Artificial Intelligence for Product Managers Course

Course Summary

The AI for Product Managers course will provide product managers with a functional understanding of Artificial Intelligence (AI). Students will explore through hands-on workshops how to define a goal and business case for AI, how long it takes to build such a system, and how to design the project plan plus much more.

This course begins by exploring the AI ecosystem and the differences between AI, Machine Learning (ML), Deep Learning, and Natural Language Processing (NLP). Next, students will learn how AI works with data including terminologies and workflows. The course concludes with a discussion on what data scientists do, what feature engineering is, and what makes a ‘killer’ data scientist.

Purpose

Learn the practical skills to plan, implement and manage an AI program.

Audience

This course is designed for Product Managers interested in better understanding Artificial Intelligence and how it works with data.

Role

Project Manager

Skill Level

Introduction

Style

Workshops

Duration

3 Days

Related Technologies

Artificial Intelligence

Natural Language Processing

Download

Productivity Objectives:

Describe a potential AI program/product
Identify how AI uses data
Evaluate ROI for an AI application

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about our training

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

What You'll Learn

In the Artificial Intelligence for Product Managers training course, you'll learn:

What is AI?
- Overview of AI ecosystem
- AI vs. ML vs. Deep Learning vs. NLP vs. Time Series vs. …
Why Now?
- Why AI is applicable today?
Data and AI
- Data requirements for AI/ML
- Quality, quantity and application
- What data are useful for AI and what data are not
- Labeling the data
- Test and Train splits
Data Science Terminologies
- A summary of all keywords and what they mean
Data Science Workflow
- What does a data scientist do?
- AI/ML is a repetitive process
- Feature engineering
- What is time consuming in building an AI system
- What makes a data scientist a killer data scientist?
BlackBox vs. GlassBox
- Explainable AI vs. Unexplainable AI
- Is a Blackbox approach useful?
Accuracy and Performance Metrics
- Evaluating AI algorithms performance can be tricky
- Is accuracy enough? Can it mislead us?
- What other metrics to look at?
ROI Evaluation for an AI Application
- It is dependent on the business case
- New insights
- Accuracy improvements
- Manual time savings
AI Algorithms (Concepts) – Supervised vs. Unsupervised Learning
- Decision trees
- Bayesian methods
- regression
- KNN
- Neural networks and deep learning
- Etc.
AI Applications
- 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
AI in Real World
- Maintenance and updating AI models
- How to get better over time?
Can AI Be Biased?
- The consequences of bias
- How to discover the biases
- How can we prevent them
Project Management of an AI Project
- What is actually time-consuming for data scientists
- Time estimates for each step of AI/ML process
- A/B testing of AI/ML models
- Production considerations
- What can go wrong in a project plan
AI product Strategy and Communication
- There is more than one way to build an AI product
- How the design can affect the ROI
- How to Communicate with Stakeholders
- What is hype and what is really deliverable