From cats on the internet to cancer detection in hospitals — how does one AI model jump across worlds?
That’s the magic of transfer learning. In this first post of my new AI series, I break it down with simple analogies and real-world examples anyone can understand.
Transfer Learning – Beginners Guide Read More »
🌱 Can AI Really Spot Plant Diseases?
Yes! In my latest beginner-friendly blog, I share how I built an AI model that detects plant leaf diseases using deep learning. From a free dataset to training a modern model, you’ll see how simple (and exciting!) it is to apply AI in agriculture.
👉 Read the full story and try it yourself!
Detecting Plant Diseases with AI – A Beginner-Friendly Deep Learning Project Read More »
Ever wondered how AI can spot skin cancer from images? My easy guide walks you through HAM10000 with a Colab + GitHub project. Click to explore!
How I built a beginner-friendly skin-cancer detector Read More »
Explore how machine learning and satellite imagery can reveal how cities grow, how nightlights change, and how vegetation evolves. Three notebooks (India nightlights, Delhi urban map, Singapore NDVI) walk beginners step-by-step from visualization to a supervised ML example.
Visualising Change from Space — A Beginner’s Guide to ML with Satellite Data – Part 1 Read More »
In this post, you’ll learn how to build your own AI SQL Agent — a tool that transforms natural language into SQL queries and executes them on a real database.
In real-world teams, everyone — from marketers to HR to analysts — wants to “talk to the database” without needing to know SQL. But data lives behind structured queries. That’s where LLMs like GPT-4 shine: they understand natural language and, with the right schema and safety controls, can generate SQL queries that pull live data.
No prior coding experience required. Just download the code, run it, enhance it — and start learning!
Agentic AI – LLM with SQL – Beginner Bootcamp Read More »
In this post, you’ll learn how to build your own document-reading AI assistant — an intelligent tool that can read and understand documents like PDFs, Word files, Excel sheets, HTML pages, JSON files, and more using OpenAI’s powerful language models combined with a cutting-edge technique called RAG (Retrieval-Augmented Generation).
No prior coding experience required. Just download the code, run it, enhance it, and start learning!
Agentic AI – LLM with RAG – Beginner Bootcamp Read More »
In this beginner-friendly bootcamp, learn how to create a smart web agent that scrapes websites, processes content with GPT-4, and answers user questions intelligently. You’ll start with basic LLM-based querying and then upgrade to a scalable Retrieval-Augmented Generation (RAG) system using vector databases like FAISS. Perfect for Python and AI learners!
Agentic AI – LLM with Web Scraping – Beginner Bootcamp Read More »
🧠 Build Your First Agentic AI!
Curious how ChatGPT can actually call functions and fetch live data?
In our latest Beginner Bootcamp, learn how to create a smart Weather Agent using OpenAI’s GPT-4 and WeatherAPI — no advanced coding required!
Agentic AI – LLM with API Calls – Beginner Bootcamp Read More »
🚀 Build Your First Agentic AI App (With Code Example!)
In this beginner-friendly bootcamp, you’ll learn how to build a multi-agent AI application from scratch. We’ll walk you through the core architecture of agentic systems and show you how to bring it to life with a real-world project — an AI-powered Workshop Planner and Meeting Assistant.
You’ll get:
✅ Step-by-step tutorial
💡 Ready-to-run Python code
🔧 Hands-on experience with LangChain and LangGraph
Just download the notebook, run it in your Python environment (like Google Colab), and start building. Tweak it, expand it, and you’re on your way to creating your own AI agents!
Agentic AI for Beginners – Learn by Building Your First App Read More »
Data quality is mission-critical in banking, as poor data can erode trust and even impact revenue (businesses reported an average 31% revenue loss due to bad data in 2023). Banks handle diverse data (customer info, transactions, risk metrics, etc.), and regulators demand ( BCBS239, GDPR etc) that this data be accurate, complete, timely, and well-governed.
Generative AI (GenAI) offers new ways to automate and enhance data quality management across these phases. Modern AI can summarize and generate documents, extract and classify information, and even assist in detecting data issues, thereby accelerating data governance and compliance tasks. Below, we break down the key DQMF phases – from data creation, storage, processing, usage, to archival and deletion – highlighting critical activities and how GenAI can realistically improve or streamline outcomes in each. We then present a structured table summarizing GenAI applications for each phase, implementation steps, and example prompt templates.
End-to-End Data Quality Management Framework (DQMF) in Banking with GenAI Integration Read More »
