Feb 11 · 10 min read · Introduction Hey there! This week, I went full detective mode on regression models during my DataraFlow internship. What started as a simple polynomial curve-fitting exercise on a tiny salary dataset evolved into building a production-ready used-car ...
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Jan 4 · 11 min read · Why logistic regression confused me at first? Before I started learning Logistic regression in detail, I knew that it is used for classification problems, but the name says “regression”. That already felt inconsistent. Most explanations I found jumpe...
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Oct 3, 2025 · 2 min read · 📖 Your journey to mastering AI begins with one of the most important steps: choosing the right model. 1️⃣ The Foundations Every AI project starts with a problem definition: prediction, classification, clustering, or generation. Choosing the wrong...
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Feb 28, 2025 · 5 min read · Hello everyone, we at Nomadev AI, back with another blog of our series WTF IS AI? Welcome to Day 4 of our 30-day series on Machine Learning, Deep Learning, and Generative AI.No jargon, no buzzwords, just practical, beginner-friendly explanations to h...
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Jan 30, 2025 · 3 min read · In the early days of LLMs, things were simpler. GPT-3.5 reigned supreme, and developers only had to integrate one model, one framework, and one standard. Fast forward to today, and the landscape has transformed dramatically. With a surge of new model...
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Jan 19, 2025 · 17 min read · Table of Content 6. Linear Model Selection and Regularization 6.1 Subset Selection 6.1.1 Best Subset Selection 6.1.2 Stepwise Selection 6.1.3 Choosing the Optimal Model 6.2 Shrinkage Methods 6.2.1 Ridge Regression 6.2.2 The Lasso 6.3 Dimensi...
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Nov 27, 2024 · 6 min read · Welcome to the final part of our journey. After we built our Machine Learning models, I guess we still have some questions: How do we handle the bias-variance tradeoff when building a model and checking its performance? How do we pick the best valu...
Join discussionSep 22, 2024 · 4 min read · In machine learning, selecting the right model and tuning its hyperparameters is a critical step toward achieving optimal performance. Several techniques exist to assist in this process, ranging from exhaustive searches like GridSearchCV to more rand...
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Sep 12, 2024 · 7 min read · Introduction There are a few Machine Learning (ML) algorithms to choose from when building an ML model. Different ML algorithms perform differently on the same dataset due to differences in algorithm complexity, bias-variance tradeoff, nature of data...
Join discussionAug 8, 2024 · 7 min read · Till now, we have been dividing our main dataset to Training Set and Test set Let's now split the Training set to 10 parts Then what we're going to do is we're going to train the data on nine of these folds and keep one fold as an unseen fold for v...
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