Most up-to-date AI & Gen AI coaching for senior IT professionals
AI + Gen AI for Senior IT professionals
Why should I take this course?
Not another copy-paste AI course
Most up-to-date
Industry-relevant
No Maths & Stats
Short
Who will benefit most from this course?
Who want to learn AI + LLM quickly and easily.
Course Curriculum
Course 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
Course Duration
Total (Classroom coaching):
- Theory – 24 hours
- Hands-on – 72 hours
- It’s a short program but a rigorous one
FAQs
Course Mode
Instructor-led Interactive Online Classes.
Pre-requisite
Python (the ability to read code in Python)
(Most of the code in the Lab and Practical applications are already written)
Who is the teacher?
Nikhilesh Tayal – https://www.aimletc.com/about-me-nikhilesh-tayal/
LinkedIn – https://www.linkedin.com/in/nikhileshtayal/