01
Students After 12th
Students from any stream can begin with Python and gradually progress into AI and machine learning concepts.
Start right after school
Best After 12th 6-Month Artificial Intelligence Program in Mohali
Turn your interest in AI into practical development skills with a six-month learning path covering Python, machine learning, deep learning, NLP, LLMs, prompt engineering, RAG, AI agents, multimodal applications and cloud deployment.
Designed for students after 12th and aspiring AI developers, this programme combines technical foundations with hands-on projects so you can move from writing your first Python programs to developing and deploying AI-powered applications.
0113
01Where this starts
Course Overview
This programme follows a structured progression from programming fundamentals to modern AI engineering.
Instead of treating artificial intelligence as a single topic, the curriculum separates the major skills required to create useful AI applications.
Month 1 — Python & AI/ML Foundations. The first month establishes your programming and data foundations.
You will learn Python from the beginning, work with VS Code, create virtual environments and use Git and GitHub for version control. Development practices such as object-oriented programming, exception handling, logging, type hints and automated testing are introduced alongside APIs and JSON.
The AI foundation includes NumPy, Pandas, statistics, probability, linear algebra and machine learning using scikit-learn. You will also understand model training, evaluation and cross-validation.
Month 2 — Deep Learning & NLP. Once the fundamentals are established, the programme moves into neural networks and deep learning.
You will work with PyTorch and learn tensor operations, neural networks, CNNs, transfer learning and computer-vision workflows using OpenCV.
The NLP section introduces text processing, embeddings, sequence models and language understanding before moving into Transformers and the Hugging Face ecosystem.
Month 3 — LLM Fundamentals & Prompt Engineering. The third month focuses on large language models.
You will learn how tokenization, embeddings, context windows and attention contribute to the behaviour of LLM-based applications.
Prompt engineering covers system prompts, structured prompting and prompt optimisation. You will work with multiple AI providers including OpenAI, Gemini, Claude and Grok, while Ollama introduces local-model workflows and LiteLLM provides an approach for working across different providers.
Month 4 — RAG & AI Agents. This module moves from using AI models to building applications around them.
You will learn embeddings, semantic search and vector databases such as FAISS, ChromaDB, Pinecone and Qdrant.
RAG architecture is introduced with hybrid search, re-ranking, evaluation and guardrails. The agent-development section covers LangChain, LangGraph, CrewAI, MCP, tool calling, function calling, structured outputs, memory and multi-agent workflows.
Month 5 — AI Application Development. The fifth month concentrates on turning AI components into usable applications.
You will work with advanced FastAPI, asynchronous programming, background tasks and WebSockets. Application interfaces can be developed using Streamlit, Gradio and Chainlit.
You will also explore conversational AI, chatbot architecture, dialogue management and multimodal applications involving text, images, audio and video.
Whisper, vision-language models and speech-AI concepts are included to demonstrate how AI applications can work beyond text.
Month 6 — Deployment, Security & Capstone. The final month connects development with deployment.
You will learn Docker, Docker Compose, Linux and Nginx before exploring deployment concepts across AWS, Azure AI and Google Vertex AI.
AI-security topics include prompt injection, jailbreak defence, secret management and responsible AI.
The programme concludes with an end-to-end capstone combining technologies such as LLMs, RAG, agents, APIs, Docker and cloud deployment.
Where it takes you
- AI Engineer
- Machine Learning Engineer
- Deep Learning Engineer
- LLM Application Developer
- AI Agent Developer
- AI Application Developer
- Generative AI Developer
- Python AI Developer
02Skills you collect
What You'll Learn
Seven outcomes across the six months — from a first Python program through to a deployed, documented AI capstone.
Build AI Models With Python & PyTorch
Start with Python and progress towards machine-learning and deep-learning workflows using scikit-learn and PyTorch.
Understand NLP & Transformers
Learn how computers process language and how Transformer-based architectures support modern language applications.
Work With Large Language Models
Understand tokens, embeddings, context windows and attention while experimenting with multiple model providers.
Create RAG Applications
Connect language models with external knowledge using embeddings, vector databases, retrieval and evaluation techniques.
Develop AI Agents
Build systems capable of using tools and following multi-step workflows with LangChain, LangGraph, CrewAI and related technologies.
Deploy AI Applications
Move beyond notebooks and prototypes by learning APIs, Docker, cloud deployment, application interfaces and basic AI security.
Complete an AI Capstone
Combine the skills from the programme into a complete AI application that can be documented and presented as part of your portfolio.
