AI for Product Manager

Learn the power of AI to define the right path for teams by management

Duration

2 Days

Level

Basic Level

Design and Tailor this course

As per your team needs

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Artificial intelligence is changing the way we work and live, so this course will help in enabling product managers and techno-functional professionals in understanding the Digital Intelligent Automation space, how AI is creating tremendous value in various industries such as Banking, Insurance, Healthcare, Retail and Automotive industries across the world.

What you will learn from this course

  • What is AI
  • Al vs ML vs Deep Learning vs NLP vs. Data Science
  • Data Science Terminologies
  • AI Applications
  • AI Algorithms
  • AI implementation techniques -AI strategy, how to start AI project, building AI team, generating ROI from AI project
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  • CXO
  • VP
  • Director
  • Managers
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  • Overview of Al ecosystem
  • Al vs ML vs Deep Learning vs NLP vs. Data Science
  • Why Al is applicable today?
  • A summary of keywords and what they mean
  • A summary of keywords and what they mean
  • What does a data scientist do?
  • AI/ML is a repetitive process
  • Feature engineering
  • What is time-consuming in building an Al system¬†
  • What makes a data scientist a killer data scientist?
  • Explainable Al vs Unexplainable Al
  • Is a Blackbox approach useful?
  • Evaluating Al algorithms performance can be tricky
  • Is Accuracy enough? Can it mislead us?
  • What other metrics to look at?
  • It is dependent on the business case
  • New insights
  • Accuracy improvements
  • Manual time savings
  • Decision trees
  • Bayesian methods
  • Regression
  • KNN
  • Neural networks and deep learning
  • Forecasting Algorithms use cases
  • Recommendation Engines use cases
  • Classification vs. Clustering + use cases
  • Chatbots and Virtual Agents use cases
  • Deep Learning use cases
  • Image and Video Processing use cases
  • Time series use cases
  • Maintenance and updating Al models
  • How to get better over time?
  • The consequences of bias
  • How to discover the biases
  • How can we prevent them
  • What is actually time-consuming for data scientists
  • Time estimates for each step of the Al/ML process
  • A/B testing of AI/ML models
  • Production considerations
  • What can go wrong with a project plan
  • There is more than one way to build an Al product
  • How the design can affect the ROI
  • How to Communicate with Stakeholders
  • What is hype and what is really deliverable
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None, Basic understanding of technology

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