Students Learn How AI Actually Works
Not just how to use ChatGPT.nt.
✦ Senior Coder · Python Programs · Level 4 · Ages 12–16+
Build the Technology Behind ChatGPT, AI Search, Recommendation Engines & Autonomous AI Agents
Students progress from machine learning foundations through classical NLP, neural language models, transformers, large language models, Retrieval-Augmented Generation (RAG), and Agentic AI. By the end of the program, students will understand how modern AI systems reason, retrieve information, use tools, maintain memory, and collaborate through multi-agent architectures.
⏱ 65 Hrs + 5–8 Hrs Assessment
🔴 Online - Personalized Live Classes
💻 Python Programming Language
🎖 Certificate On Completion
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Artificial Intelligence is transforming software, healthcare, finance, education, robotics, entertainment, scientific research and almost every technology-driven industry.
But understanding AI today requires more than knowing how to use ChatGPT.
Students need to understand how AI systems work, how software is built, how data becomes intelligence, and how developers can work effectively alongside AI tools.
The Advanced AI Engineer Program takes students on a complete journey from Machine Learning and Natural Language Processing to Transformers, Large Language Models, RAG and Agentic AI.
Along the way, students build real AI systems inspired by technologies used in modern industry.
And now, they also learn an increasingly important professional skill:
How AI Engineers Use AI to Build AI.
AI is rapidly becoming a foundational technology across industries.
Students who understand the technology behind AI will be better prepared for a future where software development, data and artificial intelligence increasingly overlap.
AI engineers need more than the ability to use an AI tool.
They need to understand:AI Can Generate Code. It Cannot Replace Understanding.
AI coding tools can generate impressive programs in seconds.
But professional developers still need to understand:
It may seem that AI makes programming less important.
In reality, AI makes understanding programming even more valuable.
When AI generates 100 lines of Python, a student who cannot read Python cannot reliably determine whether those 100 lines are correct.
That is why programming remains at the heart of this program.
Students learn Python, algorithms and AI concepts before and alongside AI-assisted development.
They are not trained to simply ask AI for code.
They are trained to become the person who can understand, evaluate, modify, test and improve that code.
Students therefore learn both sides of modern development:
The goal isn't to code less.
The goal is to become better at building with code and AI.
Many AI courses teach students how to use existing AI applications.
This program teaches students how the technology behind those applications works.
Students progress through the evolution of modern AI:
This gives students a much deeper understanding of the technology powering today's AI ecosystem.
Students begin with the foundations of Artificial Intelligence and Machine Learning.
Students build projects that demonstrate how machines learn patterns from data and use those patterns to make predictions.
Before modern LLMs, computers already had ways of processing human language.
Projects include text classifiers, search systems, news analyzers and intelligent text-processing tools.
Students then move into Neural NLP and Deep Learning.
They discover how machines move beyond simple keywords and begin representing meaning mathematically.
Students follow the technological evolution that led to today's Generative AI.
Students don't simply learn that ChatGPT exists.
They learn the ideas and technologies that made systems like it possible.
Students move from understanding models to building applications around them.
They build applications that use modern AI models as part of larger software systems.
Modern AI applications often need access to information outside the model's original training data.
Students build RAG systems capable of retrieving information from knowledge sources before generating responses.
The next generation of AI applications is moving beyond simple question-and-answer systems.
Students learn how AI agents can:
AI-Assisted AI Engineering
AI is now becoming part of the software development process itself.
But professional AI engineers cannot simply accept whatever an AI tool produces.
They need to understand the technology well enough to evaluate it. That is what students learn in the final module.
“Build this for me.”
“Here is the problem. Here is my approach. Help me investigate this implementation, identify weaknesses, compare alternatives and improve the solution.”
That difference matters.
Students learn to use AI as a development partner, rather than treating it as a replacement for programming knowledge.
Students learn a professional workflow:
Understand the problem before asking AI for help.
Plan the solution and architecture.
Write the initial implementation.
Run and evaluate the program.
Use AI to investigate specific problems.
Critically examine the AI's suggestions.
Modify the implementation where appropriate.
Verify that the change actually works.
Explain the final solution without relying on AI.
AI assists the engineer. The engineer remains responsible for the solution.
Students continue building substantial AI projects throughout the program.
Projects may include:
Students work with concepts and technologies connected to real AI products and systems. They explore the ideas behind:
Like those used by streaming and e-commerce platforms.
Systems that retrieve and rank relevant information.
Systems that understand questions and generate responses.
AI applications that retrieve information from knowledge bases.
Systems capable of using tools and performing multi-step tasks.
Multiple specialized AI agents collaborating on complex workflows.
The objective isn't to recreate a commercial product exactly. It is to understand the engineering ideas behind the technology.
Every stage of the program produces practical work.
Students finish with projects demonstrating experience across:
The final capstone requires students to design, build, test and present an industry-inspired AI solution.
Build the technologies behind ChatGPT, Claude, Gemini, Netflix Recommendations, Perplexity AI, Enterprise AI Assistants, and Autonomous AI Agents.
Students design, build, test, and present an industry-inspired AI solution using technologies learned throughout the course.
