What AI can and cannot do — without math or code Duration: 21 weeks · 105 teaching days Audience: Product Owners, Product Leaders, Business Stakeholders Outcome: You develop AI judgment, not technical skills

Mental Models for Models Duration: 21 Weeks · 105 Teaching Days Audience: AI Product Owners, PMs, Business & Tech Leaders Style: Conceptual, visual, analogy-driven, zero math

Understanding Data, Quality, and Limits Duration: 21 Weeks · 105 Teaching Days Audience: Non-technical Product Owners, AI PMs, Business Leaders

Duration: 5 Months · 21 Weeks · 105 Teaching Days Audience: AI Product Owners, PMs, Business Leaders Outcome: Consistently identify high-value, feasible, responsible AI problems—and avoid costly AI mistakes.

Duration: 5 Months · 21 Weeks · 105 Days Audience: Product Owners, PMs, Business Leaders Goal: Build industry-agnostic AI intuition by recognizing repeatable patterns

Duration: 5 Months · 21 Weeks · 105 Teaching Days Audience: Non-technical Product Owners & Business Leaders Philosophy: Ethics as a business capability, not a philosophy class

We’re excited to open applications for our AI Internship Program, starting 15th January 2026. Congratulations and welcome to the School of AI. As an AI Intern, you will: - Build 25 end-to-end AI projects - Work hands-on with real-world AI systems - Create a strong GitHub + project portfolio - Learn how to showcase your work to future employers This is a build-first, execution-driven program designed for motivated learners who want real experience—not just theory.

Duration: 10 weeks Projects: 25 Web Development Projects Format: Self-paced Outcome: A complete web dev portfolio + GitHub proof-of-work + completion certificate Who this is for - Beginners who can do basic HTML/CSS and want real projects - Career switchers building a portfolio - Developers wanting modern full-stack practice

The Machine Learning Internship Program at School of AI is a hands-on, project-based experience where learners build 25 real-world machine learning projects end to end. Interns receive step-by-step guidance on data analysis, model building, evaluation, and deployment-ready code, while learning how to document and publish professional projects on GitHub. The focus is on practical skills, strong portfolios, and industry-ready ML workflows.

The School of AI Data Scientist Internship Program is a hands-on, project-driven experience where participants build 25 real-world data science projects from scratch. Interns learn data analysis, machine learning, storytelling, and deployment by writing code step by step and publishing every project to GitHub—graduating with a strong, job-ready data science portfolio.

Live: January 17th, 2026 - 9 AM EST I built a $10K–$15K/month income by creating online courses from scratch. In this live session, I’ll show you the step-by-step process, tools, and mistakes to avoid. No prior experience needed—just a computer and willingness to execute.

7 Day AI Engineer Bootcamp is a hands-on, beginner-friendly program designed to help you understand and build real-world AI systems in just one week. You’ll learn how AI works under the hood, use LLMs, build AI agents, work with embeddings and RAG, and deploy AI-powered applications. Perfect for developers, professionals, and AI enthusiasts who want practical skills—not theory. Starts January 4th. The first batch is completely free.

AI Evals for Engineers & Product Managers is a 5-day hands-on bootcamp focused on designing, measuring, and improving real-world AI systems. Learn how to evaluate LLMs, RAG pipelines, and AI agents using meaningful metrics, human feedback, and automated LLM-based evals. You’ll build production-ready evaluation frameworks, catch regressions, support model upgrades, and make confident product decisions using data—not guesswork.

From Idea to Scalable AI Product Audience - Product Managers & Product Owners - Technical Program Managers - Founders & Startup PMs - Engineers transitioning into PM roles - Business leaders managing AI initiatives Outcome By the end of Day 5, participants will design, validate, and present a production-ready AI product plan with metrics, risk controls, and execution strategy.

From LLMs to Autonomous, Multi-Agent Systems * 5 Days | ~6–8 hours/day * 40% theory, 60% hands-on * Daily mini-projects + cumulative system build * Tool-agnostic (works with OpenAI, Anthropic, open-source LLMs) Outcome:
By Day 5, participants build a fully autonomous, multi-agent AI system with memory, tools, orchestration, and guardrails.

This curated Harvard playlist delivers a clear, practical understanding of Generative AI—from how deep neural networks and large language models work to how they should be used responsibly. Topics include prompt engineering, system design beyond chatbots, alignment with human values, real-world case studies, and key risks such as bias, misinformation, and intellectual property. Designed for professionals and leaders seeking strategic, real-world AI insight.

