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How it Works

  • Each course is 2.5 hours long

    The primer series is taught LIVE

    If you miss the live course, each session is available on-demand the next day

  • Each course includes hands-on exercises

    33 coding exercises allow you to learn hands-on skills and improve your learning outcomes.

  • Learn at your own pace

    Build your AI foundation with beginner-friendly on-demand courses in Python, SQL, data literacy, and GenAI.

What You’ll Learn


WEEK 1

AI & ML Foundations for Agentic Systems

Sept 10th | 2:00 - 4:30pm ET

This AI course introduces participants to artificial intelligence (AI) and machine learning basics. We will first explore the various types of AI and then progress to understand fundamental concepts such as algorithms, features, and models. We will study the machine learning workflow and how it is used to design, build, and deploy models that can learn from data to make predictions. This will cover model training including supervised, unsupervised learning, and generative AI

Week 2

AI-Assisted Coding for AI Development

Sept 15th | 2:00 - 4:30pm ET

This session is your on-ramp to AI-native coding — where natural language replaces boilerplate and speed matters more than syntax. You’ll explore how tools like Lovable, Claude Code and Colab can help you write and reason through Python code, generate functions, and structure real-world AI projects.

Data Prep for Machine Learning and LLMs with Python

Sept 17th | 2:00 - 4:30pm ET

Data prep is the cornerstone of any data-driven project, and Python stands as one of the most powerful tools in this domain. In preparation for the ODSC AI conference, our specially designed course on “Machine Learning Data Prep with Python” offers attendees a hands-on experience to master the essential techniques. From cleaning and transforming raw data to making it ready for analysis, this course will equip you with the skills needed to handle real-world data challenges.

Week 3

AI Engineering Foundations

Sept 24th | 2:00 - 4:30pm ET

We’ll cover topics like modular code for ML pipelines, data versioning, model evaluation strategies beyond accuracy, and tools for tracking experiments and model performance. You’ll explore what makes an AI system reliable, observable, and maintainable — and how to structure code and workflows that can grow with your use case.

Week 4

LLMs: Architecture, Fine-Tuning & Evaluation

Oct 1st | 2:00 - 4:30pm ET

This session gives you a working understanding of the tools and patterns behind real-world LLM systems. You’ll explore how large language models process input, how to design effective prompts, and when fine-tuning or parameter-efficient training makes sense for your use case.

You’ll learn how to structure data for LLM workflows, select and evaluate model outputs beyond just accuracy or BLEU scores, and apply key principles for prompt safety, latency, and cost-performance tradeoffs. Hands-on examples will demonstrate how to build and test LLM components using modern frameworks.

Week 5

Context and Memory for AI Agents

Oct 8th | 2:00 - 4:30pm ET

This hands-on course provides a comprehensive introduction to Retrieval-Augmented Generation (RAG), exploring how it combines traditional retrieval techniques with generative AI models to deliver contextually accurate and real-time responses. Starting with the limitations of traditional search systems, the course delves into the components and workflow of RAG, including data processing, indexing, and querying methods. Through hands-on examples and best practices, learners will gain familiarity with key tools and embedding techniques, such as LangChain, vector stores and ranking, for building effective RAG applications.

The final module covers advanced frameworks and evaluation strategies, empowering students to implement and optimize RAG systems for various business and technical applications.

Week 6

: Building AI Agents and Agentic Workflows

Oct 15th | 2:00 - 4:30pm ET

Introduction to AI Agents & RAG is a comprehensive course designed to equip students with the foundational knowledge and practical skills needed to build and implement AI agents. This course explores the core concepts of AI agents, including their types, key components, and tools, as well as advanced topics like agent chains and Agent Frameworks.

Through a blend of theoretical insights and hands-on exercises, participants will learn how to leverage powerful frameworks like LangChain and LlamaIndex to create intelligent, context-aware systems that can handle complex tasks. Whether you are new to AI or looking to deepen your expertise, this course offers a structured pathway to mastering the latest advancements in AI agent technology.

Capstone: Build and Deploy Your Agentic AI Application

Oct 22nd | 2:00 - 4:30pm ET

In this capstone project, students will progressively build a useful, end-to-end AI agent by combining Retrieval-Augmented Generation (RAG), language model fine-tuning, and interactive AI agent design. Each module of the bootcamp will focus on constructing a core component of the agent, from fine-tuning a language model (LLM) on domain-specific data to setting up a real-time RAG pipeline. By the end, students will have a customized AI agent capable of delivering accurate, context-aware responses to user queries.

With hands-on guidance, template notebooks, and regular insights, students will build each part step-by-step, gaining confidence as they progress. This “build-as-you-go” approach not only strengthens technical skills but also provides a real-world AI application students can showcase, demonstrating their expertise in building the latest AI solutions.

Optional On-Demand Prerequisites:




Regardless of your current skill level

AI Engineering Accelerator will help transform you from AI novice to confident practitioner

  • NOVICES

    This course is designed for AI novices, including professionals, students, and enthusiasts eager to dive into the world of AI

  • Engineers

    Software engineers and other technical professionals seeking to deepen their understanding of AI and enhance their skills

  • Product Managers and Builders

    Product Managers and Builders who want to understand and build AI into their products, leveraging AI-powered innovations to stay competitive

Instructors Bio

Sheamus McGovern

Founder of ODSC, and Venture Partner/Head of AI at Cortical Ventures

Sheamus McGovern is the founder of ODSC (The Open Data Science Conference). He is also a software architect, data engineer, and AI expert. He started his career in finance by building stock and bond trading systems and risk assessment platforms and has worked for numerous financial institutions and quant hedge funds. Over the last decade, Sheamus has consulted with dozens of companies and startups to build leading-edge data-driven applications in finance, healthcare, eCommerce, and venture capital. He holds degrees from Northeastern University, Boston University, Harvard University, and a CQF in Quantitative Finance.

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