Learn Generative AI by building practical applications you can explain in interviews and showcase on your resume and LinkedIn.
No prior AI experience required. Learn through guided, hands-on projects and on-demand lessons.
Tools & Platforms You'll Work With
Knowing AI concepts is only the beginning. To stand out, you need to apply what you learn by building projects you can demonstrate, explain, and add to your portfolio.
Leapstone AI takes you from the foundations of Generative AI to practical applications through a structured, hands-on learning path.
Understand the key concepts behind Generative AI, not just which tools to click.
Apply what you learn through projects involving RAG, AI agents, multimodal AI, and app deployment.
Complete a capstone project and learn how to present your work on your resume, LinkedIn, and in interviews.
Move beyond watching tutorials. Throughout the course, you'll build hands-on Generative AI applications, understand how they work, and turn your best work into a portfolio you can discuss in interviews.
Build an AI application that answers questions from uploaded documents using Retrieval-Augmented Generation.
You'll practice: document ingestion, retrieval, embeddings, vector databases, and context-aware responses.
Build an AI agent that uses the Model Context Protocol to pull live news sources and deliver structured, summarized updates on demand.
You'll practice: MCP fundamentals, tool connections, structured outputs, and multi-step agentic workflows.
Build an AI agent that can follow instructions, use tools, and complete a sequence of tasks.
You'll practice: agentic workflows, tool use, human-in-the-loop steps, and workflow automation.
Create an application that works with more than one type of input, such as text, audio, images, or video.
You'll practice: speech-to-text, text-to-speech, text-to-image generation, and multimodal workflow design.
Customize a language model on a focused dataset and compare its behavior against prompting and retrieval-based approaches.
You'll practice: model customization, fine-tuning workflows, and evaluating trade-offs between fine-tuning, prompting, and retrieval.
Choose a real-world problem and build an end-to-end AI application around it.
You'll complete: problem definition, solution design, working prototype, project review, and portfolio presentation.
Every project is designed to help you understand not only how to build an AI application, but also how to explain the problem, approach, tools, and limitations behind it.
Follow a structured journey that moves from understanding the fundamentals to building, deploying, and presenting practical AI applications.
Learn the foundations of Generative AI, Python, prompts, models, and the modern AI ecosystem.
Create guided applications using RAG, AI agents, multimodal AI, and AI-assisted development workflows.
Turn your projects into usable applications and understand the tools involved in hosting and deployment.
Complete a capstone, organize your portfolio, improve your resume, and prepare to discuss your work in interviews.
Get a preview of the concepts, tools, and practical outcomes covered across the eight-week learning path.
Understand the concepts behind modern AI and learn how to work with prompts and language models.
Build an application that retrieves relevant information before generating an answer.
Create workflows in which AI can use tools and complete multi-step tasks.
Work with text, audio, images, and video to understand how different AI capabilities connect.
Turn a project into a usable application and understand the path from prototype to deployment.
Bring your skills together to solve a problem with an end-to-end AI application.
Watch the free class to understand how a practical AI project works, what skills are involved, and how project-based learning can help you build a stronger portfolio.
If you found this project useful, the full Generative AI Launchpad gives you the structured path to build more applications, complete a capstone project, and create a portfolio you can confidently discuss.
Follow an eight-week sequence instead of jumping between disconnected tutorials.
Practice by creating AI applications, not just watching explanations.
Learn at a time that fits your schedule and revisit lessons whenever you need to review a concept.
Apply your learning to a complete project based on a real-world problem.
Learn how to present your projects, describe your technical decisions, and improve your resume and LinkedIn profile.
Complete the course and capstone requirements to receive a certificate you can showcase on your resume and LinkedIn.
Hi! I am Kaushik Annambhotla, your instructor for this course. I am an AI Product Manager and an AI consultant who created multiple apps both at work and in personal life. Check a sample of my portfolio below.
Leapstone AI was created to help students move beyond passive AI tutorials and build projects they can actually explain. The course combines clear foundations, guided code walkthroughs, practical tools, and portfolio-building support so beginners can learn by doing.
You won't be expected to memorize disconnected concepts. You'll be guided through the reasoning, building process, and explanation behind each project.
See Sample PortfolioYes. The course begins with foundational concepts and introduces the technical material progressively. It includes both code and no-code approaches to help beginners understand how AI applications work.
No prior professional programming experience is required. The course covers the relevant Python foundations and guides you through the code needed for the applications.
You will work on practical applications involving Generative AI foundations, RAG, AI agents, multimodal AI, deployment, and a final capstone project.
Plan for approximately one to two hours per week for the core learning path, plus additional time if you want to explore the projects in greater depth or complete the course sooner.
Yes. The video lessons are available on demand, allowing you to learn at a time that fits your schedule and revisit topics when needed.
Students can begin with free versions of the tools where available. Some AI platforms may require optional usage credits depending on how much you use them.
Yes, students who complete the stated course and capstone requirements receive a certificate.
You will have practical projects to describe, demonstrate, and link from your resume or LinkedIn profile. The course also includes guidance on presenting your projects and technical decisions.
1 Year from enrollment.
The course is not refundable. Please read the full refund and cancellation policy from our website footer.
Follow a structured path, build practical projects, complete a capstone, and develop the confidence to discuss your work in interviews.
Start with the free class if you want to experience the teaching approach before enrolling.
