Content from Machine Learning from Scratch, covering statistical methods, machine learning algorithms, and practical implementations with mathematical foundations.
Practical reporting guidelines, summary of key concepts, test selection parameters table, multiple comparison corrections table, and scipy.stats functions reference. Complete reference guide for hypothesis testing.
Family-wise error rate, false discovery rate, Bonferroni correction, Holm's method, and Benjamini-Hochberg procedure. Learn how to control error rates when conducting multiple hypothesis tests.
Cohen's d, practical significance, interpreting effect sizes, and why tiny p-values can mean tiny effects. Learn to distinguish statistical significance from practical importance.
Power analysis, sample size determination, MDE calculation, and avoiding underpowered studies. Learn how to design studies with adequate sensitivity to detect meaningful effects.
Understanding false positives, false negatives, statistical power, and the tradeoff between error types. Learn how to balance Type I and Type II errors in study design.
One-way ANOVA, post-hoc tests, assumptions, and when to use ANOVA. Learn how to compare means across three or more groups while controlling Type I error rates.
F-distribution, F-test for comparing variances, F-test in regression, and nested model comparison. Learn how F-tests extend hypothesis testing beyond means to variance analysis and model comparison.
Complete guide to t-tests including one-sample, two-sample (pooled and Welch), paired tests, assumptions, and decision framework. Learn when to use each variant and how to check assumptions.
Mathematical equivalence between confidence intervals and hypothesis tests, test assumptions (independence, normality, equal variances), and choosing between z and t tests. Learn how to validate assumptions and select appropriate tests.
Complete guide to z-tests including one-sample, two-sample, and proportion tests. Learn when to use z-tests, how to calculate test statistics, and interpret results when population variance is known.
Foundation of hypothesis testing covering p-values, null and alternative hypotheses, one-sided vs two-sided tests, and test statistics. Learn how to set up and interpret hypothesis tests correctly.
Master DBSCAN (Density-Based Spatial Clustering of Applications with Noise), the algorithm that discovers clusters of any shape without requiring predefined cluster counts. Learn core concepts, parameter tuning, and practical implementation.
Learn quadratic programming (QP) for portfolio optimization, including the mean-variance framework, efficient frontier construction, and scipy implementation with practical examples.
Master the Vehicle Routing Problem with Time Windows (VRPTW), including mathematical formulation, constraint programming, and practical implementation using Google OR-Tools for logistics optimization.
Learn minimum cost flow optimization for slotting problems, including network flow theory, mathematical formulation, and practical implementation with OR-Tools. Master resource allocation across time slots, capacity constraints, and cost structures.
Complete guide to Mixed Integer Linear Programming (MILP) for factory optimization, covering mathematical foundations, constraint modeling, branch-and-bound algorithms, and practical implementation with Google OR-Tools. Learn how to optimize production planning with discrete setup decisions and continuous quantities.
Learn CP-SAT rostering using Google OR-Tools to solve complex workforce scheduling problems with binary decision variables, coverage constraints, and employee availability. Master constraint programming for optimal employee shift assignments.
Master NHITS (Neural Hierarchical Interpolation for Time Series), a deep learning architecture for multi-scale time series forecasting. Learn hierarchical decomposition, neural interpolation, and how to implement NHITS for complex temporal patterns in retail, energy, and financial data.
Complete guide to N-BEATS, an interpretable deep learning architecture for time series forecasting. Learn how N-BEATS decomposes time series into trend and seasonal components, understand the mathematical foundation, and implement it in PyTorch.
Complete guide to HDBSCAN clustering algorithm covering density-based clustering, automatic cluster selection, noise detection, and handling variable density clusters. Learn how to implement HDBSCAN for real-world clustering problems.
Comprehensive guide to hierarchical clustering, including dendrograms, linkage criteria (single, complete, average, Ward), and scikit-learn implementation. Learn how to build cluster hierarchies and interpret dendrograms.
Learn SARIMA (Seasonal AutoRegressive Integrated Moving Average) for forecasting time series with seasonal patterns. Includes mathematical foundations, step-by-step implementation, and practical applications.
Learn exponential smoothing for time series forecasting, including simple, double (Holt's), and triple (Holt-Winters) methods. Master weighted averages, smoothing parameters, and practical implementation in Python.
