Learn Machine Learning Product Management from Cats and Dogs

Learn Machine Learning Product Management from Cats and Dogs, available at $54.99, has an average rating of 4.5, with 43 lectures, based on 11 reviews, and has 42 subscribers.

You will learn about How to product manage and roadmap your A.I. project Basics of ML in easy doggy and kitty format 馃榾 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 a simple coding notebook for pet image classification This course is ideal for individuals who are Product managers or UX designers or User researchers or Business executives or Marketing Professionals or Engineers who want to learn about the product / business side of ML or Venture Capitalists / Investors who want to understand the A.I. domain better It is particularly useful for Product managers or UX designers or User researchers or Business executives or Marketing Professionals or Engineers who want to learn about the product / business side of ML or Venture Capitalists / Investors who want to understand the A.I. domain better.

Enroll now: Learn Machine Learning Product Management from Cats and Dogs

Summary

Title: Learn Machine Learning Product Management from Cats and Dogs

Price: $54.99

Average Rating: 4.5

Number of Lectures: 43

Number of Published Lectures: 43

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

How to product manage and roadmap your A.I. project

Basics of ML in easy doggy and kitty format 馃榾

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 a simple coding notebook for pet image classification

Who Should Attend

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

Target Audiences

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

This 30 day money back guarantee course is optimized to teach you the highest impact concepts and strategies for building an A.I. productin the least time possible, just 2.5 hours, with no risk! Your time is valuable don’t waste it on a longer, less efficient course, take this one to quickly learn:

1)The basics of machine learning, use cases and major success metrics easily explained using concrete examplesof cute dogs and catsand exercises designed by a trained educational psychologist. No unnecessary or confusing math equations or algorithms, just the core concepts!

2)A pre-made coding notebook for predicting if an image is a cat or dog with deep learning with simple 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 designof 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction!

Lecture 2: When ML is not the best approach!

Lecture 3: Main Takeway Preview!

Lecture 4: Main Takeway Preview! Part II

Chapter 2: Machine Learning Basics

Lecture 1: Major ML Use Cases

Lecture 2: Basic Premise of ML

Lecture 3: Three major types of ML

Lecture 4: Supervised Learning

Lecture 5: Unsupervised Learning

Lecture 6: Semi-supervised Learning

Lecture 7: Reinforcement Learning

Lecture 8: Overfitting and Underfitting

Chapter 3: Data Science Loop

Lecture 1: Data Science Process

Lecture 2: Data Science Loop Specifically

Lecture 3: Ex2: Map out the Data Science Loop for your product!

Lecture 4: Data Science Loop

Lecture 5: Comparing ML and Data Analysis

Lecture 6: Limitations of ML

Chapter 4: Algorithms Overview and Linear Regression Deep Dive

Lecture 1: Algorithms Overview

Lecture 2: Linear Regression Overview

Lecture 3: Linear Regression Pros and Cons

Chapter 5: Success Metrics

Lecture 1: Classification success metrics

Lecture 2: General success metrics

Lecture 3: ROC Curves

Lecture 4: Ex3: Post success metrics for your product!

Chapter 6: Deep Learning Intro

Lecture 1: Introduction to deep learning (convolutional neural networks)

Lecture 2: Deep Learning Coding Notebook – Case Study in Predicting Dog and Cat Images!

Chapter 7: User Experience and Research Considerations for ML Products!

Lecture 1: UX Issues in ML

Lecture 2: Ex4: Post UX considerations for your ML product

Lecture 3: Testing mockups of your ML product

Lecture 4: Testing concept diagrams of your ML Product

Lecture 5: Demographic and Psychographic Factors to Consider

Lecture 6: A Research Roadmap for your ML Product

Lecture 7: How UX, UR, Product and DS/Eng should work together!

Lecture 8: Ex5: Post a research roadmap of your ML product!

Chapter 8: Key Questions to Ask and Roadmap Template for your ML Product

Lecture 1: ML Disclaimer Again!

Lecture 2: Key questions to ask your team

Lecture 3: Checklist of Key Sections for you ML Product Roadmap

Lecture 4: Product Roadmap Example

Lecture 5: Post your end of course roadmap here!

Lecture 6: Please fill out the end of the course survey! https://forms.gle/zhrCaPGY8LJrQFy2

Lecture 7: Thank you for attending the course!

Lecture 8: Bonus lecture – connect with us for consulting / learn about free edtech apps!

Instructors

Satyugjit Virk, PhD
Trained Educational Psychology PhD Instructor of STEM

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 2 votes

5 stars: 8 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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