Machine Learning Product Management: A Practical Guide

Machine Learning Product Management: A Practical Guide, available at $84.99, has an average rating of 4.52, with 59 lectures, 7 quizzes, based on 480 reviews, and has 3750 subscribers.

You will learn about When and how machine learning can be applied to solve problems How to organize machine learning teams Key roles in a machine learning team How to build and test a hypothesis Popular machine learning algorithms & how they work Data acquisition strategies Data scrubbing & transformation Model evaluation approaches Model deployment options Model monitoring This course is ideal for individuals who are Anyone looking to get a job as a Machine Learning / AI Product Manager or Product Managers who want to advance their skillset to build machine learning powered products or Anyone who wants to learn about the business side of machine learning It is particularly useful for Anyone looking to get a job as a Machine Learning / AI Product Manager or Product Managers who want to advance their skillset to build machine learning powered products or Anyone who wants to learn about the business side of machine learning.

Enroll now: Machine Learning Product Management: A Practical Guide

Summary

Title: Machine Learning Product Management: A Practical Guide

Price: $84.99

Average Rating: 4.52

Number of Lectures: 59

Number of Quizzes: 7

Number of Published Lectures: 59

Number of Published Quizzes: 7

Number of Curriculum Items: 66

Number of Published Curriculum Objects: 66

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

When and how machine learning can be applied to solve problems
How to organize machine learning teams
Key roles in a machine learning team
How to build and test a hypothesis
Popular machine learning algorithms & how they work
Data acquisition strategies
Data scrubbing & transformation
Model evaluation approaches
Model deployment options
Model monitoring

Who Should Attend

Anyone looking to get a job as a Machine Learning / AI Product Manager
Product Managers who want to advance their skillset to build machine learning powered products
Anyone who wants to learn about the business side of machine learning

Target Audiences

Anyone looking to get a job as a Machine Learning / AI Product Manager
Product Managers who want to advance their skillset to build machine learning powered products
Anyone who wants to learn about the business side of machine learning

== As of March 2024 ==

228 students have secured jobs as Machine Learning Product Managers —> at Google, Myntra, Flipkart, FedEx & more

Students reported improved confidence to…

Succeed in the new “Artificial Intelligence Era” —> 148%

Recognise when to use Machine Learning —> 125%

Apply popular Machine Learning algorithms —> 108%

Implement data acquisition strategies —> 84%

Evaluate, deploy & monitor Machine Learning models —> 118%

As you consider my course, I suggest asking yourself these questions.

== Do I want to become a Machine Learning (ML) Product Manager? ==

Every Product Manger will have to learn Machine Learning. It’s inevitable. And the Machine Learning Product Manager role is one of the most promising and highest-paying careers today, offering an opportunity to dramatically improve your career and quality of life.

Check out some data that shows that becoming a Machine Learning Product Manager is a great opportunity for you:

Google Search Trends shows an 87% increase in searches for “Product Management” worldwide, with a particularly strong interest in countries such as United States, India & Singapore. And within Product Management, Machine Learning is the topic with the highest interest over the last 2 years.
Glassdoor indicates Product Manager is one of the highest-paying entry level jobs,with an average of +$110,000 per year. Machine Learning Product Managers however earn “+30%” more than Product Managers.
LinkedIn included Product Manager in its 2023 Fastest Growing Jobslist. And the specialisation that is growing fastest is the Machine Learning Product Manager.

== How is this course different? ==

“Machine Learning Product Management: A Practical Guide” is the only Udemy course that is fully hands on and practical. All theory taught is paired with practical exercises that you’ll complete in your workbook.

Most machine learning courses focus on the technical work, and throw you into the deep end, asking you to start programming classifiers. This course covers machine learning, from a non-technical, product-centric perspective.

We’ll look beyond the technical, at all the things a Machine Learning Product Manager has to keep in mind, to create a successful product.

This is primarily a Learn By Doingcourse. So we’ll quickly dive into real-world exercises that demonstrate how successful teams build Machine Learning Products.

== What will I get from this course ==

1. You’ll gain an in-depth understanding of modern Machine Learning practices

When and how machine learning can be applied to solve problems
How to de-risk machine learning initiatives
Popular machine learning algorithms & how they work
Data acquisition strategies
Data scrubbing & transformation
Model evaluation approaches
Model deployment & monitoring options

2. The course lectures are accompanied by a workbook that includes 13 practical exercises to bridge the gap between the theory and real-world practices.

== Why should I invest in a Machine Learning Product Management course? ==

Machine learning is set to transform the traditional product manager role. As artificial intelligence and machine learning capabilities are designed into more products and services, product managers will need to upskill, or risk getting left behind. The Machine Learning / AI Product Manager is one of the hardest roles to fill in an AI team and is consequently highly sought after.

== What if I want a refund? ==

If after taking the course, you want a refund, thats totally ok. I want you to be happy with your decision to purchase this course.

This course has a 30-day money-back guarantee policy!

No questions asked!

There is no risk for you!

What are you waiting for?

Join now and take a step further into becoming a Product Manager and uplift your career!

