Understand strategies for identifying and mitigating risks in machine learning pipelines, ensuring robustness and reliability in ML applications.

Discover strategies for mitigating disasters in machine learning pipelines, focusing on best practices for monitoring, validation, and error handling.

Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

Building and evaluating machine learning (ML) models is daunting, but correctly engineered models can provide millions of dollars in value. In this course, you'll learn to build and evaluate these tools, leveraging existing data science knowledge.

Understand the processes of building, validating, and optimizing predictive models for data-driven decision-making.

Learn what a Power BI semantic model is, which data loading approach to use, and how to build out your semantic model to get the insights you need.

The machine learning pipeline doesn’t end at just building the model. This course will teach you how to deploy your machine learning models as application programming interface (API) endpoints, and the maintenance required to support the model.

Master model evaluation techniques to refine and improve your machine learning models. Learn to use metrics like accuracy, precision, recall, and F1-score to assess performance and tune models for optimal results.

Review the techniques and metrics used to evaluate how well your machine learning model performs. You will also learn methods to select the best machine learning model from a set of models that you've built.

Learn how to work with data that comes from different sources and is structured in different ways by using Power BI transformations to combine, reshape, cleanse, and enhance that data to create a model that supports reporting and analytics.

Along with good working experience and knowledge of how to train and evaluate models, you need to have a good understanding of all the ML algorithms provided by AWS. This course will teach you the use cases of built-in algorithms provided by AWS.

Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.