Artificial Intelligence: Practical Essentials for Management
- 2.5 hours on-demand video
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- 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
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:
What is AI, Machine Learning & Deep Learning, and how are they different from BI
How to make a business problem solvable by AI
How to decide whether the problem needs AI at all
If AI is needed, how to become data ready – Tuscane© Approach
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
How to tell whether a software uses AI, or what kind of AI it uses
How to differentiate powerful AI solutions from weaker ones
How to determine which AI technique(s) can solve your specific business or organizational problem
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
How to measure AI’s performance
How to get others to use it in the organization
How to implement it successfully so that you don't waste thousands of dollars and hours of effort
How to estimate the financial value of an AI solution
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.
- Executive Leaders
- New Graduates
- University / College Students
Defining Artificial Intelligence
Everyday examples of AI: It's more than robots
Why is AI becoming so important
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?
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
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
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?
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?
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
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.
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