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.