Overview

## Become an AI Engineer: From Data Science to Production-Ready AI Start your journey to becoming an AI Engineer by building the essential skills to take AI models from development to deployment. In this Track, you'll gain hands-on experience with the latest AI technologies and best practices, enabling you to create robust, production-ready AI solutions. ## Master the AI Development Lifecycle Progress through the key stages of the AI development process, including: * Training and evaluating machine learning models using Python libraries like scikit-learn and PyTorch * Working with real-world datasets to solve practical problems across various domains * Fine-tuning state-of-the-art Large Language Models (LLMs) like Llama 3 for natural language tasks * Integrating AI models into applications using frameworks like LangChain * Applying MLOps principles to ensure reliable and scalable AI deployments ## Gain Hands-on Experience with Cutting-Edge AI Technologies Explore the tools and techniques driving the AI revolution through practical experience with deep learning architectures, including CNNs, RNNs, LSTMs, and GRUs. You'll also work with transformer-based models and their applications in natural language processing, gaining insight into their impact on modern AI. Additionally, you'll learn explainable AI methods to build transparent and accountable AI systems while applying responsible AI practices to manage data effectively throughout the AI lifecycle. ## From LLMs to Production: Putting AI into Practice Apply your skills to real-world scenarios that mirror the challenges faced by AI Engineers. You'll learn to fine-tune LLMs like Llama 3 on custom datasets, integrate them into applications using LangChain, and deploy these solutions into production environments. Discover how MLOps principles like testing, version control, and continuous integration can help you build reliable and scalable AI systems. ## Designed for Data Scientists Transitioning to AI Engineering This Track is ideal for data scientists looking to expand their skill set and take on AI engineering roles. Building on your existing knowledge of machine learning and Python, you'll acquire the additional skills needed to design, develop, and deploy production-grade AI solutions. No prior experience with AI engineering or MLOps is required. ## Launch Your Career as an AI Engineer Upon completing this Track, you'll have the confidence and the portfolio to: * Apply for AI Engineer positions across industries * Collaborate with cross-functional teams to deliver end-to-end AI solutions * Implement responsible AI practices and build trustworthy AI systems * Stay at the forefront of the rapidly evolving AI landscape Take the first step towards becoming an AI Engineer and unlock new career opportunities in this exciting field.

Syllabus

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!

Unsupervised Learning in Python

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

Working with Hugging Face

Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub.

Introduction to Deep Learning with PyTorch

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

Explainable AI in Python

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

Intermediate Deep Learning with PyTorch

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

Developing Multi-Input Models For OCR
Responsible AI Data Management

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

Introduction to LLMs in Python

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

Analyzing Car Reviews with LLMs
Working with Llama 3

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

MLOps Concepts

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

Software Engineering Principles in Python

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

Introduction to Git

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

Introduction to Testing in Python

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

Taught by

Benjamin Wilson, Adam Spannbauer, George Boorman, Folkert Stijnman, Maham Khan, Thomas Hossler, Alexander Levin, Michał Oleszak, Jasmin Ludolf, Iván Palomares Carrascosa, Maria Prokofieva, Imtihan Ahmed, and Fouad Trad