==What are students saying? ==

Here is a small preview of what my students have reported.

A lot of information in a tight package. I have PM and ML experience, so it was a nice mix of validating my current workflows and learning new things. It’s worthwhile even if you have some experience, particularly if you have PM experience but haven’t worked with ML. [Zachary Lounsberry – Sr Research Scientist @ Embark]

A great course to get up to speed on everything that is going on with AI today. The course content is great and the information presented in the course is at the level needed to get your basics correct before you dive deep into AI and ML [Sekhar Banerjee – Principal Product Manager @ GE Digital]

Easy to understand for both beginners and someone with little to moderate background in ML. The content covers overall aspects of ML realm without going too deep on the technical fuzz. It should be suitable for the management level people in data analytics field too. [Peerajate Soonthrajan – Data Analytics Leader @ Accenture]

Love the sketches, the depth of information provided, the selection of topics covered, the anecdotal information examples, and the overall organization of the material. Excellent presentation. I’m less than midway through and have already learned tons of useful concepts and details. [Lance Silver – Technical Product Manager @ Expedia Group]

Great pacing, good content and delivery and overall a really informative course! [Hanut Singh Husain – Founder @ Patch]

REMEMBER… I’m so confident that you’ll love this course that we’re offering a FULL money-back guarantee for 30 days! So it’s a complete no-brainer, sign up today with ZERO risk and EVERYTHING to gain.

So what are you waiting for? Click the buy now button and join the world’s most complete course on Machine Learning Product Management.

Course Curriculum

Chapter 1: Intro to ML

Lecture 1: Congratulations! You Are Now An ML Product Manager

Lecture 2: Get Course Slides

Lecture 3: What is Machine Learning (ML)?

Lecture 4: Meet Your Instructor

Lecture 5: Important Message

Lecture 6: ML is Going Mainstream

Lecture 7: Get Your Course Workbook

Lecture 8: Exercise #1: Pick Your Product

Lecture 9: The Learning Algorithm

Lecture 10: Types of ML

Lecture 11: Exercise #2: What Type of ML is This?

Lecture 12: Deep Learning

Lecture 13: ML Real World Examples

Lecture 14: ML Terminology

Lecture 15: Exercise #3: Apply your ML Lingo

Chapter 2: When to ML

Lecture 1: The AI Flywheel

Lecture 2: ML Product Horror Stories

Lecture 3: When to ML

Lecture 4: When not to ML

Lecture 5: Exercise #4: Need interpretability?

Lecture 6: ML Data Considerations

Lecture 7: Exercise #5: To ML or Not to ML

Chapter 3: How to ML

Lecture 1: How the ML PM's Role Differs

Lecture 2: Organizing ML Teams

Lecture 3: Key Roles in An ML Team

Lecture 4: The ML Life Cycle

Lecture 5: Exercise#6: Create & Test Your Hypothesis

Lecture 6: Exercise #7: Frame Your ML Problem

Lecture 7: Exercise #8: Formulate Your ML Problem

Chapter 4: Get Your Data

Lecture 1: Data Acquisition Strategies

Lecture 2: Google reCAPTCHA

Lecture 3: Exercise #9: Find Examples of User-Generated Data Labelling

Lecture 4: Start with a Simplified Problem

Lecture 5: Exercise #10: Design Your Data for The Model

Lecture 6: Top Open Data Sources

Lecture 7: How Much Data Do I Need?

Lecture 8: Storing Data: Warehouses, Lakes & Graph Databases

Chapter 5: Prepare Your Data

Lecture 1: Data Scrubbing

Lecture 2: Sampling and Splitting Data

Lecture 3: Transforming Data

Lecture 4: Feature Engineering

Lecture 5: Exercise #11: Brainstorm a new feature

Chapter 6: Build Your Model

Lecture 1: Which ML algorithm should I use?

Lecture 2: Build, Outsource or Buy Your ML Solution?

Lecture 3: Machine Learning as a Service (MLaaS)

Lecture 4: Regression (Linear, Polynomial, Logistic)

Lecture 5: Classification (SVM, K-NN, Decision Trees)

Lecture 6: Clustering (K-Means, Means Shift)

Lecture 7: Anomaly Detection (Local Outlier Factor, DBSCAN)

Lecture 8: Ensemble Methods (Bagging, Boosting, Stacking)

Chapter 7: Deploy Your Model

Lecture 1: The Confusion Matrix

Lecture 2: Evaluation Metrics (Precision, Recall & F1 Score)

Lecture 3: Exercise #12: Lets Calculate Evaluation Metrics

Lecture 4: User Experience Optimization

Lecture 5: Exercise #13: Precision, Recall or F1 score?

Lecture 6: Deployment Methods

Lecture 7: Monitoring Your Model

Lecture 8: Congratulations!

Lecture 9: Bonus Lecture

Instructors

Raj Elakkara
Product Manager | Instructor | Founder of GapSquad | Harvard

Rating Distribution

1 stars: 1 votes
2 stars: 7 votes
3 stars: 37 votes
4 stars: 174 votes
5 stars: 261 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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