Data science/ML/AI

📍If Your Model Suddenly Gets Worse, Check These First Before retraining everything, inspect: • Data drift • Missing values • Feature distribution changes • New categories • Pipeline failures • Label quality Production issues are often data problems, not algorithm problems.

How Does Machine Learning Work?

15 GitHub Repositories For Machine Learning Engineers

List of AI Project Ideas 👨🏻‍💻🤖 - Beginner Projects 🔹 Sentiment Analyzer 🔹 Image Classifier 🔹 Spam Detection System 🔹 Face Detection 🔹 Chatbot (Rule-based) 🔹 Movie Recommendation System 🔹 Handwritten Digit Recognition 🔹 Speech-to-Text Converter 🔹 AI-Powered Calculator 🔹 AI Hangman Game Intermediate Projects 🔸 AI Virtual Assistant 🔸 Fake News Detector 🔸 Music Genre Classification 🔸 AI Resume Screener 🔸 Style Transfer App 🔸 Real-Time Object Detection 🔸 Chatbot with Memory 🔸 Autocorrect Tool 🔸 Face Recognition Attendance System 🔸 AI Sudoku Solver Advanced Projects 🔺 AI Stock Predictor 🔺 AI Writer (GPT-based) 🔺 AI-powered Resume Builder 🔺 Deepfake Generator 🔺 AI Lawyer Assistant 🔺 AI-Powered Medical Diagnosis 🔺 AI-based Game Bot 🔺 Custom Voice Cloning 🔺 Multi-modal AI App 🔺 AI Research Paper Summarizer @datascience_bds

We recently had a request from for Unsupervised Learning notes. To make this resource even more valuable for everyone, we decided to bundle them together with Supervised Learning notes as well! Source: Princeton University Lecture Notes @datascience_bds

ETL Process For Data Analytics

✅ The Most Underrated Habit in Data Science 👉 Keep a modeling journal. After every experiment, write down: • What changed • Why you changed it • The metric before • The metric after • What you learned Six months later, this notebook becomes more valuable than your code.

Power BI vs Microsoft Fabric

🚩7 Red Flags You Should Check in Every Dataset Before EDA, look for these. 🔻Duplicate rows 🔻Missing values that aren't random 🔻Impossible numbers (negative ages, future dates) 🔻Columns with only one value 🔻Categories with inconsistent spelling 🔻Target leakage 🔻Suspiciously perfect distributions Catching these early saves hours of debugging later.

🧠 LLM Wiki LLM Wiki is a concept by Andrej Karpathy for maintaining a continuously evolving Markdown knowledge base instead of relying only on document retrieval. As information is processed, related topics are organized into linked wiki pages, making knowledge easier to navigate, update, and reuse over time. This is a thoughtful read if you're interested in how long-term knowledge systems for LLMs could evolve. 🔗 https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f

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Why Learning Rate Can Make or Break Training Think of gradient descent as walking downhill. The learning rate controls your step size. Too small? You'll eventually reach the bottom. It just takes forever. Too large? You'll keep overshooting the minimum. Sometimes you'll bounce back and forth without ever converging. That's why training loss can suddenly explode. Not because your model is bad. Because your optimizer is taking steps that are simply too big. The goal isn't the fastest movement. It's stable progress. 👉 Takeaway: When loss behaves unpredictably, learning rate should be one of the first things you investigate.

Vector Databases

superset Superset is a modern data exploration and data visualization platform. It provides A no-code interface for building charts quickly, A powerful, web-based SQL Editor for advanced querying, A wide array of beautiful visualizations to showcase your data, Highly extensible security roles and authentication options, An API for programmatic customization, and so much more. Creator: apache Stars ⭐️: 73,475 Forked by: 17,699 Github Repo: https://github.com/apache/superset ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @github_repositories_bds for more cool repositories. This channel belongs to @bigdataspecialist group

❔Why Batch Normalization Makes Deep Networks Easier to Train Training a deep network is a bit like passing a message through twenty people. If each person changes the message slightly, by the end it's completely different. The same thing happens inside neural networks. As earlier layers update, the distribution of values reaching later layers keeps changing. Every layer has to constantly readjust. Batch Normalization reduces this problem by normalizing each mini-batch during training. That gives later layers a more stable input distribution. ✅ The result? • Faster convergence • Higher learning rates • Less sensitivity to initialization • Better training stability