Data, AI, and Cloud Courses

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

Learn how to detect fraud using Python.

Learn how to approach and win competitions on Kaggle.

Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.

In this course youll learn to use and present logistic regression models for making predictions.

Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.

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

Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.

Learn to build recommendation engines in Python using machine learning techniques.

Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.

This course covers everything you need to know to build a basic machine learning monitoring system in Python

Learn to build pipelines that stand the test of time.

Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.

Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.

Learn how to design, automate, and monitor scalable forecasting pipelines in Python.

Learn the bag of words technique for text mining with R.

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

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

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

Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Learn to streamline your machine learning workflows with tidymodels.

Learn how to prepare and organize your data for predictive analytics.

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

In this course youll learn how to use data science for several common marketing tasks.

Learn to process sensitive information with privacy-preserving techniques.