Associate AI Engineer for Data Scientists

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Learn how to build your first neural network, adjust hyperparameters, and tackle classification and regression problems in PyTorch.

Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.

Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.

Develop a multi-input model to classify characters from scanned documents.

Learn the theory behind responsibly managing your data for any AI project, from start to finish and beyond.

Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!

Use LLMs to solve diverse language tasks for a car dealership company.

Explore the latest techniques for running the Llama LLM locally, fine-tuning it, and integrating it within your stack.

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

Discover the fundamentals of Git for version control in your software and data projects.

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