Machine Learning in Practice with Python (Paperback)

Paperback. Machine learning becomes much easier when you understand the complete process behind a trustworthy result.Machine Learning in Practice with Python gives beginners a structured path from raw data and a clearly defined problem to an evaluated, interpreted, and reusable predictive solution.Rather than overwhelming you with advanced mathematics or an endless collection of algorithms, this book focuses on the habits that matter in real projects. You will learn how to inspect information before using it, prepare features correctly, protect evaluation data, establish meaningful baselines, compare different approaches fairly, diagnose weak results, and improve your workflow using evidence instead of guesswork.Inside, you will learn how to: Set up a clean Python environment for reproducible experimentsWork confidently with NumPy, pandas, Matplotlib, JupyterLab, and scikit learnLoad, inspect, filter, sort, group, and transform tabular datasetsIdentify missing values, duplicate records, suspicious entries, and potential outliersSeparate identifiers from useful predictive variablesPrepare numerical and categorical information correctlyCreate reliable train and test splitsRecognize and prevent data leakageEncode categorical variables and scale numerical featuresEstablish simple baselines before adding complexityBuild and assess regression solutionsApply linear and regularized regression methodsBuild binary and multiclass classifiersWork with logistic regression and k nearest neighborsUnderstand confusion matrices, accuracy, precision, recall, and F1 scoreBuild decision trees, random forests, and gradient boosting solutionsCompare linear and tree based approachesDiscover groups using K Means clusteringApply principal component analysis for dimensionality reductionUse cross validation for more reliable comparisonsPerform grid search and randomized search efficientlyCreate reproducible preprocessing pipelinesEngineer more informative features from domain knowledgeHandle imbalanced classes with class weights and threshold adjustmentInvestigate variable importance and prediction errorsExplain results without confusing correlation with causationSave complete fitted pipelines for later useValidate incoming records before predictionRecord assumptions, limitations, dependencies, and metadataUnderstand basic monitoring and serialization security concernsThe final capstone combines the entire process into one complete project. You will define the objective, inspect and clean the dataset, create the preprocessing pipeline, establish a baseline, compare candidate approaches, use cross validation, tune the strongest candidate, examine errors, interpret results, test against protected data, save the finished pipeline, and create a reusable prediction workflow.This book is designed for new Python learners, students, analysts, career switchers, developers, and working professionals who want a dependable introduction to applied predictive analytics. Advanced programming experience is not required, and neither calculus nor advanced linear algebra is necessary.If you have seen tutorials that show you how to call fit() but leave you uncertain about what happens before and after that step, this book provides the missing structure.Learn how to move from raw information to evidence, build results you can defend, and develop a repeatable workflow you can apply to your own projects. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…