In this module, we will guide you through the decision-making process for implementing machine learning in your product. You’ll learn how to evaluate when ML is the best solution, the data needs for a project, and the common challenges to watch out for.

In this module, we will explore the unique role of an ML Product Manager, how to structure teams for success, and what each phase of the ML project lifecycle entails. You'll also get hands-on experience with formulating and validating project hypotheses.

In this module, we will cover strategies for acquiring and preparing data for machine learning models. You will explore data acquisition techniques, data storage options, and methods to structure data for successful ML model training.

In this module, we will guide you through essential preprocessing techniques including data cleaning, transformation, and feature engineering. You’ll also learn how to split and sample data effectively for your ML models.

In this module, we will dive into selecting the right machine learning algorithm for your project, exploring various types of models including regression, classification, and anomaly detection. You’ll also learn when to build, buy, or outsource solutions.

In this module, we will explore how to evaluate and optimize machine learning models using metrics like the confusion matrix, precision, and recall. You’ll also learn strategies for continuous performance improvement and user experience optimization.

In this module, we will take you through the steps of deploying your machine learning model into production and discuss how to monitor its performance. You will also learn strategies for keeping your model optimized and scalable.