This A.I. - Three Bootcamp Bundle includes registration for three separate 2-day bootcamps, Data Science and Machine Learning, Large Language Models, and Agentic A.I..  Each bootcamp is also available for stand alone registration at the price of $1,499 each, so purchasing this limited time only bundle will provide a savings of more than $500.

Data Science and Machine Learning (March 12-13, 2026)

The Data Science and Machine Learning Bootcamp, the first in our A.I. bootcamp series, provides an engaging introduction to Data Science and Machine Learning for practitioners. It covers foundational concepts such as data cleaning and exploratory analysis using Python libraries like Pandas and NumPy. Participants will gain hands-on experience with essential tools and techniques, progressing from data preprocessing and visualization to building robust machine learning models, empowering them with the skills needed to tackle real-world data challenges effectively.

By the end of this course, students should be able to:

1. Gain practical experience in data preprocessing, visualization, and model development.
2. Understand and apply key machine learning techniques, including regression, classification, and clustering.
3. Build and implement both supervised and unsupervised ML models.
4. Learn to analyze time series data and develop forecasting models.
5. Explore the fundamentals of neural networks and their applications.

Large Language Models (April 6 & 8, 2026)

The Large Language Models Bootcamp, the second in our A.I. bootcamp series, is designed to provide a comprehensive introduction to Large Language Models (LLMs) and their practical applications. Participants will explore the evolution of language models, learn how tokenization and embeddings work, and dive into the inner workings of transformer-based architectures. It covers essential topics like prompt engineering, advanced text generation techniques, Retrieval-Augmented Generation (RAG) and LLM Agents. Through guided exercises and real-world tools and platforms like Hugging Face, LangChain, etc. attendees will gain practical experience building LLM-powered systems.

By the end of the course, participants will be able to:

1. Describe the evolution of language models and the significance of LLMs in modern NLP
2. Understand the architecture and functionality of transformer-based models
3. Apply prompt engineering techniques for various tasks such as summarization, Q&A, and creative generation
4. Use libraries and tools like Hugging Face Transformers, LangChain etc. to develop LLM-based solutions.
5. Build a simple Retrieval-Augmented Generation (RAG) pipeline for domain-specific applications.
6. Design and implement a small-scale LLM-powered project demonstrating practical skills learned in the course.

Agentic A.I. (May 21-22, 2026)

The Agentic A.I. Bootcamp, the third and final bootcamp in our A.I. bootcamp series, provides a complete, practical introduction to building modern AI agents that can reason, plan, use tools, retrieve information, collaborate, and operate safely in real-world settings. Learners begin by understanding what AI agents are and how they differ from traditional software and simple LLM applications, then explore core agent architectures, the modern AI agent stack, and the fundamentals of using LLMs as reasoning engines. Through guided exercises, participants build agents that use function calling, retrieval, vector databases, and memory systems to maintain context and act autonomously. The course also explores advanced capabilities such as multi-step reasoning, task planning, and multi-agent workflows enabling participants to create agents that decompose tasks, self-correct, and coordinate with other agents. The final modules focus on production readiness, including reliability, safety guardrails, evaluation, monitoring, and API-based deployment. By the end of the course, participants will develop a strong foundation for designing and deploying real-world agentic AI systems.

By the end of the course, participants will be able to:

1. Design and architect AI agent systems for real-world applications
2. Implement agents using modern frameworks and best practices
3. Build multi-agent systems with effective coordination
4. Implement memory and state management for agents
5. Deploy production-ready agents with proper guardrails and monitoring
6. Evaluate and iterate on agent performance