03The route
Course Curriculum
The six-month curriculum moves through programming, machine learning, deep learning, LLMs, RAG, agents, application development and deployment.
- Python & Development
- Python fundamentals
- VS Code
- uv package manager
- Virtual environments
- Git
- GitHub
- Object-oriented programming
- Exception handling
- Logging
- Type hints
- pytest
- APIs
- JSON
- Data & Mathematics
- NumPy
- Pandas
- Statistics
- Probability
- Linear algebra fundamentals
- Machine Learning
- scikit-learn
- Model training
- Model evaluation
- Cross-validation
- Basic machine-learning workflows
- AI Development Tools
- ChatGPT
- GitHub Copilot
- AI-assisted coding
- Development workflows
Mini Projects, Then a Capstone
The programme runs mini projects alongside the syllabus and closes on a final capstone — a machine-learning application in month one, computer vision in month two, an LLM assistant in month three, a RAG knowledge assistant in month four and an agent workflow in month five.
Outcome
The capstone brings the six months together in one application — an API backend, AI model integration, RAG or agents, Docker and cloud deployment, documented as portfolio work.
04Your toolkit
Technologies You Can Explore During the Programme
The toolchain spans programming, data and machine learning, deep learning, generative AI, retrieval, agents, application development and deployment.
PythonVS CodeuvGitGitHubFastAPINumPyPandasscikit-learnPyTorchOpenCVHugging FaceTransformersTokenizersOpenAIGeminiClaudeGrokOllamaPythonVS CodeuvGitGitHubFastAPINumPyPandasscikit-learnPyTorchOpenCVHugging FaceTransformersTokenizersOpenAIGeminiClaudeGrokOllamaPythonVS CodeuvGitGitHubFastAPINumPyPandasscikit-learnPyTorchOpenCVHugging FaceTransformersTokenizersOpenAIGeminiClaudeGrokOllama
LiteLLMFAISSChromaDBPineconeQdrantLangChainLangGraphCrewAIMCPStreamlitGradioChainlitDockerDocker ComposeNginxAWSAzure AIGoogle Vertex AILiteLLMFAISSChromaDBPineconeQdrantLangChainLangGraphCrewAIMCPStreamlitGradioChainlitDockerDocker ComposeNginxAWSAzure AIGoogle Vertex AILiteLLMFAISSChromaDBPineconeQdrantLangChainLangGraphCrewAIMCPStreamlitGradioChainlitDockerDocker ComposeNginxAWSAzure AIGoogle Vertex AI
- Python
- Programming & development — the language the whole course runs on.
- VS Code
- Programming & development — the editor and project workspace.
- uv
- Programming & development — packages and virtual environments.
- Git
- Programming & development — version control and branches.
- GitHub
- Programming & development — where the portfolio lives.
- FastAPI
- Programming & development — the API layer, basic through advanced.
- NumPy
- Data & machine learning — arrays and numerical operations.
- Pandas
- Data & machine learning — loading, transforming and analysing data.
- scikit-learn
- Data & machine learning — training, evaluation and cross-validation.
- PyTorch
- Deep learning — tensors, neural networks, CNNs and transfer learning.
- OpenCV
- Deep learning — image processing and vision workflows.
- Hugging Face
- Deep learning — the Model Hub and ecosystem.
- Transformers
- Deep learning — the architecture behind modern language models.
- Tokenizers
- Deep learning — how text becomes tokens a model can read.
- OpenAI
- Generative AI — the first LLM API you call.
- Gemini
- Generative AI — a second provider to compare against.
- Claude
- Generative AI — long-context reasoning work.
- Grok
- Generative AI — a further model API.
- Ollama
- Generative AI — running models locally.
- LiteLLM
- Generative AI — one interface across providers.
- FAISS
- RAG & vector search — local similarity search.
- ChromaDB
- RAG & vector search — an embedded vector store.
- Pinecone
- RAG & vector search — the hosted vector database.
- Qdrant
- RAG & vector search — filtering alongside vector search.
- LangChain
- AI agents — chains, prompt templates and memory.
- LangGraph
- AI agents — stateful graph workflows.
- CrewAI
- AI agents — multi-agent workflows.
- MCP
- AI agents — Model Context Protocol for tools and resources.
- Streamlit
- Application development — the fastest route to an interface.
- Gradio
- Application development — demo interfaces for models.
- Chainlit
- Application development — chat interfaces for AI apps.
- Docker
- Deployment — containerising the application.
- Docker Compose
- Deployment — running the whole stack together.
- Nginx
- Deployment — reverse proxy in front of the app.
- AWS
- Deployment — cloud hosting and services.
