Generative AI Training Program - Live
90k+ interested Geeks
Become an industry-ready Gen AI Developer from the ground up.
- Master the end-to-end lifecycle: design, build, and deploy advanced solutions.
- Advance from Python and Deep Learning to context-aware apps with LangChain and RAG.
- Customize local LLMs and build autonomous workflows with Crew AI and n8n.
- Evaluate and deploy your AI systems to the cloud using Docker and FastAPI.
Beginner to Advanced16 Weeks4 Seats Left
Still Confused, Get free Counselling to upskill your carrer, Call us at +91- 9217990450
Course Overview
Foundational Skills
Build a comprehensive foundation in Python, Object-Oriented Programming (OOP), and Data Science.
Build a comprehensive foundation in Python, Object-Oriented Programming (OOP), and Data Science.
Deep Learning Mastery
Master Feedforward Networks, CNNs, RNNs, Backpropagation, and Transformer architectures.
Master Feedforward Networks, CNNs, RNNs, Backpropagation, and Transformer architectures.
Practical AI Workflows
Build Gen AI applications for NLP and Computer Vision using Hugging Face and LangChain.
Build Gen AI applications for NLP and Computer Vision using Hugging Face and LangChain.
AI System Design
Work with Embeddings, Vector Databases, and advanced RAG pipelines using enterprise AI workflows.
Work with Embeddings, Vector Databases, and advanced RAG pipelines using enterprise AI workflows.
LLM Customization & Agents
Fine-tune Large Language Models using QLoRA & PEFT and create sophisticated multi-agent systems.
Fine-tune Large Language Models using QLoRA & PEFT and create sophisticated multi-agent systems.
Responsible AI
Implement guardrails, bias checks, and evaluation metrics for secure and reliable AI systems.
Implement guardrails, bias checks, and evaluation metrics for secure and reliable AI systems.
End-to-End Deployment
Build and deploy AI solutions using FastAPI, Docker, Streamlit, Gradio, and local LLM workflows.
Build and deploy AI solutions using FastAPI, Docker, Streamlit, Gradio, and local LLM workflows.
Skills You Will Learn
Deep Learning & Transformers
Prompt Engineering
RAG Systems
LLM Fine-tuning
AI Agents
Model Context Protocols (MCP)
Model Evaluation
MLOps & Deployment
Tech Stack You Will Learn
Python
Keras & PyTorch
Hugging Face
LangChain
Ollama
Crew AI
n8n
FastAPI
Gradio & Chainlit
Docker
DeepEval
LangSmith
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What Sets Us Apart
24 X 7 AI Doubt Support
AI Chat Support for instant doubt resolution, plus a dedicated Teaching Assistant exclusively assigned to your batch.
Hands-On Project Work
We offer industry-grade projects that strengthen your portfolio and make your resume stand out.
IBM & GeeksforGeeks Certification
Earn dual certification from IBM and GeeksforGeeks to validate your skills and boost career growth.
Modern AI Toolkit
Takes techies from AI tools to AI-native systems, covering prompts, RAG, agents, MCPs, and full-stack AI.
Course Syllabus
Generative AI Training Program - Live
AI Toolkit for Developers
Week 1: Python Basics for Data Science
Day 1: Python Basics for Data Science
- Introduction to Google Colab
- Data Types and Typecasting
- Conditionals, and Loops
Day 2: Python Basics for Data Science
- List Comprehensions
- Functions, and Lambda Functions
- Randomization
- Inputs in Python
Week 2: Feedforward Neural Networks
Day 1: Feedforward Neural Networks
- Understanding the AI Glossary
- Perceptron, and Activation Functions
- Layers and MLPs
- Understanding the Training Loop
Day 2: Backpropagation
- Backpropagation by Hand
- Loss and Error Metrics
- Project: MNIST Handwritten Digits Classification
Week 3: More Neural Networks
Day 1: Convolutional Neural Networks
- What is an image?
- Understanding kernels and convolutions
- Convolution Layers, Pooling Layers
- Convolutional Neural Networks
- Project: Cats vs Dogs Classification
Day 2: Understanding the Intuition of Recurrent Neural Networks
- What is a Sequence?
- What is a Seq2Seq Model?
- The RNN Cell
- The LSTM Cell
- Long-term Dependencies: Why RNNs are no longer relevant
Week 4: Transformers - Conceptuals
Day 1: Transformers - Conceptual Knowledge
- Seq2Seq Models with RNN
- What is Attention?
- A High-Level Look at Transformers
- Encoders
- Self-Attention
Day 2: Transformers - Conceptual Knowledge
- How are Attention Scores calculated?
- Multi-Headed Attention
- Positional Encoding
- Residuals & Layer Normalization
- Decoders
- Linear and Softmax Layer
- Revisiting the High-Level Look
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Upcoming Batches
Batch
Mentor
STARTING FROM
TIMINGS
Frequently Asked Questions
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How will my doubts be addressed throughout the course?
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Is there any number to contact for query?
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Is there a refund option if I am not satisfied with the course?
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