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Become an Agentic AI Engineer by building real-world, production-ready AI agents that autonomously reason and execute complex tasks.

Professor & AI Expert (540K+ Students)

Microsoft

General Motors

Samsung

HSBC

Dr. Ryan Ahmed

Professor, AI expert, & YouTuber who has taught 540,000+ learners worldwide.

Cohort 1

Cohort 2

⭐"Join the instructor trusted by 540K+ students with 40K+ five-star reviews and 50M+ minutes of AI training watched worldwide."Agentic AI Engineering is 2026's most in-demand skill.Software is shifting from static systems to AI-native architectures, where autonomous agents reason, plan, and execute complex work.The problem: Most developers are stuck building API wrappers, and they lack the depth to manage state, memory, &amp; multi-agent coordination in real production systems.The Solution: This bootcamp is your bridge to elite Agentic AI engineering. We move beyond the basics to architect robust agent fleets in which specialized agents collaborate autonomously in production.What makes this course unique?<ul><li>Production-Grade Architecture: Engineer real systems using LangGraph, CrewAI, &amp; AutoGen.</li><li>Automate Agent Workflow Creation: Use Claude Code &amp; Codex to generate, refactor, and evolve agent workflows.</li><li>Enterprise Reliability: Design for guardrails, persistent memory, observability, &amp; human-in-the-loop safety. </li></ul><h3>Real-world Capstone Project</h3>Design &amp; deploy a production-grade, portfolio-ready agentic AI system for real-world use, demonstrating senior-level Agentic AI engineering skills.

Master Agentic AI Engineering & Architect Production-Grade Autonomous Systems

Architect, build & deploy production-grade agentic AI systems end-to-end

Orchestrate Autonomous Multi-Agent Teams

Master Tool Use & The Model Context Protocol (MCP)

Automate Workflows with AI Coding Assistants

Engineer Context-Aware Agents with Retrieval Augmented Generation (RAG)

Deploy, Monitor & Benchmark for Enterprise

Software Engineers &amp; Developers wanting to master state-of-the-art Agentic AI frameworks and build autonomous, production-grade AI agents.

Technical Founders &amp; CTOs who want to architect scalable, fault-tolerant autonomous systems that serve as your product's core engine.

Modern ML &amp; Data Science Practitioners who want to evolve from static models to dynamic Agentic AI workflows that reason, plan, &amp; execute.

"Python for Beginners" is a foundational course designed for absolute beginners, to introduce you to programming.

Founder and CEO, ScholarU

Roberto Reif

20+ years of Technology and Data Trainings

Winter 2026

Summer 2026

Why should I learn Python?Python is a versatile, beginner-friendly programming language that holds significant value across numerous industries, including web development, data analysis, artificial intelligence, machine learning, and scientific computing. Its broad applicability has created a strong demand for Python skills, opening doors to excellent job prospects and career advancement.About the CourseThis course is designed to prepare you with the essential skills to use Python effectively. You'll dive into fundamental data types such as integers, strings, floats, booleans, lists, dictionaries, sets, and tuples, while mastering core programming concepts like conditionals, loops, functions, and modules. With hands-on exercises, you'll apply what you learn in real-world scenarios.Who Should Take This Course?This course is intended for individuals starting a career in coding, data analysis or data science, as well as professionals seeking to expand their expertise in the field.Course Duration:The course will be delivered live online through Zoom, offering a total of 10 hours of instruction.

Absolute beginners can confidently learn Python!

Get started with Python basics, including syntax and setup

Use comments, variables, and data types effectively

Apply conditionals and booleans to control program flow

Work with lists, ranges, dictionaries, sets, and tuples to manage data

Implement loops and functions to write efficient, reusable code

Import modules and format strings to extend and refine functionality

Gmail account

We will run our live online sessions with Zoom

Zoom account

A thirst for knowledge

Career Changers - Professionals who are transitioning into tech roles and want to learn Python as a first programming language.

Aspiring Data Analysts or Scientists - Individuals looking to build Python skills in data analysis, machine learning, or data science roles.

Excel Users - Experts aiming to enhance their skill sets by mastering advanced and more efficient analysis techniques.

You will implement Quantum SVMs, Quantum neural networks, Quantum feature maps, and hybrid quantum-classical models in Qiskit 2.3.

Quantum Machine Learning Scientist

Dr. Muhammad Faryad

Classical machine learning already pushes limits with kernel methods, optimization, and high-dimensional feature spaces. As models scale, so do the bottlenecks—training cost, feature engineering complexity, and diminishing returns.Quantum computing introduces a fundamentally different regime:<ul><li>⚙️ Exponentially large Hilbert spaces for richer representations</li><li>🧠 Quantum feature maps for embedding data in new ways</li><li>🔁 Variational quantum models with compact expressivity</li><li>📈 Native kernel estimation via quantum circuits</li></ul>But the real challenge is not theory—it is applicability.<ul><li>❓ Where do quantum models fit in an ML pipeline?</li><li>❓ When does a quantum kernel outperform a classical one?</li><li>❓ How do you move from equations to working code on real hardware?</li></ul>This course is designed to answer exactly these questions.<ul><li>🎯 Focus on what is usable today—not distant promises</li><li>💻 Hands-on implementation using Qiskit</li><li>🔬 Build and test quantum feature maps, QSVMs, and variational classifiers</li><li>🔗 Learn how to integrate quantum models into existing ML workflows</li></ul>By the end, you will not just understand quantum ML—you will know when, why, and how to apply it in practice.

Upskill your ML career: Integrate quantum computing with machine learning.

Build and train quantum models end-to-end

Master quantum data encoding strategies

Develop production-ready quantum programming skills

Understand constraints of current quantum hardware for practical programming

This course assumes knowledge and fluency with basic classical ML concepts, including supervised ML, SVMs, kernels, and neural networks.

Classical Machine Learning Concepts 

The course will focus on using Qiskit for quantum ML programming, assuming students are already experienced in implementing ML problems. 

Working Knowledge of Numpy, Sklearn, Pandas

This course is meant for experienced ML/AI engineers/leaders who are already well-versed in classical AI tools and want to learn quantum ML.

This course is NOT for beginner ML students

Senior ML Engineers — Extend production ML expertise into quantum kernels, quantum neural networks, and hybrid training loops

AI / ML Researchers — Reproduce, benchmark, and rigorously evaluate quantum ML models with solid theoretical depth and practical codes.

Tech Lead / Innovation Strategists — Develop insights for quantum machine learning, hardware limits, and credible quantum-ready use cases.

AI Fundamentals

Apply AI in your role

Career Development

AI Fundamentals

Career development

Craft

AI Fundamentals

Career development

Craft

AI Fundamentals

Career development

Craft

AI Fundamentals

Craft

AI Fundamentals

Leadership fundamentals

AI Fundamentals

Leadership fundamentals

By Role

Programming

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