KKelvin R. Tobias4d ago · 17 min readTough Vikings Take Delayed Icebaths 🧊Understanding the Machine Learning Lifecycle: Training, Validation, Testing, Deployment, and Inference Ever trained a machine learning model and wondered, "Okay… now what?" You import your libraries, kelvinintech.hashnode.dev
BBahati Brenda KizitoOct 12 · 19 min readI Finally Understand What an ML Pipeline Actually DoesImagine training a machine learning model today. You load a dataset, clean it, transform a few columns, train a model, check its accuracy, and save the result. Everything works. Now imagine doing the godfident-data.hashnode.dev
LLibrarymindsOct 5 · 24 min readSemantic Search Over Transcripts in Python: What Embeddings Fix and What They Don'tShort answer: keyword search finds the words you typed, and vector search finds paragraphs that sit close to them in meaning. I built both in about 120 lines of Python, ran them on a made-up talk tran15KAlibraryminds.hashnode.dev
FFaith MwendeSep 29 · 4 min readI Finally Understand AUC, Confusion Matrix, and Classification Report (Day 4)Day 3 was splitting. Day 4 was my first actual model : a baseline logistic regression. The model trained fine. The AUC looked great. But the confusion matrix and classification report are what finallybeginnerslog.hashnode.dev
FFaith MwendeSep 29 · 3 min readSplitting Fraud Data Without Breaking the Ratio (Day 3)Day 1 was setup. Day 2 was looking at the data. Day 3 was the first real modeling decision: how do I split this data without breaking it? The problem it solves You can't train a model on all your databeginnerslog.hashnode.dev
MMuhammad Ariel ShakaramiroSep 6 · 4 min readIntent Classification with TF-IDF + Logistic RegressionThe previous two notes used TF-IDF to measure similarity between texts (FAQ search). This time the approach is different: TF-IDF is used as a feature to train a classifier that predicts the intent behshaka-ai.hashnode.dev
MMuhammad Ariel ShakaramiroSep 6 · 6 min readText Vectorizer Explained: BoW, N-gram, TF-IDF, and Word EmbeddingComputers can't read words the way humans do — everything has to be converted into numbers before a machine learning model can process it. That conversion is what's called a text vectorizer. In this nshaka-ai.hashnode.dev
MMuhammad Ariel ShakaramiroSep 4 · 10 min readHands-On: Classifying Iris Flowers — Logistic Regression vs Decision Treeby Muhammad Ariel Shakaramiro In Part 1, I covered the theory behind Supervised vs Unsupervised Learning and how to measure model performance. Now it's time to go hands-on: training an actual model th3Ashaka-ai.hashnode.dev
MMuhammad Ariel ShakaramiroSep 4 · 11 min readHands-On: Unsupervised Learning — Clustering Iris Flowers with KMeans (Plus Follow-Up Experiments)by Muhammad Ariel Shakaramiro In Part 1, I covered the theory behind Supervised vs Unsupervised Learning, and in Part 2 I put the supervised side into practice — training a model to recognize Iris speshaka-ai.hashnode.dev
OOteopeSep 3 · 14 min readHow much predictive information is actually hidden in a chess opening?📌 This article was originally written and published by me on HackerNoon. A data-driven investigation into board geometry, player Elo, and the limits of early-game predictions. 1. Introduction: Beyoteopeml.hashnode.dev