- Azure AI
- Deployment — Microsoft's AI platform.
- Google Vertex AI
- Deployment — Google's managed AI platform.
05Is this you?
Who Can Join This Course?
02
College Students
BCA, B.Sc, B.Tech, BBA and other students can use the programme to supplement their academic learning with practical AI development.
03
Graduates
Graduates looking to build technical skills in artificial intelligence can use the structured six-month curriculum to develop a portfolio.
04
Aspiring AI Developers
If your goal is to create AI-powered applications rather than only learn AI theory, the programme covers APIs, RAG, agents, backends and deployment.
05
Python Beginners
You do not need to be an advanced Python developer. The first module establishes the programming foundation required for later AI topics.
06
Developers & Technical Learners
Learners who already understand programming can focus more heavily on the LLM, RAG, agent and deployment sections.
06Why this one
Why Choose a Six-Month AI Programme?
01
Learn the Full AI Application Journey
Understand how an AI application moves from data and models to retrieval, agents, backend APIs and deployment.
02
Build Strong Programming Foundations
Python, Git, testing, APIs and software-development practices give you a foundation for more advanced AI work.
03
Explore Modern Generative AI
Learn how LLMs, embeddings, RAG and AI agents are used to create applications around foundation models.
04
Go Beyond Chatbots
The curriculum includes machine learning, computer vision, NLP, multimodal AI, backend development and deployment.
05
Work With Multiple AI Models
Exposure to different providers and local models helps you understand that AI development is not limited to a single platform.
06
Build Portfolio Projects
Practical assignments and the final capstone give you work that can be documented and discussed during interviews.
07Why now
From Python to Production AI
AI development increasingly involves more than training a model.
Learning these layers together can help you understand how modern AI products are assembled.
The programme therefore focuses not only on AI concepts but also on the engineering skills needed to connect models with real applications.
A useful AI application may require
- PythonMonth 1 — fundamentals, OOP, testing, logging and type hints.
- DataMonth 1 — NumPy, Pandas, statistics, probability and linear algebra.
- Machine LearningMonth 1 — scikit-learn, training, evaluation and cross-validation.
- Deep LearningMonth 2 — PyTorch, neural networks, CNNs and transfer learning.
- LLMsMonth 3 — tokenization, context windows, attention and model APIs.
- RAGMonth 4 — embeddings, vector search, hybrid retrieval and guardrails.
- AgentsMonth 4 — LangChain, LangGraph, CrewAI, MCP and tool calling.
- APIsMonth 5 — advanced FastAPI, async, background tasks and WebSockets.
- InterfacesMonth 5 — Streamlit, Gradio, Chainlit and conversational design.
- DockerMonth 6 — containerising the application with Compose and Nginx.
- CloudMonth 6 — AWS, Azure AI, Google Vertex AI and serverless concepts.
- SecurityMonth 6 — prompt injection, jailbreak defence and responsible AI.
Talk to a Course Advisor
Ten minutes with the techcadd team settles eligibility, session timings, fees and where this leads — before you commit six months to it.
08Certification
Build Your Skills and Document Your Work
Learners who complete the applicable programme requirements can receive the relevant course-completion documentation provided by techcadd. Certificate and internship details should be confirmed with the Mohali centre before enrolment.
Course Completion DocumentationDocumentation of successful completion of the applicable training programme.
Capstone Project DocumentationA written record of the architecture, implementation and results of your final AI application.
GitHub Portfolio DevelopmentYour repositories organised and presented as portfolio work.
Career PreparationResume preparation, LinkedIn profile optimisation, technical interview preparation, mock interviews, career guidance and placement assistance, subject to current techcadd terms.
Capstone Defense
Fullscreen
Project Completion Certificate
ISO 9001:2015 Accredited
Fullscreen
Course Completion Certificate
Two documents on completion — the course certificate, and a separate certificate for the capstone you build and defend. Click any card to inspect in full screen.
09Where it leads
Where This Course Can Take You
The skills covered in the programme can help you explore roles and learning paths across AI, machine learning and application development.
Roles this programme prepares you for
Develop applications that integrate machine-learning or generative-AI technologies.
