Course program · 6 blocks · 10 chapters
Block 01 · 1 chapter
Fundamental Principles of Machine Learning
Key topics
What are fundamental concepts, principles, and problems in machine learning
How AI creates value for businesses
Key ideas behind generative AI and large language models
Projects
Creating initial versions of ML models for each of the projects
Chapters
01
Typical problems and basic concepts of machine learning and artificial intelligence
Block 02 · 1 chapter
Evaluating the Quality of ML Models and Their Impact on Business
Key topics
What are model quality metrics and why it is important to evaluate them
What are business metrics and how do they relate to model quality metrics
How to evaluate the impact of the model on business metrics
Projects
Choose quality and business metrics for each of the projects
Chapters
02
Evaluating the quality of ML models and their business impact
Block 03 · 1 chapter
How to Choose Approaches for Solving AI/ML Problems
Key topics
How to explore possible solutions for AI/ML problems
How to prioritize team efforts
Projects
Choose specific approaches to solve problems within the projects
Chapters
03
How to approach ML problems
Block 04 · 2 chapters
How to Improve the Quality of ML Models
Key topics
Tools for diagnosing the causes of poor model quality
The main levers for improving model quality
How to create datasets for training and evaluating models
How to choose the training method for the model
What is representativeness and model complexity
Projects
Diagnose model quality issues for each of the projects
Identify levers to enhance model quality
Chapters
04
Error analysis for ML models
05
How to improve the quality of ML models
Block 05 · 4 chapters
Stages of Working on an AI Project
Key topics
The importance of problem analysis for defining AI project tasks
How to assess the product, technical, and economic risks of a project
How to design and deploy a MVP and a pilot
How to do an early evaluation of the business impact
Projects
In-depth examination of the problems addressed within the projects
Project risk assessment and mitigation planning
Chapters
06
Meeting the real world
07
AI/ML project setup. Understanding the problem
08
Stages of AI/ML project work. Product development and technical risks of an AI/ML project
09
Economic risks of AI/ML projects
Block 06 · 1 chapter
Principles of Managing AI Projects
Key topics
What are the principles for achieving success in AI projects
What are the responsibilities of the AI product manager
Overview of approaches for AI/ML in production
Projects
Covered throughout the preceding blocks
Chapters
10
Approaches for AI/ML in production
Format: simulator, self-paced
Course duration: ~60 hours
Required knowledge: no programming skills or deep knowledge of mathematics required