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.

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.

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

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.

Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Dive into machine learning with Python. Learn to build models, preprocess data, and evaluate results using Python libraries like Scikit-learn, pandas, and numpy, to unlock powerful insights for a variety of business and data science applications.

Master machine learning with R, a popular language for data analysis and statistical modeling. Learn how to apply machine learning algorithms, preprocess data, and evaluate models using R’s powerful libraries and tools for data science.

A comprehensive source to help you learn Machine learning with TensorFlow