MMuhammad Ariel Shakaramiro·Sep 4Machine Learning from Scratch: Supervised vs Unsupervised Learning (and How to Tell If a Model Actually Learned)Learn the difference between Supervised and Unsupervised Learning, how they work, and how to measure performance with accuracy, precision, recall.9 min read·shaka-ai.hashnode.dev3A
TTharun·Aug 10What Does It Mean For a Machine to "Learn"?Ever wondered how a machine actually learns? Learn the core idea behind ML using data, parameters, predictions, and simple real-world examples.7 min read·tharunpandya.hashnode.dev1
HHao Hua·Jul 21Conceptual Training and Inference Workflow of a Deep Learning Model for Supervised Learning TaskTL;DR This page conceptually describes the inference workflow of a Deep Learning Model, wherein a pretrained Deep Learning Model may receive an input for the inference of an output according to the p6 min read·h2c-blogs.hashnode.dev
BBerkan Sesen·Jul 5Handling Class Imbalance: Downsampling, SMOTE, and BeyondCompare five strategies for imbalanced classification: random downsampling, upsampling, SMOTE, and class weights. Evaluate with precision-recall curves and F1, not just accuracy.13 min read·sesenai.hashnode.dev
SSilent Scribe·Jul 4From Study Hours to Scores: How Linear Regression Predicts the FutureDiscover how Linear Regression predicts outcomes like student marks using a simple formula, Python steps, and real examples.4 min read·silentscribejournal.hashnode.dev
BBerkan Sesen·Jun 29AIC and BIC: Choosing the Right Model Without OverfittingDerive the log-likelihood from first principles, then build AIC and BIC as penalised model selection criteria. Compare polynomial fits, visualise the bias-variance tradeoff, and understand when to use each criterion.13 min read·sesenai.hashnode.dev
DDivyajot Kaur·Jun 26Part 12: Logistic Regression: How Machines Learn to Make Yes-or-No DecisionsA simple, step-by-step guide to understanding how Logistic Regression makes accurate classification decisions.6 min read·ai-beginners-journey.hashnode.dev
DDivyajot Kaur·Jun 20Part 11: What is Gradient Descent?How Machine Learning Models Learn and Improve4 min read·ai-beginners-journey.hashnode.dev
BBerkan Sesen·Jun 12LDA vs PCA: Supervised Meets Unsupervised Dimensionality ReductionCompare PCA and Fisher's Linear Discriminant Analysis on the Iris dataset. Learn when unsupervised reduction fails, why labels matter, and the eigenvalue problem behind both methods.13 min read·sesenai.hashnode.dev
AAnik Chand·May 23#2 Supervised vs Unsupervised LearningMachine Learning is a branch of Artificial Intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Machine learning is mainl2 min read·anikchandml.hashnode.dev
