AI and machine learning,
explained so you can actually build things.
Deep-dive articles, tutorials and topic guides spanning classic ML, generative AI, LLMs, RAG, agents and cloud AI — written for engineers, data professionals, architects and product teams who want to ship, not just read.
Fresh from the blog
Building a Diet AI Assistance App having Converstational feature build using Amazon Bedrock, RAG Knowledge Bases, Cognito, API Gateway, Lambda, DynamoDB
Learn how to design a secure, serverless, multi-user AI Diet Assistant using Amazon Bedrock, RAG, Knowledge Bases, Amazon Cognito, API Gateway, Lambda, DynamoDB
Malicious URL Detection Using AI/ML: A Multi-Class Classification Approach
Learn how to build a scalable multi-class machine learning pipeline using 110 engineered lexical, domain, and short-URL features to detect web threats with 95% accuracy.
Managing Small Context Windows in Language Models
Learn three practical strategies for managing small context windows in large language models, complete with runnable Python examples for sliding window memory and token budgeting with RAG.
Learn by topic
Linear & Logistic Regression
24 postsFoundational predictive models — the workhorses of applied ML.
Trees & Ensembles
18 postsDecision trees, random forests, XGBoost, LightGBM.
Deep Learning
42 postsNeural networks from perceptrons to transformers.
Natural Language Processing
31 postsTokenization, embeddings, LLMs and beyond.
Computer Vision
26 postsCNNs, object detection, segmentation, diffusion.
Time Series Forecasting
15 postsARIMA, Prophet, and neural forecasters for temporal data.
One practical AI idea, every Wednesday.
Short, honest and practical — from classic ML to LLMs and agents. No hype threads.