Capstone projects:
Students independently design and develop a complete AI system demonstrating:
Core Competencies
Final Deliverables
Students complete the program with practical experience in:
The program introduces foundations relevant to many future technology careers.
Students may eventually progress toward:
Design and develop intelligent software systems.
Build and deploy machine learning systems.
Develop systems that process and understand human language.
Work with large language models and generative AI systems.
Build AI-powered products and applications.
Use data, statistics and machine learning to solve complex problems.
Investigate new approaches to intelligent systems.
Design larger AI systems and technology architectures.
Build AI agents capable of using tools, planning and performing tasks.
Combine strong software engineering skills with modern AI technologies.
These career pathways are already reflected in the program's current curriculum.
The goal is not to predict exactly which programming tools students will use ten years from now.
The goal is to give them the ability to adapt to new technology.
THE ITSMYBOT ADVANTAGE
Every child learns differently. That's why we combine exceptional educators, personalised guidance, and continuous progress tracking to create a learning experience parents can trust and children enjoy.
Not just how to use ChatGPT.nt.
Students develop real programming knowledge rather than becoming dependent on AI-generated code.
Students encounter concepts such as Transformers, LLMs, RAG, vector databases and AI agents.
Students continuously build applications rather than learning concepts in isolation.
Flexible scheduling options including evenings and weekends.
Students understand both the capabilities and limitations of AI.
Students learn to question AI outputs and verify technical decisions.
Students finish with a collection of increasingly sophisticated AI projects.
Students develop the combination of coding + AI + problem-solving + engineering thinking that will remain valuable as technology evolves.
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Pricing varies based on the teacher, teaching style, and course chosen - because every learner is different. We'll tell you the exact cost after your free trial class, when you know what fits best.
Life happens. If your needs change after any month, you have complete freedom to adjust. We'd love for you to stay - but we'll never make you feel stuck.
Why don't we show fixed prices? Because we don't believe in one-size-fits-all. Different teachers bring different expertise, teaching methods, and experience - and that's reflected in how we price. The best way to find your fit is to take the free trial lesson first, meet your instructor, and then decide. Pricing is shared after the trial - transparently, with no pressure.
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FAQs
The Advanced AI Engineer Program is an advanced AI and programming course designed for Senior Coder students who want to understand how modern AI systems are built. Students learn Python and progress through Machine Learning, Natural Language Processing, Deep Learning, Transformers, Large Language Models, RAG, and Agentic AI while building practical AI projects.
Students learn how AI systems work and how to build them using Python. The program covers Machine Learning, NLP, neural networks, word embeddings, RNNs, LSTMs, attention mechanisms, Transformers, BERT, GPT, Large Language Models, prompt engineering, vector databases, RAG, AI agents, memory systems, ReAct, LangGraph, CrewAI, and multi-agent systems.
Yes. Students learn AI-assisted software development as part of the program. They learn how to use AI to explore technical ideas, understand code, debug programs, generate test cases, review code, explain concepts, improve documentation, and compare different implementation approaches. They also learn to evaluate and verify AI-generated code rather than simply accepting it.
The program goes beyond using existing AI applications. Students learn the technologies and engineering concepts behind modern AI systems, progressing from Machine Learning and NLP to Transformers, LLMs, RAG, and Agentic AI. They learn to build and evaluate AI systems rather than simply use AI tools.
The program is built as a progressive learning journey, beginning with Machine Learning foundations and continuing through increasingly advanced AI concepts. Students develop programming and engineering skills alongside their AI learning, including Python, algorithms, problem-solving, data, testing, debugging, and software design.
The program includes 65 hours of learning modules, followed by 5–8 hours of assessment. The exact pace can vary depending on the student's learning journey and schedule.
Students work on practical AI projects throughout the program. Projects include recommendation systems, search engines, text classifiers, AI assistants, semantic search platforms, knowledge-base assistants, RAG applications, research agents, memory-powered AI assistants, and multi-agent systems.
Retrieval-Augmented Generation, or RAG, teaches AI systems to retrieve relevant information from knowledge sources before generating responses. Students learn embeddings, vector search, chunking, semantic search, reranking, retrieval pipelines, knowledge grounding, and how RAG can be used to build enterprise AI systems.
Students learn how AI agents can use tools, make decisions, plan tasks, remember information, and perform multi-step workflows. They explore tool calling, function calling, memory systems, planning agents, ReAct, LangGraph, CrewAI, and multi-agent collaboration.
The program prepares students to work on different types of AI applications, including AI research assistants, AI tutors, recommendation systems, enterprise search platforms, knowledge-base assistants, career coaches, study coaches, productivity assistants, and multi-agent business systems.
Yes. Students who successfully complete the course are eligible to receive a certificate.
Students leave the program with more than familiarity with AI tools.
They develop practical experience building:
Practical AI Systems
Most importantly, they learn to move from:
It is a journey into understanding, building and engineering intelligent systems.
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This is not just an AI course. It is a complete journey from Machine Learning to Large Language Models, Retrieval-Augmented Generation, and Agentic AI.
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