The Data Mining Map: an introduction to data science, machine learning, and AI.https://saedsayad.com/https://saedsayad.com/learn/data-mining/https://saedsayad.com/learn/data-mining/What data mining is: a multidisciplinary field for explaining the past and predicting the future, mapped end to end from problem definition to deployment.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/problem-definition/https://saedsayad.com/learn/problem-definition/How to convert a business objective into a well-posed data science problem: the target variable, unit of analysis, success criteria, and a preliminary plan.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/explaining-the-past/https://saedsayad.com/learn/explaining-the-past/Descriptive data mining: how exploration, summarization, and visualization explain what happened in your data and why, before any predictive modeling.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/predicting-the-future/https://saedsayad.com/learn/predicting-the-future/Predictive data mining: how models learned from historical data generalize to predict unseen outcomes, and what separates prediction from description.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/modeling/https://saedsayad.com/learn/modeling/The four families of data mining models: classification, regression, clustering, and association rules — what each predicts and how to choose.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/model-deployment/https://saedsayad.com/learn/model-deployment/Putting a trained model to work on new data: the four deployment paths — tools, programming languages, SQL, and PMML — and the lifecycle after go-live.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/data-exploration/https://saedsayad.com/learn/data-exploration/Explore data with statistical summaries and visualizations: univariate and bivariate analysis, frequency tables, histograms, and box plots explained.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/univariate-analysis/https://saedsayad.com/learn/univariate-analysis/Univariate analysis examines one variable at a time using frequency tables, summary statistics, histograms, and box plots for categorical and numerical data.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/categorical-variables/https://saedsayad.com/learn/categorical-variables/Explore categorical variables, nominal and ordinal, with frequency tables, percentages, bar charts, and pie charts on a real housing example.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/numerical-variables/https://saedsayad.com/learn/numerical-variables/Summarize numerical variables with mean, median, mode, variance, standard deviation, IQR, skewness, and kurtosis, plus histograms and box plots.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/bivariate-analysis/https://saedsayad.com/learn/bivariate-analysis/Bivariate analysis studies the relationship between two variables: categorical vs categorical, numerical vs numerical, and categorical vs numerical.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/categorical-vs-categorical/https://saedsayad.com/learn/categorical-vs-categorical/Analyze two categorical variables with contingency tables, stacked and combination charts, and the chi-square test of independence with a worked example.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/numerical-vs-numerical/https://saedsayad.com/learn/numerical-vs-numerical/Analyze two numerical variables with scatter plots and the Pearson correlation coefficient: formula, interpretation, and a worked temperature-humidity example.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/categorical-vs-numerical/https://saedsayad.com/learn/categorical-vs-numerical/Compare a numerical variable across categories with error-bar charts, box plots, t-tests, and ANOVA, with worked examples on real datasets.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/data-preparation/https://saedsayad.com/learn/data-preparation/How raw measurements become a modeling-ready dataset: data types, dataset anatomy, databases and SQL, and the ETL pipeline that ties them together.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/encoding/https://saedsayad.com/learn/encoding/Encoding transforms categorical variables into numbers models can use: one-hot and ordinal encoding, target-based encoding, and when each one applies.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/binning/https://saedsayad.com/learn/binning/Binning (discretization) turns numerical variables into categorical ones. Compare equal-width and equal-frequency binning with formulas and a worked example.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/classification/https://saedsayad.com/learn/classification/Classification predicts a categorical target from predictors. Tour the nine algorithm families — from ZeroR baselines to SVMs — and when to use each.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/zero-r/https://saedsayad.com/learn/zero-r/ZeroR is the simplest classifier: ignore every predictor and always predict the majority class. Useless alone, essential as a baseline benchmark.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/one-r/https://saedsayad.com/learn/one-r/OneR builds one simple rule per predictor from frequency tables and keeps the most accurate — often nearly as good as far more complex classifiers.