Machine Learning Basics
What learning is: loss, overfitting, evaluation — the foundation for everything
01
·Machine Learning Basics·FREE·6 min read
What Machine Learning Really Is — Understanding “Learning” Without the Math
What actually separates writing a program from training a model. Rules versus examples, memorization versus generalization, and a map of supervised, unsupervised, and reinforcement learning — with almost no math.
02
·Machine Learning Basics·FREE·8 min read
Loss Functions and Optimization — How a Model Learns From Being Wrong
Why MSE and cross-entropy have the shapes they do, what the gradient actually points at, and one step of gradient descent taken apart with equations, a draggable figure, and ten lines of numpy — divergence included.
03
·Machine Learning Basics·★ MEMBER·8 min read
Overfitting and Evaluation Design — Be Suspicious of 99% Accuracy
Why a model that fits its training data perfectly falls apart in production: the division of labour between train, validation and test, cross-validation, the data leakage that ruins more projects than any modelling mistake, and how accuracy lies on imbalanced data.
04
·Machine Learning Basics·FREE·PAPER·11 min read
Decision Trees and Gradient Boosting — Still the Champion on Tabular Data
On data made of rows and columns, ensembles of decision trees are still the first thing to reach for. From the intuition behind a single split to boosting, what XGBoost and LightGBM actually do, and why neural networks have not taken this territory — built up from nothing.
05
·Machine Learning Basics·★ MEMBER·PAPER·11 min read
Unsupervised Learning from Scratch — Clustering and Dimensionality Reduction
How to pull structure out of data that carries no labels at all, assuming nothing. What k-means, hierarchical clustering and DBSCAN are each good for, what PCA is actually maximizing, and the three ways people misread a t-SNE or UMAP picture — metaphor, then formula, then a figure you can move, then code.
06
·Machine Learning Basics·★ MEMBER·PAPER·11 min read
Self-Supervised Learning — The Day Unlabeled Data Became an Asset
Nobody has to label the data — the data can write its own exam. A ground-up tour of the two families (masked prediction and contrastive learning), from the intuition through the InfoNCE loss, an interactive figure, PyTorch code, and finally why LLM pretraining is the largest self-supervised system ever run.
07
·Machine Learning Basics·★ MEMBER·10 min read
Data Leakage and Experiment Hygiene — When the Score Is Too Good, Suspect It
When a model scores better than you expected, that is not good news yet. This article splits data leakage into five concrete shapes — outcome-derived columns, preprocessing before the split, time running backwards, duplicates and groups, and a worn-out test set — then covers the detection moves that actually find them and the experiment records that let you dissect the day the number jumped.
08
·Machine Learning Basics·★ MEMBER·10 min read
Imbalanced Data in Practice — What to Optimize When 99% Is Normal
When only 1% of your data is positive, accuracy lies. We build up from the confusion matrix to PR curves, class weighting, resampling, probability calibration, and deriving the decision threshold from cost — no prior knowledge assumed.
09
·Machine Learning Basics·★ MEMBER·8 min read
ML System Design — The 90% Outside the Model
The accuracy you hit in a notebook is not a promise about production. Feature definitions, training-serving skew, monitoring that catches slow decay, and the retraining loop — the 90% that lives outside the model, laid out in the order you actually design it.