Data, AI, and Cloud Courses

Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.

Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.

Learn how to implement and schedule data engineering workflows.

Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!

This course introduces dbt for data modeling, transformations, testing, and building documentation.

Learn the fundamentals of working with big data with PySpark.

Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.

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

Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.

Apply PyTorch to images and use deep learning models for object detection with bounding boxes and image segmentation generation.

Learn how to clean data with Apache Spark in Python.

Build your OOP skills with descriptors, multilevel inheritance, and abstract base classes!

In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

In this interactive course, you’ll learn how to use functions for your Tableau calculations and when you should use them!

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

Learn about ARIMA models in Python and become an expert in time series analysis.

Discover the exciting world of Deep Learning for Text with PyTorch and unlock new possibilities in natural language processing and text generation.

Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.

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

Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.

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

Prepare for your next coding interviews in Python.

Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control

Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.

Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.

Learn how to approach and win competitions on Kaggle.

Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.

Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.