Machine Learning using Python

Machine learning becomes easier to approach when its statistical foundations, Python tools, and modelling methods are taught in a deliberate sequence. Machine Learning using Python takes that route: beginning with core concepts and descriptive analytics, it moves from probability and hypothesis testing into model development, evaluation, and a range of practical applications.

Build a foundation before building models

The opening chapters introduce machine learning and Python, then develop the groundwork through data analysis, probability distributions, and hypothesis tests. This progression gives readers context for the modelling techniques that follow, rather than treating algorithms as disconnected recipes.

From regression to classification

Dedicated chapters explore regression and classification, including logistic regression and decision trees. Model evaluation is part of the discussion, with measures and tools such as RMSE, R-squared, confusion matrices, ROC AUC, precision, and recall. The emphasis is on understanding how a model performs—not only how to fit one.

Explore a wider range of methods

The book extends beyond introductory supervised learning to cover gradient descent, K-nearest neighbors, random forests, bagging, boosting, and grid search. Readers then encounter clustering approaches, including K-Means and hierarchical clustering, alongside distance and similarity measures.

Applications in time, choice, and language

Later chapters address time-series forecasting, recommender systems, and text analytics. Topics include moving averages and ARIMA; association rules, collaborative filtering, and matrix factorization; and text preparation methods such as TF-IDF, stemming, and lemmatization, applied to sentiment classification.

A structured guide for study and practice

With ten chapters arranged to build on earlier material, this first edition is suited to students and industry professionals looking for a practitioner-oriented introduction to machine learning with Python. Its mix of statistical concepts and applied modelling makes it a useful choice for readers who want to understand the steps behind analytical work, from data exploration through model evaluation.