Associate AI Engineer for Data Scientists

Begin building AI solutions and gain the career-building skills you need to succeed as an AI Engineer, from model development to deployment into production! In this track, you’ll train and evaluate robust predictive models on real-world datasets across a variety of domains. Starting with the fundamentals of machine learning, you’ll get hands-on with some of the most popular Python libraries for machine learning and deep learning, including scikit-learn, PyTorch, and many more. As you progress, you’ll get hands-on with Large Language Models (LLMs) for a variety of natural language tasks. You'll learn to fine-tune Llama 3 on custom data and integrate this into a LangChain application to begin surfacing predictions to end-users. Finally, you'll discover what it takes to move an AI model from a notebook into production. You'll build foundational skills in MLOps, including testing, version control, and monitoring performance in production.

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Supervised Learning with scikit-learn

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!

4 hours

Unsupervised Learning in Python

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

4 hours

Introduction to Deep Learning with PyTorch

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

4 hours

Intermediate Deep Learning with PyTorch

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

4 hours

Developing Multi-Input Models For OCR

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

2 hours

Responsible AI Data Management

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

4 hours

Introduction to LLMs in Python

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

4 hours

Analyzing Car Reviews with LLMs

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

2 hours

Working with Llama 3

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

4 hours

MLOps Concepts

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

2 hours

MLOps Deployment and Life Cycling

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

4 hours

Introduction to Git

Familiarize yourself with Git for version control. Explore how to track, compare, modify, and revert files, as well as collaborate with colleagues using Git.

4 hours

Software Engineering Principles in Python

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

4 hours

Introduction to Testing in Python

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

4 hours

Monitoring Machine Learning Concepts

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

2 hours

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