Quick tech ticks and comparisons
Topics in the Machine Learning category.
A friendly, story-style introduction to Machine Learning. What it is, how it differs from regular programming, the three big types, the full ML pipeline, and the key ideas every beginner should know. With diagrams.
A friendly story-style tour of every machine learning technique covered in this handbook. No math, no code, no jargon. Just a clear, plain English explanation of what each tool does and when you would actually use it in real life.
Complete beginner's guide to Linear Regression - learn to predict continuous values with real-world examples and Python code.
Deep dive into Linear Regression assumptions, challenges, and advanced techniques - scaling, feature engineering, handling violations, and regularization explained simply.
Master Linear Regression with expert-level topics - categorical encoding, deep multicollinearity, influential points, cross-validation, polynomial features, and real-world techniques.
Master Logistic Regression from zero to hero - the most popular algorithm for classification. Beginner-friendly explanations with real examples and Python code.
Master Regularization from zero - Ridge, Lasso, Elastic Net explained simply with analogies, math, and Python code. Stop overfitting forever!
A simple beginner friendly guide to decision trees. Part 1 covers what they are, why we use them, and how they actually work step by step using real life examples.
Part 2 of the decision trees tutorial. We explain in very simple terms what impurity means, how to measure it using Gini and entropy, and how the algorithm decides which question to ask using information gain.
Part 3 of the decision trees tutorial. We walk through the full training process, talk about overfitting and pruning, discuss real world advantages and disadvantages, and then build a working decision tree in Python on a real dataset.
A simple introduction to ensemble learning. Why combining many weak models beats relying on one strong model. The wisdom of crowds, bias and variance, and the two big families of ensembles.
Part 2 of the ensembles tutorial. We explain bootstrap sampling, build up to the Random Forest algorithm step by step, and walk through full hyperparameter tuning using number of trees, maximum depth, and grid search with cross validation.
Part 3 of the ensembles tutorial. We explain boosting from the ground up, walk through AdaBoost and Gradient Boosting in plain language, meet the modern champions like XGBoost and LightGBM, and finish with practical advice on when to use each method.
A complete beginner friendly guide to the metrics machine learning uses to decide whether two points are similar or different. We cover distance-based, angle-based, correlation-based, and set-based metrics with simple examples and Python code.
A complete beginner friendly guide to K-Means Clustering. We cover unsupervised learning, how K-Means actually works step by step, picking the right number of clusters, common pitfalls, and a full Python demonstration.
A complete beginner friendly guide to hierarchical clustering. We cover the agglomerative algorithm step by step, the different linkage methods, how to read a dendrogram, practical considerations, and a full Python demonstration.
A complete beginner friendly guide to K-Nearest Neighbours (KNN). We cover how it works, weighted aggregation, picking K, key practical considerations, and a full Python demonstration for both classification and regression.
A complete beginner-friendly guide to Naive Bayes. Learn how probability drives classification, why the "naive" assumption works in practice, and build a spam detector step by step in Python.
A complete beginner-friendly guide to Support Vector Machines. Learn how SVMs find the best boundary between classes, what the kernel trick does, and how to use SVMs in Python for real classification tasks.
A complete beginner-friendly guide to PCA and dimensionality reduction. Learn why too many features hurt models, how PCA finds the directions of maximum variance, and how to apply it in Python step by step.
A beginner-friendly guide to semi-supervised learning — the technique that uses a small amount of labelled data and a large amount of unlabelled data together. Covers self-training, label propagation, and GANs with Python examples.
A beginner-friendly guide to self-supervised learning — how models train themselves on unlabelled data by predicting parts of the input from other parts. Covers masked prediction, contrastive learning, and the role of SSL in GPT and BERT.
A beginner-friendly guide to reinforcement learning. Learn how an agent learns by trial and error using rewards and punishments, how Q-learning works, and how RLHF is used to train language models like ChatGPT.
A beginner-friendly guide to Gaussian Mixture Models (GMMs). Learn how probabilistic clustering differs from K-Means, what the EM algorithm does, and how to fit GMMs in Python for soft cluster assignments.
A beginner-friendly guide to association rules and the Apriori algorithm. Learn how to find "customers who bought X also bought Y" patterns in transaction data, with support, confidence, lift explained and full Python examples.
A beginner-friendly guide to anomaly detection. Learn how to find unusual data points using isolation forest, one-class SVM, and statistical methods. Covers fraud detection, network intrusion, and quality control with full Python examples.
A single-page quick reference for every ML topic in this handbook. Libraries to import, classes to use, key hyperparameters, evaluation metrics with formulas, and a master cheat sheet for picking the right tool and the right metric.
An A-to-Z plain English dictionary of every machine learning term used in this handbook. No equations, no jargon defined with more jargon. Just clear short explanations for every concept you will meet.
A symptom to cause to fix troubleshooting guide for the most common machine learning problems. When your model behaves weirdly, look up the symptom here and find out what is going wrong.
A frequently asked questions page answering every common 'what's the difference between X and Y' question in machine learning. Quick, clear, side-by-side comparisons of the concepts that confuse beginners most.
One flashcard per algorithm. The core idea, an everyday analogy, when to use it, and a tiny code snippet. Perfect for review, revision, or refreshing your memory right before an interview.
A side-by-side visual tour of how every classifier carves up the same 2D dataset. See exactly why linear models draw straight lines, decision trees make boxes, KNN makes wavy regions, and ensembles smooth things out.
A complete end-to-end ML project on the Titanic dataset, from raw CSV to final evaluation. We do data exploration, cleaning, feature engineering, model comparison, hyperparameter tuning, and final test set evaluation - showing exactly how all the pieces from the previous tutorials fit together.
A beginner-friendly guide to how machine learning works with text. Learn how computers understand language through simple, real-world examples—no heavy math, just clear explanations of sentiment analysis, spam detection, named entity recognition, and more.