Data, AI, and Cloud Courses

Learn how to reduce training times for large language models with Accelerator and Trainer for distributed training

In this Power BI case study you’ll play the role of a junior trader, analyzing mortgage trading and enhancing your data modeling and financial analysis skills.

Learn to work with Microsoft Copilot. Master prompting, navigate Microsoft 365 apps, and build custom agents.

Enhance your KNIME skills with our course on data transformation, column operations, and workflow optimization.

Learn how computers work, design efficient algorithms, and explore computational theory to solve real-world problems.

Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.

Master Databricks with Python: learn to authenticate, manage clusters, automate jobs, and query AI models programmatically.

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

Learn how to write effective tests in Java using JUnit and Mockito to build robust, reliable applications with confidence.

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

Create a healthcare AI agent using Haystack, an open-source framework for orchestrating LLMs and external components.

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

Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.

Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.

Explore a range of programming paradigms, including imperative and declarative, procedural, functional, and object-oriented programming.

In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

Learn how to pull character strings apart, put them back together and use the stringr package.

Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.

Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

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

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.

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