Python Machine Learning Practice (64 results)

Condition: As New. Unread book in perfect condition. Vaidya, Aniruddh (illustrator).

Condition: New. Vaidya, Aniruddh (illustrator).

Condition: New. Vaidya, Aniruddh (illustrator).

Condition: As New. Unread book in perfect condition. Vaidya, Aniruddh (illustrator).

Condition: New. Vaidya, Aniruddh (illustrator).

Taschenbuch. Condition: Neu. Neuware - 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 learning- Use Python for practical machine-learning workflows- Work with NumPy and Pandas for data preparation and analysis- Clean, transform, and prepare datasets for machine learning- Explore datasets using statistical analysis and visualization- Build regression models for numerical prediction- Build classification models for binary and multiclass problems- Work with linear regression, logistic regression, K-nearest neighbors, decision trees, random forests, and boosting methods- Apply unsupervised learning techniques such as K-Means and hierarchical clustering- Use dimensionality reduction with Principal Component Analysis (PCA)- Perform feature engineering to improve machine-learning models- Apply encoding, scaling, transformations, and feature selection- Understand and prevent data leakage- Evaluate models using appropriate performance metrics- Use accuracy, precision, recall, F1 score, MAE, MSE, RMSE, R , ROC analysis, and precision-recall analysis- Apply cross-validation and hyperparameter tuning- Compare different machine-learning models- Analyze model errors and understand model limitations- Save trained models and create reproducible workflows- Build practical machine-learning projects using Python- Work through complete workflows from problem definition and data preparation to model evaluationThe 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.…

Taschenbuch. Condition: Neu. Neuware.

Hardcover. Condition: Brand New. 2nd edition. 520 pages. 9.44x6.61x9.69 inches. In Stock.

Condition: New. Vaidya, Aniruddh (illustrator).

Condition: New.

Condition: New. Second Edition 2025 NO-PA16APR2015-KAP.

Paperback. Condition: new. 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 multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides an introduction to machine learning, security and cloud computing, from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AIand ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.…

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Paperback. Condition: new. Paperback. 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, R2, 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 evaluationThe 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

paperback. Condition: New. Language:Chinese.Paperback.Pub Date:2023-03 Pages:400 Publisher:People's Posts and Telecommunications Press This book features zero-based explanations. uses examples to guide readers to learn. and introduces Python machine learning related knowledge and practical skills in an easy-to-understand way. The book has 17 chapters. divided into 5 parts. Part I is an introduction to machine learning. including Chapter 1. which mainly introduces the concept of machine learning. the main tasks of machi.…

Taschenbuch. Condition: Neu. Introduction to Machine Learning with Security | Theory and Practice Using Python in the Cloud | Pramod Gupta (u. a.) | Taschenbuch | Synthesis Lectures on Engineering, Science, and Technology | xxviii | Englisch | 2025 | Springer | EAN 9783031591716 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

paperback. Condition: New. Paperback. Pub Date: 2023-06 Pages: 164 Publisher: Machinery Industry Press This book provides the basic principles of developing machine learning applications using Python. The main contents include: machine learning concepts and their applications; data preprocessing. dimensionality reduction; various linear models. ensemble learning methods. random forests. boosting methods; natural language processing. deep learning. etc. This book follows up on the latest research results in machine lear. …

paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2023-09 Pages: 576 Publisher: Tsinghua University Press Python Machine Learning Practice: Neural Network Solutions Based on Scikit-learn and PyTorch is based on the author's years of accumulation. Through concepts and their explanations. Python code examples and their explanations and code output. this advanced guide to machine learning is carefully designed for readers with zero foundation. The book contains 3 parts and 16 chapters. After introducing the.…

paperback. Condition: New. Paperback. Pub Date: 2024-04 Pages: 253 Language: Chinese Publisher: Petroleum Industry Press This book comprehensively explains the solution strategy of small samples of artificial intelligence from three dimensions: data. model and algorithm. It fills the gap of no relevant books on the market. The book is illustrated and equipped with all program codes and data sets. This book can be used as a reference book for college students and artificial intelligence researchers.…

paperback. Condition: New. Paperback. Pub Date: 2025-03 Pages: 356 Publisher: Tsinghua University Press This book introduces the relevant algorithmic principles of machine learning in detail with numerous examples and applies them to the Python language. The content covers the complete knowledge system of machine learning and the fundamentals of deep learning. such as multilayer perceptrons and convolutional neural networks. In addition to the project-based exercises. each chapter provides numerous problems with parame.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: Hervorragend. Zustand: Hervorragend | Seiten: 520 | Sprache: Englisch | Produktart: Bücher | This book provides an introduction to machine learning and cloud computing, both from conceptual and practical levels, along with their usage with a Public Cloud infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and security considerations, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and several case studies.In addition, this book: Provides broad coverage of AI, Machine Learning and Cloud Computing; Uses real examples and case studies to demonstrate key topics; Demonstrates concepts, as well as practical usage.   &nbsp.…

paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2023-03 Pages: 367 Publisher: Tsinghua University Press Python Machine Learning Project Practice leads everyone to master key machine learning concepts in the process of building actual projects! Using machine learning. we can complete tasks such as customer behavior analysis. price trend prediction. risk assessment. etc. To master machine learning. you need high-quality examples. clear explanations. and a lot of exercises. This book fully meets these thr.…

paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2023-12 Publisher: Beijing Institute of Technology Press Python Machine Learning: Core Technology and Development Practice (Original Book 2nd Edition) is a practical guide for a wide range of Python programmers. which comprehensively and in-depth introduces key concepts and technologies in the field of artificial intelligence. From basic concepts. algorithms to practical applications. it covers multiple topics such as recommendation systems. feature engin.…

paperback. Condition: New. Paperback. Pub Date: 2022-01-01 Pages: 456 Publisher: Mechanical Industry Press This book is a problem-solving guide for creating real-world intelligent systems. Provides a comprehensive approach that includes concepts. practices. practical examples. and code examples to teach readers the important skills needed to understand and solve different problems in machine learning. By introducing real-world case studies from the Python machine learning ecosystem. it teaches the machine learning tech.…

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