AI courses and coaching exclusively for senior IT professionals, led by a Google Developer Expert (AI)
AI + Gen AI + AI Agents for Senior IT professionals
Why should I opt for this program?
Designed from first principles, not recycled slides
Continuously Updated
Production-focussed
No Maths & Stats
Short
Who will benefit most from this course?
For professionals who need to understand, evaluate, and lead AI initiatives
Program Curriculum
Program Overview – Labs and Practical Applications
Hands-On Labs
- Pandas Lab
- Machine Learning Lab
- Deep Learning Lab
- Computer Vision Lab
- NLP Lab
- Building LLM Applications Lab
- Promot Engineering for Developers Lab
- LangChain Lab
- Function calling Lab
- Monitoring Langchain application with Langsmith
- RAG Lab
- FineTune Gemma model in Keras using LoRA
- Deploying LLMs using DeepInfra, Replicate, RunPod
- LM Scorer
- AI Nutrionist using Gemini Pro with Streamlit
- LangServe
- LangGraph
- DSPy Demo
- Small Language Modela
- Quantize any LLM
- MemoRAG
- Prompt Caching
- Pre-processing videos for
Multimodal RAG
Practical AI Tools/ Applications that you will build
- ML Classification model
- Image Segmentation
- Text-to-Voice
- AI Video Generation
- Image Generation using Open Source models on Huggingface
- Calling any Open-Source LLMs
- Calling OpenAI models using its API
- LLM Orderbot to take orders on your website
- Multilingual Text Summarization Tool
- YouTube Video Summarization Tool
- Chat with your Own PDF
- LLM Chatbot for your proprietary data using the Google Cloud Platform
- OpenAI + Google Search
- AI Medical Agent with conversational memory
- Multi AI Agent using open-sourced LLM
- Multi AI Agent for customer support automation
- Gradio application
- Building Generative AI applications using Gemini and Streamlit
- Evaluating LLMs using Giskard
- DSPy framework Demo
- AI Voice Assistant App using Multimodal LLM “Llava” and Whisper
- Multi AI Agent System to Tailor Job Applications
- LangGraph -Multi AI Agent System for coding
- Delhi vs Mumbai – Multi AI Agent Debate using AutoGen.
- Chat with multiple PDFs
- Multi AI Agent System for Project Planning for an Agency
- Two AI Agents doing Standup comedy
- RAG on Excel Sheet
- Multimodality – Extracting information from invoice
- AI Agent with self managing memory
- AI Customer Support Agent for Ecommerce business
Hands-On Labs
- Pandas Lab
- Machine Learning Lab
- Deep Learning Lab
- Computer Vision Lab
- NLP Lab
- Building LLM Applications Lab
- Promot Engineering for Developers Lab
- LangChain Lab
- Function calling Lab
- Monitoring Langchain application with Langsmith
- RAG Lab
- FineTune Gemma model in Keras using LoRA
- Deploying LLMs using DeepInfra, Replicate, RunPod
- LM Scorer
- AI Nutrionist using Gemini Pro with Streamlit
- LangServe
- LangGraph
- DSPy Demo
- Small Language Modela
- Quantize any LLM
- MemoRAG
- Prompt Caching
- Pre-processing videos for Multimodal RAG
Program Duration and Commitment
- Live Instruction – 24 hours
- Hands-on labs and projects – 70+ hours
- It’s a compact but intensive program
FAQs
Course Mode
Instructor-led Interactive Online Classes.
Pre-requisite
The prerequisite for the program is Python.
You should be comfortable reading and understanding Python code.
(Most of the code in the Lab and Practical applications are already written)
Who is the teacher?
Nikhilesh Tayal leads the program.
He is a Google Developer Expert (AI/ML), an IIT Kharagpur alumnus, a LinkedIn Learning instructor, and the author of AI Agents Without Jargon.
His work sits at the intersection of AI systems, engineering, and leadership, focused on how AI actually behaves in real-world production environments.
Learn more:
About the instructor: https://www.aimletc.com/about-me-nikhilesh-tayal/
LinkedIn: https://www.linkedin.com/in/nikhileshtayal/