This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases.

This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases.

Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.

Prepare for machine learning job interviews with essential concepts, techniques, and practice problems.

Learn the basics of practical machine learning methods for classification problems.

This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.

This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work.

Gain insights into ML system design, state-of-the-art techniques, and best practices for scalable production. Learn from top researchers and stand out in your next ML interview.

Learn Machine Learning Terminology & Process and gain solid understanding along with hand-on labs. Master 5 Steps of ML

Learn machine learning using ML.NET

Explore machine learning with Apache SystemML, an open-source platform for scalable machine learning. Learn how to build efficient and distributed machine learning models, and optimize their performance with SystemML's advanced algorithms.