Logistic Regression and Classification

Build a strong foundation in Data Science with Logistic Regression and Classification, a practical course in probabilistic classification, decision boundaries, model evaluation, and real-world classifier design. You will learn how classification models turn data into predictive decisions, how to evaluate their performance, and how to communicate results with confidence.

Apply Logistic Regression and Classification To Real Data Science Problems

• Learn how logistic regression converts features into probability scores for practical classification tasks.
• Understand decision boundaries, sigmoid functions, odds, log-odds, and coefficient interpretation.
• Evaluate classifiers using confusion matrices, precision, recall, F1, ROC curves, AUC, and threshold selection.
• Design more reliable real-world models with preprocessing, regularisation, calibration, imbalance handling, and an end-to-end case study.

This Data Science course teaches the core theory and applied workflow behind Logistic Regression and Classification.

Logistic Regression and Classification begins with the foundations of classification problems, showing how predictive decisions differ from continuous prediction and why probability-based outputs matter. You will move from linear models to probability scores, explore the sigmoid function, and learn how decision boundaries separate classes in a clear and practical way.

The course then explains the mechanics behind logistic regression, including odds, log-odds, coefficient interpretation, maximum likelihood, cross-entropy loss, and gradient descent. These lessons help you understand not just how to run a model, but why it behaves the way it does and how training choices affect performance.

You will also develop a practical modelling workflow for Data Science projects, including feature scaling, encoding, preprocessing, binary logistic regression, and multiclass classification. Dedicated lessons on model evaluation cover confusion matrices, precision, recall, F1, cost-aware evaluation, ROC curves, AUC, and threshold selection so you can judge classifier performance in context.

Real-world classifier design often involves imperfect data, unequal class distributions, and the need for reliable probability scores. This course addresses those challenges through class imbalance strategies, L1 and L2 regularisation, probability calibration, interpretation, explanation, and communication of results. By the end, you will be able to build, evaluate, improve, and explain classification models with the practical judgment expected in applied Data Science work.