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/naive-bayes/https://saedsayad.com/learn/naive-bayes/Naive Bayes classification with Bayes' rule, worked on the Play Golf dataset: likelihood tables, posterior calculation, and Laplace smoothing.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/decision-tree/https://saedsayad.com/learn/decision-tree/Build a decision tree with the ID3 algorithm: entropy, information gain, and recursive splits, worked step by step on the Play Golf dataset.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/linear-discriminant-analysis/https://saedsayad.com/learn/linear-discriminant-analysis/Linear Discriminant Analysis finds the linear combination of predictors that best separates classes, via Fisher's criterion and scatter matrices.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/logistic-regression/https://saedsayad.com/learn/logistic-regression/Logistic regression models binary outcomes with the sigmoid curve, log-odds, and maximum likelihood — including pseudo R-squared and the Wald test.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/k-nearest-neighbors/https://saedsayad.com/learn/k-nearest-neighbors/k-nearest neighbors classifies by majority vote over the closest training cases: distance metrics, choosing k, and why normalization matters.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/neural-networks/https://saedsayad.com/learn/neural-networks/Artificial neural networks: the perceptron, activation functions, backpropagation intuition, and the path from MLPs to transformers.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/support-vector-machine/https://saedsayad.com/learn/support-vector-machine/Support vector machines find the maximum-margin hyperplane: support vectors, soft margins, hinge loss, and the kernel trick with RBF.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/regression/https://saedsayad.com/learn/regression/Regression predicts a numerical target from one or more predictors. Learn simple linear regression, ordinary least squares, residuals, and SSE.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/multiple-linear-regression/https://saedsayad.com/learn/multiple-linear-regression/Multiple linear regression models a numerical target with several predictors. Matrix-form OLS, R-squared, the F-test, and multicollinearity.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/decision-tree-regression/https://saedsayad.com/learn/decision-tree-regression/Regression trees split data by standard deviation reduction instead of information gain. Build one step by step on the golf hours dataset.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/knn-regression/https://saedsayad.com/learn/knn-regression/k-NN regression predicts a numerical target as the average of the k nearest neighbors. Distance functions, weighting, and standardization.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/support-vector-regression/https://saedsayad.com/learn/support-vector-regression/Support vector regression fits the flattest function inside an epsilon-insensitive tube, tolerating small errors. Linear and kernel SVR explained.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/clustering/https://saedsayad.com/learn/clustering/Clustering groups similar data points without labels. Distance-based intuition, the main algorithm families, and where clustering is applied.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/hierarchical-clustering/https://saedsayad.com/learn/hierarchical-clustering/Hierarchical clustering builds a tree of nested clusters. Agglomerative merging, single/complete/average linkage, and dendrograms with a worked example.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/k-means/https://saedsayad.com/learn/k-means/k-means partitions data into k clusters by iterating assign and update steps. The WCSS objective, the algorithm, and a fully worked example.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/self-organizing-maps/https://saedsayad.com/learn/self-organizing-maps/Self-organizing maps project high-dimensional data onto a low-dimensional grid. BMU, neighborhood shrinkage, and weight updates explained.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/association-rules/https://saedsayad.com/learn/association-rules/Association rule mining explained: support, confidence, and lift, the Apriori principle, and a worked market-basket example with five transactions.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/model-evaluation/https://saedsayad.com/learn/model-evaluation/How to evaluate predictive models honestly: hold-out train, validation, and test splits, k-fold cross-validation, and how to detect overfitting.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/model-evaluation-classification/https://saedsayad.com/learn/model-evaluation-classification/Classification metrics from the confusion matrix: accuracy, precision, recall, specificity, F1, ROC curves and AUC, with a worked 2x2 example.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/model-evaluation-regression/https://saedsayad.com/learn/model-evaluation-regression/Regression metrics explained: MAE, MSE, RMSE, relative errors, R-squared, and residual plots, with a fully worked numeric example.Sat, 08 Aug 2026 00:00:00 GMThttps://saedsayad.com/learn/further-readings/https://saedsayad.com/learn/further-readings/A curated reading list for data science and machine learning: classic essays, tutorials, and books recommended by Saed Sayad.Sat, 08 Aug 2026 00:00:00 GMT