Learn Prophet time series forecasting including additive decomposition, trend modeling, seasonal patterns, and holiday effects. Master Facebook's powerful forecasting tool for business applications.
Master K-means clustering from mathematical foundations to practical implementation. Learn the algorithm, initialization strategies, optimal cluster selection, and real-world applications.
A comprehensive guide covering t-SNE (t-Distributed Stochastic Neighbor Embedding), including mathematical foundations, probability distributions, KL divergence optimization, and practical implementation. Learn how to visualize complex high-dimensional datasets effectively.
A comprehensive guide covering LIME (Local Interpretable Model-Agnostic Explanations), including mathematical foundations, implementation strategies, and practical applications. Learn how to explain any machine learning model's predictions with interpretable local approximations.
A comprehensive guide covering UMAP dimensionality reduction, including mathematical foundations, fuzzy simplicial sets, manifold learning, and practical implementation. Learn how to preserve both local and global structure in high-dimensional data visualization.
A comprehensive guide covering Principal Component Analysis, including mathematical foundations, eigenvalue decomposition, and practical implementation. Learn how to reduce dimensionality while preserving maximum variance in your data.
A comprehensive guide to XGBoost (eXtreme Gradient Boosting), including second-order Taylor expansion, regularization techniques, split gain optimization, ranking loss functions, and practical implementation with classification, regression, and learning-to-rank examples.
A comprehensive guide to SHAP values covering mathematical foundations, feature attribution, and practical implementations for explaining any machine learning model
A comprehensive guide covering LightGBM gradient boosting framework, including leaf-wise tree growth, histogram-based binning, GOSS sampling, exclusive feature bundling, mathematical foundations, and Python implementation. Learn how to use LightGBM for large-scale machine learning with speed and memory efficiency.
A comprehensive guide to CatBoost (Categorical Boosting), including categorical feature handling, target statistics, symmetric trees, ordered boosting, regularization techniques, and practical implementation with mixed data types.
A comprehensive guide to Isolation Forest covering unsupervised anomaly detection, path length calculations, harmonic numbers, anomaly scoring, and implementation in scikit-learn. Learn how to detect rare outliers in high-dimensional data with practical examples.
A comprehensive guide to boosted trees and gradient boosting, covering ensemble learning, loss functions, sequential error correction, and scikit-learn implementation. Learn how to build high-performance predictive models using gradient boosting.
A comprehensive guide to Random Forest covering ensemble learning, bootstrap sampling, random feature selection, bias-variance tradeoff, and implementation in scikit-learn. Learn how to build robust predictive models for classification and regression with practical examples.
A comprehensive guide to CART (Classification and Regression Trees), including mathematical foundations, Gini impurity, variance reduction, and practical implementation with scikit-learn. Learn how to build interpretable decision trees for both classification and regression tasks.
A comprehensive guide to logistic regression covering mathematical foundations, the logistic function, optimization algorithms, and practical implementation. Learn how to build binary classification models with interpretable results.
A comprehensive guide to Poisson regression for count data analysis. Learn mathematical foundations, maximum likelihood estimation, rate ratio interpretation, and practical implementation with scikit-learn. Includes real-world examples and diagnostic techniques.
A comprehensive guide to spline regression covering B-splines, knot selection, natural cubic splines, and practical implementation. Learn how to model complex non-linear relationships with piecewise polynomials.
A comprehensive guide to multinomial logistic regression covering mathematical foundations, softmax function, coefficient estimation, and practical implementation in Python with scikit-learn.
A comprehensive guide covering Elastic Net regularization, including mathematical foundations, geometric interpretation, and practical implementation. Learn how to combine L1 and L2 regularization for optimal feature selection and model stability.
A comprehensive guide covering polynomial regression, including mathematical foundations, implementation in Python, bias-variance trade-offs, and practical applications. Learn how to model non-linear relationships using polynomial features.
A comprehensive guide covering Ridge regression and L2 regularization, including mathematical foundations, geometric interpretation, bias-variance tradeoff, and practical implementation. Learn how to prevent overfitting in linear regression using coefficient shrinkage.
A comprehensive guide covering relationships between variables, including covariance, correlation, simple and multiple regression. Learn how to measure, model, and interpret variable associations while understanding the crucial distinction between correlation and causation.
A comprehensive guide covering data quality fundamentals, including measurement error, systematic bias, missing data mechanisms, and outlier detection. Learn how to assess, diagnose, and improve data quality for reliable statistical analysis and machine learning.
