Discover Insights. Build Intelligent AI Systems.
Our Data Science with Gen AI & Agentic AI Course takes you from Python and statistical analysis to machine learning, generative AI, retrieval-augmented generation and autonomous AI agents through practical projects.
Why Technogeeks X AI
Learn data and AI by combining analytical thinking, model building and production-oriented projects.
Complete Data Science Path
Learn Python, statistics, data preparation, visualisation, machine learning and model evaluation.
Generative AI Skills
Build LLM-powered applications with prompting, embeddings, vector search and RAG pipelines.
Agentic AI Development
Design agents that use tools, memory, planning and workflows to complete multi-step tasks.
Experienced AI Mentors
Learn from practitioners who work with analytics, machine learning and modern AI systems.
Placement & Career Support
Build a project portfolio with resume support, mock interviews and role-focused preparation.
Proven Track Record
5000+ learners trained across our data, AI and automation programs.
Real skills, not just certificates
By the end of the program, you will be able to analyse data, train models and build intelligent AI applications.
Analyse
Use Python, SQL, statistics and visualisation to extract meaningful insights from data.
Predict
Build, evaluate and improve machine learning models for classification, regression and clustering.
Generate
Create LLM applications using prompts, embeddings, RAG, vector databases and evaluation techniques.
Orchestrate
Build autonomous agents that use tools, memory, planning and multi-step workflow execution.
Everything an AI Data Professional needs
Build one connected toolkit spanning data analysis, machine learning, generative AI and autonomous systems.
+ AI
- Python
- Pandas
- Statistics
- Machine Learning
- SQL
- LLMs
- RAG
- Embeddings
- AI Agents
- LangGraph
From raw data to autonomous intelligence
Follow a complete workflow from data collection and modelling to GenAI applications and agentic automation.
-
01
Collect
SQL · APIs · Files
-
02
Prepare
Clean · Transform · Explore
-
03
Model
ML · Features · Evaluation
-
04
Augment
LLMs · Embeddings · RAG
-
05
Orchestrate
Agents · Tools · Memory
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06
Deploy
APIs · Monitoring · Iterate
Data and AI projects you'll actually build
Build portfolio-ready projects that demonstrate analytical reasoning, model development and intelligent automation.
Customer Churn Prediction
Problem — A business needed to identify customers at risk of leaving and prioritise retention efforts.
Stack — Python, Pandas, feature engineering, classification models and model evaluation.
Outcome — A predictive workflow that scores customer risk and explains important factors.
Enterprise Knowledge Assistant
Problem — Teams needed accurate answers from internal documents and company knowledge.
Stack — LLMs, document loaders, embeddings, vector database and RAG pipeline.
Outcome — A grounded question-answering assistant with source-aware responses.
Multi-Agent Research System
Problem — A research team needed automated information gathering, analysis and report generation.
Stack — LangChain, LangGraph, tools, web search, memory and structured outputs.
Outcome — A multi-agent workflow that researches topics, validates findings and creates reports.
Six phases, one connected program
Progress from data science foundations to production-ready GenAI and agentic AI applications.
Python programming, NumPy, Pandas, statistics, probability, data cleaning, exploratory analysis and visualisation using Matplotlib and Seaborn.
SQL queries, joins, aggregations, feature engineering, supervised learning, unsupervised learning, model evaluation and scikit-learn workflows.
LLM concepts, prompt engineering, structured outputs, function calling, embeddings, vector databases and responsible AI application design.
Document ingestion, chunking, retrieval, reranking, grounding, evaluation and production-oriented retrieval-augmented generation pipelines.
Agent design, tools, memory, planning, task delegation, LangChain, LangGraph and multi-agent workflow orchestration.
Model and application deployment, APIs, observability, evaluation, cost optimisation, safety and an end-to-end Data + GenAI + Agentic AI capstone.
Get an Industry-Recognized Certificate
A certificate that reflects practical data science, GenAI and agentic AI development skills.
- Project-based Awarded after completing data science, GenAI and agentic AI projects.
- Shareable Add the certificate directly to your LinkedIn profile and resume.
- Verifiable Every certificate carries a unique ID employers can verify.
- Industry-aligned Mapped to analytics, machine learning, LLM and AI engineering skills.
Learn by building intelligent systems
Every module combines concepts with hands-on notebooks, models, LLM applications and agent workflows.
- Explore DataClean, analyse and visualise datasets with Python
- Train ModelsBuild and evaluate machine learning pipelines
- Augment KnowledgeConnect LLMs to documents and vector search
- Design AgentsGive agents tools, memory and planning capabilities
- Deploy SystemsExpose AI workflows through APIs and production interfaces
Don't just analyse data. Learn to build intelligence.
Modern data science, generative AI and agentic workflows are taught as one connected engineering path.
Where this course takes you
Learn
Master Python, statistics, machine learning, LLMs, RAG and agentic AI foundations.
Build
Create predictive models, knowledge assistants and multi-agent AI workflows.
Portfolio
Publish end-to-end projects that demonstrate data, GenAI and agent engineering skills.
Interview
Prepare for Python, ML, data science, LLM and AI system design interviews.
Apply
Apply for AI and data roles with mentor guidance, resume support and profile improvement.
Support that goes beyond the syllabus
Resume / CV
Build an AI-focused resume highlighting data, machine learning and GenAI projects.
Mock Interviews
Practice Python, statistics, ML, LLM, RAG and AI system design questions.
Interview Prep
Prepare for model-building tasks, data cases, prompt challenges and agent design discussions.
Mentorship
Get guidance on project architecture, model quality, AI evaluation and career progression.
Job Assistance
Access relevant openings for data science, ML, GenAI and AI engineering roles.
Profile Guidance
Review your GitHub, LinkedIn and AI portfolio before applying for roles.
Alumni Working Across the Industry
A growing network of professionals working across data, machine learning, GenAI and software teams.
Course Snapshot
Data Science with GenAI & Agentic AI
Data Science with Gen AI & Agentic AI Course
₹70,000 / full program
No-cost EMI options available
- + Live instructor-led training
- + Python, statistics, SQL and machine learning
- + Generative AI and LLM application development
- + RAG, embeddings and vector databases
- + AI agents, tools, memory and orchestration
- + Real-world data and AI projects
- + Resume, mock interviews and job assistance
- + Certificate of completion
Frequently asked questions
The course covers Python, statistics, SQL, data analysis, machine learning, generative AI, LLMs, RAG, embeddings, AI agents and deployment fundamentals.
Students, developers, analysts, software professionals and aspiring data scientists or AI engineers can join the program.
Basic programming knowledge is helpful, but the program includes the Python fundamentals needed for data science and AI development.
Yes. You will learn supervised and unsupervised learning, feature engineering, model evaluation and practical scikit-learn workflows.
The module covers LLM concepts, prompt engineering, structured outputs, embeddings, vector search, RAG and AI application design.
Yes. You will learn agent design, tool use, memory, planning, workflow orchestration and multi-agent patterns using modern frameworks.
Yes. Projects include predictive modelling, enterprise knowledge assistants, RAG applications and multi-agent research systems.
The course introduces framework-based LLM and agent workflows, including chains, tools, state, memory and graph-based orchestration.
Yes, a certificate of completion is provided after completing the required training and projects.
Support includes resume building, mock interviews, project reviews, mentorship and job assistance for data and AI roles.