Name: The Beginner's Guide to Data Science and Machine Learning
SKU: 69135901
Price: 31.32 EUR
Availability: InStock
Author: Ethan M Anderson
ISBN: 9798194253722

portada The Beginner's Guide to Data Science and Machine Learning

The Beginner's Guide to Data Science and Machine Learning

Type

Physical Book

Publisher

Amazon Digital Services LLC - Kdp

Author

Ethan M Anderson

Language

English

Pages

294

ISBN13

9798194253722

The Beginner's Guide to Data Science and Machine Learning

Ethan M Anderson (Author) · Amazon Digital Services LLC - Kdp · Physical Book

New Book Imported to Netherlands

Delivery: 12 Nov - 19 Nov

€ 31,32

Import costs and 9% BTW included in the price ✅

€ 31,32

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Synopsis "The Beginner's Guide to Data Science and Machine Learning"

DATA SCIENCE AND MACHINE LEARNING FOR BEGINNERS is a practical, beginner-friendly guide designed to help readers build real data science skills from the ground up.Starting with Python fundamentals, this book takes you step by step through data collection, cleaning, analysis, visualization, statistics, machine learning, model evaluation, feature engineering, and practical projects.Inside, you will learn how to: Understand the foundations of data science and machine learningUse Python for practical data analysisWork with Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learnClean, organize, transform, and visualize datasetsUnderstand statistics and probability for data scienceBuild regression and classification modelsExplore clustering and dimensionality reductionTrain, evaluate, and improve machine learning modelsEngineer useful features and avoid common modeling mistakesBuild complete machine learning projectsUnderstand neural networks and deep learningExplore bias, fairness, privacy, and responsible AIBuild a portfolio and communicate data-driven insights with confidenceWhether you are completely new to data science or looking to strengthen your existing Python and machine learning knowledge, this book provides a structured path from fundamental concepts to practical application.Learn the concepts. Practice the techniques. Build real projects. Turn data into meaningful insights.