10What you build
Hands-On Projects
01
Machine Learning Application
Build a machine-learning application using Python, Pandas and scikit-learn with data preparation, training and model evaluation. Skills: Python · Pandas · scikit-learn
02
Computer Vision Application
Create a basic computer-vision solution using PyTorch and OpenCV. Skills: PyTorch · OpenCV · Deep Learning
03
LLM-Powered Assistant
Develop an application using an LLM API with structured prompts and application logic. Skills: LLM APIs · Prompt Engineering · Python
04
RAG Knowledge Assistant
Create a retrieval-augmented application that uses embeddings and a vector database to retrieve relevant information before generating responses. Skills: Embeddings · Vector Search · RAG · LangChain
05
AI Agent Workflow
Develop an AI agent capable of interacting with selected tools and completing a multi-step task. Skills: LangGraph · CrewAI · Tool Calling · AI Agents
06
AI Capstone Application
Build a complete AI application integrating several technologies from the programme. Possible capstone ideas include an AI customer-support assistant, RAG-based knowledge platform, AI document assistant, AI research assistant, AI content workflow, AI analytics assistant, multi-agent business workflow or multimodal AI application. The project can include an API backend, AI model integration, RAG or agents, Docker and cloud deployment.
11How it works
Learn. Build. Deploy.
Every project you take on runs through the same five stages:
Learn
Understand the concept, technology and problem you are trying to solve.
Machine Learning ApplicationBuild
Implement the idea through guided coding, experiments and practical assignments.
Computer Vision ApplicationImprove
Test the application, evaluate its output and refine the implementation.
LLM-Powered AssistantDeploy
Package the application and explore deployment using Docker and cloud platforms.
RAG Knowledge AssistantPresent
Document your project and explain the technical decisions behind it.
AI Agent Workflow12Why techcadd
Why Choose techcadd for AI Training in Mohali?
A curriculum that builds in order, a project at every stage, and a stack that runs from a first Python program to a deployed AI application.
01
Progressive Curriculum
The course begins with programming and AI fundamentals before introducing increasingly advanced technologies.
02
Practical AI Development
Instead of focusing exclusively on definitions, learners work towards applications involving models, APIs, retrieval and agents.
03
Modern AI Stack
The curriculum includes technologies from Python and PyTorch to LLM APIs, RAG frameworks, AI agents and cloud deployment.
04
Portfolio-Oriented Learning
Projects provide opportunities to demonstrate your practical skills through GitHub repositories and project documentation.
05
Beginner-Friendly Foundation
The programme starts from Python fundamentals, making the learning path more accessible to students after 12th.
06
Career Preparation
Resume building, GitHub presentation, LinkedIn optimisation and mock interviews can help prepare learners for AI-focused opportunities.
13Who has walked it
Students who started where you are
Alumni of this route, on what changed once they were sitting in interviews.
4.7
444 reviews
582%
411%
34%
22%
11%
Running real ad budgets during the course was the difference. I walked into my first job already knowing how to read a campaign report and fix what was underperforming.
The cyber security lab setup let me break things safely and learn how attacks really work. Placement cell arranged three interviews within a month of finishing.
AutoCAD and SolidWorks were taught with actual production drawings, not textbook exercises. My employer noticed that my drawing sets followed proper standards from day one.
I joined the 6-month MERN track straight after B.Tech with almost no practical experience. The live project work is what changed things — I had real code to talk about in interviews instead of just a syllabus.
14Before you go
Frequently Asked Questions
Still unsure? A ten-minute call with a counsellor usually settles it faster than any brochure.
The programme is structured as a six-month Artificial Intelligence course, covering Python, machine learning, deep learning, NLP, LLMs, RAG, agents, AI application development and deployment.
15Final stage
Ask About Artificial Intelligence Certificate Program
Thinking about learning AI after 12th?
Speak with a course counsellor about the current curriculum, practical projects, session timings, fees, certification and career-support options available in Mohali.
Location: Mohali, Punjab. Counselling hours: Monday to Saturday, 9:00 AM to 7:00 PM.
+91 98881 22442Call the admissions desk+91 98881 22442WhatsApp for a quick replyinfo@techcadd.comEmail us your questions
Your details are used only to contact you about this enquiry — never sold, never added to a marketing list.
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Still deciding
Ready to Start Your AI Learning Journey?
You do not need to master programming before exploring artificial intelligence.
Start with Python, understand how machine learning works, build with LLMs, experiment with RAG and agents, and finish with an AI application that demonstrates your skills.
Get Started Today
- 6 months, 12th pass, any stream
- Python taught from the fundamentals — no prior coding needed
- scikit-learn, PyTorch, CNNs, computer vision and NLP
- LLM APIs across OpenAI, Gemini, Claude, Grok and Ollama
- RAG, vector databases, LangGraph, CrewAI and MCP agents
- FastAPI, Streamlit, Docker, AWS, Azure AI and Vertex AI
- 6 projects closing on a deployed AI capstone