What you’ll learn
How to product manage and roadmap your A.I. project
Basics of ML in easy doggy and kitty format 😀
How best to interact and extract key information from machine learning engineers
How to research your A.I. project for usability and product / market fit
UX design practices for your A.I. project
Exposure to simple coding notebooks for deep learning and linear regression
Requirements
None – designed for non-technical people.
Description
In this training workshop you will learn the following
1)The basics of machine learning, use cases and major success metrics easily explained using concrete examples of cute dogs and cats and exercises designed by a trained educational psychologist. No unnecessary or confusing math equations or algorithms, just the core concepts!
2)Pre-made coding notebooks and exercises to reinforce ML concepts that teach you the fundamental concepts without having to learn any code whatsoever!
3)User experience and user research tips to maximize the design of your potential A.I. project and efficiently de-risk your product ideas at many stages of development!
4)The best questions to ask your ML engineer when sizing and planning a project about business goals, data acquisition, tradeoffs, resources and risk!
5)A handy product roadmap for scoping out your machine learning project.
6)Fun discussion board exercises to help reinforce concepts throughout the course and see what other students are creating such as: thinking about ML use cases, mapping out the data science loop for your project, success metrics for your project, UX considerations, making a research roadmap for your project, making a product roadmap and UX mockup for your project.
7)A user research roadmap and chart to help you select the best methods to research your project.
Please note: for this course you will need a google account to access google classroom and to utilize google collab coding notebooks!
Who this course is for
Product managers
UX designers
User researchers
Business executives
Marketing Professionals
Engineers who want to learn about the product / business side of ML
Venture Capitalists / Investors who want to understand the A.I. domain better
