Machine Learning Product Management: A Practical Guide

Overview

Kick start your career in Machine Learning Product Management with just one course. What you'll learn:

When and how machine learning can be applied to solve problemsHow to organize machine learning teamsKey roles in a machine learning teamHow to build and test a hypothesisPopular machine learning algorithms & how they workData acquisition strategiesData scrubbing & transformationModel evaluation approachesModel deployment optionsModel monitoring == As of March 2024 ==228 students have secured jobs as Machine Learning Product Managers —> at Google, Myntra, Flipkart, FedEx & moreStudents 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 Jobs list.

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 Doing course. 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 practicesWhen and how machine learning can be applied to solve problemsHow to de-risk machine learning initiativesPopular machine learning algorithms & how they workData acquisition strategiesData scrubbing & transformationModel evaluation approachesModel deployment & monitoring options2. 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.

Syllabus

• **Introduction to Machine Learning Product Management**
Overview of ML and its impact on product management
Key roles and responsibilities of an ML product manager
Differentiating traditional product management from ML product management
• **Understanding Machine Learning Basics**
Overview of machine learning concepts and terminologies
Types of machine learning: supervised, unsupervised, and reinforcement learning
Key algorithms and their applications
• **Defining ML Product Strategy**
Identifying business problems that can be solved with ML
Aligning ML projects with business goals
Evaluating the feasibility and impact of ML initiatives
• **Data Considerations for ML Projects**
Importance of data in ML projects
Data collection, cleaning, and preprocessing
Ethical considerations and privacy concerns in data management
• **Building and Developing ML Models**
Understanding model development lifecycle
Collaboration with data scientists and engineers
Tools and platforms for ML development
• **Evaluation and Deployment of ML Models**
Metrics for evaluating ML model performance
Iterating on models and improving performance
Strategies for deploying ML models to production environments
• **Managing an ML Product Lifecycle**
Launching ML products and features
Monitoring model performance post-deployment
Handling model drift and regular updates
• **Cost Analysis and Budgeting for ML Projects**
Estimating costs associated with ML development
Budgeting for data infrastructure and computational resources
Cost-benefit analysis for ML initiatives
• **Communication and Collaboration in ML Projects**
Communicating complex ML concepts to stakeholders
Building cross-functional teams for ML projects
Managing expectations and stakeholder engagement
• **Real-world Case Studies and Applications**
Analysis of successful ML product implementations
Lessons learned from failed ML projects
Strategies for overcoming common challenges in ML product management
• **Ethics and Regulations in ML**
Understanding fairness, bias, and transparency in ML models
Regulatory compliance and legal considerations
Promoting ethical AI and responsible innovation
• **Future Trends in ML and AI Product Management**
Emerging technologies and methodologies in ML
The evolving role of AI in product management
Preparing for future developments in the field of AI and machine learning
• **Conclusion and Course Review**
Recap of key concepts and learning points
Final thoughts on the future of ML product management
Resources for continued learning and professional development

Taught by

Raj Elakkara

Subjects

Business