A comprehensive guide covering statistical modeling fundamentals, including measuring model fit with R-squared and RMSE, understanding the bias-variance tradeoff between overfitting and underfitting, and implementing cross-validation for robust model evaluation.
A comprehensive guide to foundational data visualization techniques including histograms, box plots, and scatter plots. Learn how to understand distributions, identify outliers, reveal relationships, and build intuition before statistical analysis.
A comprehensive guide to the Gauss-Markov assumptions that underpin linear regression. Learn the five key assumptions, how to test them, consequences of violations, and practical remedies for reliable OLS estimation.
A comprehensive guide to normalization in machine learning, covering min-max scaling, proper train-test split implementation, when to use normalization vs standardization, and practical applications for neural networks and distance-based algorithms.
A comprehensive guide to sampling theory and methods in data science, covering simple random sampling, stratified sampling, cluster sampling, sampling error, and uncertainty quantification. Learn how to design effective sampling strategies and interpret results from sample data.
A comprehensive guide covering probability distributions for data science, including normal, t-distribution, binomial, Poisson, exponential, and log-normal distributions. Learn when and how to apply each distribution with practical examples and visualizations.
A comprehensive guide covering statistical inference, including point and interval estimation, confidence intervals, hypothesis testing, p-values, Type I and Type II errors, and common statistical tests. Learn how to make rigorous conclusions about populations from sample data.
A comprehensive guide covering descriptive statistics fundamentals, including measures of central tendency (mean, median, mode), variability (variance, standard deviation, IQR), and distribution shape (skewness, kurtosis). Learn how to choose appropriate statistics for different data types and apply them effectively in data science.
A comprehensive guide to the Central Limit Theorem covering convergence to normality, standard error, sample size requirements, and practical applications in statistical inference. Learn how CLT enables confidence intervals, hypothesis testing, and machine learning methods.
A comprehensive guide to probability theory fundamentals, covering random variables, probability distributions, expected value and variance, independence and conditional probability, Law of Large Numbers, and Central Limit Theorem. Learn how to apply probabilistic reasoning to data science and machine learning applications.
Master data classification with this comprehensive guide covering quantitative vs. qualitative data, discrete vs. continuous data, and the data type hierarchy including nominal, ordinal, interval, and ratio scales. Learn how to choose appropriate analytical methods, avoid common pitfalls, and apply correct preprocessing techniques for data science and machine learning projects.
A comprehensive guide to standardization in machine learning, covering mathematical foundations, practical implementation, and Python examples. Learn how to properly standardize features for fair comparison across different scales and units.
A comprehensive guide to the Sum of Squared Errors (SSE) metric in regression analysis. Learn the mathematical foundation, visualization techniques, practical applications, and limitations of SSE with Python examples and detailed explanations.
A comprehensive guide to L1 regularization (LASSO) in machine learning, covering mathematical foundations, optimization theory, practical implementation, and real-world applications. Learn how LASSO performs automatic feature selection through sparsity.
A comprehensive guide to multiple linear regression, including mathematical foundations, intuitive explanations, worked examples, and Python implementation. Learn how to fit, interpret, and evaluate multiple linear regression models with real-world applications.
Learn about multicollinearity in regression analysis with this practical guide. VIF analysis, correlation matrices, coefficient stability testing, and approaches such as Ridge regression, Lasso, and PCR. Includes Python code examples, visualizations, and useful techniques for working with correlated predictors in machine learning models.
A comprehensive guide to Ordinary Least Squares (OLS) regression, including mathematical derivations, matrix formulations, step-by-step examples, and Python implementation. Learn the theory behind OLS, understand the normal equations, and implement OLS from scratch using NumPy and scikit-learn.
A complete hands-on guide to simple linear regression, including formulas, intuitive explanations, worked examples, and Python code. Learn how to fit, interpret, and evaluate a simple linear regression model from scratch.
A comprehensive guide to R-squared, the coefficient of determination. Learn what R-squared means, how to calculate it, interpret its value, and use it to evaluate regression models. Includes formulas, intuitive explanations, practical guidelines, and visualizations.
A comprehensive guide to Generalized Linear Models (GLMs), covering logistic regression, Poisson regression, and maximum likelihood estimation. Learn how to model binary outcomes, count data, and non-normal distributions with practical Python examples.
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