Machine Learning in Practice with Python

By: R. Cole, Nathan

Learn machine learning with Python through practical examples, real datasets, and complete workflows.

Machine learning becomes easier when you understand how the entire process works-from preparing data and exploring patterns to training models, evaluating results, improving performance, and building complete projects.

Machine Learning in Practice with Python is a practical guide designed to help you develop a strong foundation in machine learning while working with Python and widely used machine-learning tools.

Inside this book, you will learn how to:

Understand the foundations of artificial intelligence and machine learningUse Python for practical machine-learning workflowsWork with NumPy and Pandas for data preparation and analysisClean, transform, and prepare datasets for machine learningExplore datasets using statistical analysis and visualizationBuild regression models for numerical predictionBuild classification models for binary and multiclass problemsWork with linear regression, logistic regression, K-nearest neighbors, decision trees, random forests, and boosting methodsApply unsupervised learning techniques such as K-Means and hierarchical clusteringUse dimensionality reduction with Principal Component Analysis (PCA)Perform feature engineering to improve machine-learning modelsApply encoding, scaling, transformations, and feature selectionUnderstand and prevent data leakageEvaluate models using appropriate performance metricsUse accuracy, precision, recall, F1 score, MAE, MSE, RMSE, R , ROC analysis, and precision-recall analysisApply cross-validation and hyperparameter tuningCompare different machine-learning modelsAnalyze model errors and understand model limitationsSave trained models and create reproducible workflowsBuild practical machine-learning projects using PythonWork through complete workflows from problem definition and data preparation to model evaluation

The book focuses on practical machine-learning skills rather than memorizing algorithms. Concepts are introduced step by step and connected to Python code, datasets, model outputs, visualizations, evaluation techniques, and practical project workflows.

Whether you are beginning your journey into machine learning, strengthening your Python skills, studying data science, or looking for a practical reference for classical machine-learning projects, this book provides a structured path from fundamentals to real-world implementation.

Learn the concepts. Work with data. Train models. Evaluate results. Improve your workflow. Build practical machine-learning projects with Python.

• Format: Paperback
• Language: English
• ISBN: B0HJQPJHJJ
• ISBN13: 9798174186217
• Release Date: September 2026
• Publisher: Independently Published
• Length: 494 Pages
• Weight: 1.44 lbs.
• Dimensions: 1.0" x 6.0" x 9.0"