Explains the evolution of NLP methods, the Transformer architecture, attention mechanisms, and how large language models are built, trained, tuned, and deployed. Topics Covered Include: - NLP basics (tokenization, embeddings) - Transformer architecture & attention heads - Variants like BERT, GPT, T5 - Training fundamentals - Fine-tuning techniques (SFT, LoRA) - Reinforcement-based tuning (RLHF) - Retrieval-augmented generation & agent systems - Evaluation and advanced LLM workflows

An Agentic AI Engineer does more than fine-tune models or wire up a basic RAG pipeline. They design systems that perceive, plan, act, use tools, recover from errors, and operate autonomously in complex environments. In 2026, this role sits at the intersection of software engineering, AI systems design, and human problem-solving. This roadmap lays out a clear, step-by-step path for an Agentic AI Engineer—focusing not just on tools, but on how to think like an engineer of intelligent systems.

Who this course is for - AI Engineers & Agentic AI Builders - Researchers exploring post-LLM architectures - Advanced Product / Platform Architects - Anyone building autonomous, self-improving AI systems What makes this course unique This is not a prompt-engineering or fine-tuning course. This course teaches how models reason recursively, inspect themselves, and improve outputs over time.

This course builds a complete Agentic AI knowledge series. We define the full Agentic AI stack, then we cover Attention Mechanisms, Large Language Models as reasoning engines, Planning (ReAct, CoT, ToT), and Memory Systems for long-term autonomy.

AI Agents for Everyone introduces learners to building and deploying intelligent agents using modern AI tools like ML, NLP, AutoGPT, LangGraph, CrewAI, and IBM Bee. The course blends theory with hands-on projects, covering decision-making, autonomy, collaboration, and real-world applications across industries. Learners also explore ethics, governance, and future trends, gaining the skills to design responsible, production-ready AI agents with confidence.

Modern enterprises need more than automation—they need context-aware AI. The Model Context Protocol (MCP) enables intelligent agents with shared memory, explainability, and governance. This course teaches leaders how to design MCP-based systems that integrate with enterprise tools, ensure compliance, and scale securely. You’ll learn how to move from pilots to production using real-world use cases across operations, legal, HR, and IT—without needing a technical background.

AI for Financial Leaders: Strategy, Risk & Growth equips CFOs and finance leaders to apply AI across forecasting, risk, automation, and decision-making. The program covers AI-powered FP&A, fraud detection, compliance, treasury, and intelligent reporting, translating machine learning and agentic AI into practical financial use cases. Participants gain frameworks to evaluate AI tools, manage risk, drive ROI, and lead finance transformation with confidence.

This course explains how agentic AI transforms enterprise workflows by combining Amazon Bedrock AgentCore, IBM watsonx, and modern agent frameworks. It covers AgentCore runtime, tools, memory, policies, and observability; watsonx for governance and business workflows; LangGraph for agent logic; and reference architectures, implementation patterns, operating models, and strategies for scaling agents across the enterprise.

Credential Type: Professional Certificate Duration: 2-Day Bootcamp Audience: Beginners, professionals, managers, non-ML engineers What this cert signals • Can use AI effectively, not just talk about it • Hands-on experience with real AI tools and workflows • Understands risks, limitations, and responsible use Verified Skills • Generative AI & LLM usage • Prompt engineering fundamentals • AI with custom data (RAG concepts) • AI agents (single-agent workflows) • Responsible AI basics

AI Product Manager Explorer Course is a 7-day, non-technical program for product managers, founders, and business leaders to master AI-driven product strategy. Learn how to identify AI use cases, work with technical teams, design AI roadmaps, evaluate models, and manage ethical risks—without coding. Through real-world cases and hands-on exercises, you’ll build a complete AI product plan and earn a certification that signals readiness to lead AI-powered products.

100 AI Agents in 100 Days is a hands-on, project-based course that teaches you to build real-world AI agents from scratch—one per day. Each lesson is independent, letting you learn at your own pace while creating agents for productivity, research, business, automation, and decision-making. You’ll design agents with logic, memory, guardrails, and autonomy, progressing to advanced multi-agent systems. No prior AI experience required—just practical, real-world AI you can actually use.

“1000 Days of AI Projects” is a hands-on, project-first journey where you build one real AI project every day—step by step. From text processing and machine learning to agents, automation, and applied AI systems, each day focuses on a practical, standalone build you can reuse in real-world work. No fluff, no theory overload—just consistent, production-ready AI projects that grow your skills one day at a time.