courses

Bonus Lecture: What's Next?