Machine Learning

  1. Introduction to Machine Learning

    What machine learning is and how it differs from classical statistics: key concepts, learning types and clinical applications, in plain terms.

  2. Dataset Division and Data Leakage

    Train, validation and test splits explained, plus the data leakage mistakes that silently inflate model performance in clinical studies.

  3. Encoding of Categorical Variables

    Encoding categorical variables for machine learning: one-hot, ordinal and target encoding, with Python examples on clinical datasets.

  4. Feature Engineering and Selection

    Comprehensive guide to Feature Engineering and Selection in Machine Learning, covering modification techniques, feature creation, and selection methods with practical Python examples for data scientists and ML practitioners.

  5. Dimensionality Reduction Techniques

    Dimensionality reduction explained: PCA, t-SNE and UMAP, when to use each, and how to apply them to high-dimensional clinical data.

  6. Machine Learning Models: A Complete Guide to Classification Approaches

    How machine learning models are classified: paradigm-based (supervised, unsupervised, reinforcement) and problem-based approaches explained.

  7. Linear Regression

    Linear regression for machine learning: mathematical foundations, Python implementation on the diabetes dataset, and model evaluation.

  8. NonLinear Regression

    Nonlinear regression explained: polynomial, exponential and logistic models, fitting and diagnostics, with Python examples on medical data.

  9. Machine Learning Distances

    A comprehensive guide to distance metrics in machine learning, from Euclidean to Hellinger distances. Starting with practical examples, this guide introduces mathematical concepts, provides Python implementations, and explores use cases for each metric—with a focus on medical applications.

  10. K-Nearest Neighbors (KNN)

    A guide to K-Nearest Neighbors (KNN), covering its principles, medical uses, and implementation in Python. This article details data preparation, parameter tuning, and performance measurement for this classification algorithm.

  11. Support Vector Machines

    Support Vector Machines explained: margins, kernels and the C parameter, with a Python example on clinical classification data.

  12. Naive Bayes

    The Naive Bayes classifier explained: conditional independence, priors and likelihoods, with a Python example on medical data.

  13. Decision Tree

    Decision trees explained: splitting criteria, Gini and entropy, pruning and overfitting, with a Python example on clinical data.

  14. Ensemble Models

    A comprehensive guide to ensemble models in machine learning, covering bagging, boosting, stacking, and voting techniques. This article explains the theoretical foundations, compares different approaches through detailed analysis, and provides practical Python implementation examples.

  15. Random Forest

    A comprehensive guide to Random Forest: covering principles, medical applications, Python examples, and key advantages. Explores bagging, decision tree construction, and voting mechanisms for classification and regression. Essential for data scientists mastering this machine learning technique.

  16. Machine Learning Model Evaluation

    A practical guide to model evaluation in machine learning. Classification, regression and clustering metrics explained with worked Python (scikit-learn) examples, written from a clinical-research perspective.