Machine Learning Product Management - Strategy to Deployment: Unlock the Full Potential of ML with Expert Product Management

Raj Elakkara

£63.99

Video Aug 2025 4hrs 45mins 1st Edition

Raj Elakkara

£63.99

Video Aug 2025 4hrs 45mins 1st Edition

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Key benefits

Comprehensive coverage of machine learning concepts and product management strategies
Practical exercises to reinforce learning and real-world application of ML techniques
In-depth guidance on model evaluation, optimization, and deployment across industries

Description

This course immerses you in the field of machine learning product management. You’ll start by exploring fundamental ML concepts, key terminology, and the differences between supervised, unsupervised, and reinforcement learning. Practical exercises will help reinforce your understanding and give you the confidence to classify different ML types and understand how algorithms learn from data. As you progress, you’ll learn to assess whether machine learning is the right tool for specific problems, consider the AI flywheel, and avoid common pitfalls in ML development. You’ll gain skills in evaluating data requirements and making decisions on model interpretability, ensuring the best approach for your ML project. By the end of this section, you’ll know when and how to integrate machine learning effectively into your product. The course concludes with guidance on managing ML projects, data acquisition, preprocessing techniques, and selecting the right algorithms. You’ll gain hands-on experience in developing real-world ML solutions, from regression models to clustering algorithms. Finally, you’ll learn how to optimize model performance, evaluate using metrics like precision and recall, and deploy with confidence, preparing you to manage and deploy ML-driven products across industries.

Who is this book for?

This course is designed for aspiring machine learning product managers, data-driven product managers, and professionals looking to integrate machine learning into their products. No prior technical knowledge of machine learning is required, but familiarity with product management and an interest in technology are recommended.