In this course you'll learn how to use data science for several common marketing tasks.

From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.

Learn how to apply machine learning to sequential data, such as time series or text. Master algorithms like RNNs and LSTMs to model dependencies and make predictions based on sequential patterns in data, from stock prices to natural language.

This course focuses on feature engineering and machine learning for time series data.

Discover how to apply machine learning techniques to trading strategies and financial markets.

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.

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.

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.

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.