**Big Data Technologies** is a comprehensive guide that explores the vast landscape of big data, covering its fundamental concepts, tools, and applications. The book delves into data storage, processing frameworks, and analytics techniques, providing insights into technologies such as Hadoop, Spark, NoSQL databases, and cloud-based solutions. It also discusses real-world applications across industries, including healthcare, finance, and e-commerce. Designed for professionals, researchers, and students, this book offers a practical understanding of how big data technologies drive...

*Machine Learning Basics: Building Intelligent Systems* is a foundational guide to understanding the principles and practices of machine learning. It covers core concepts such as supervised and unsupervised learning, data preprocessing, model selection, and evaluation. The book emphasizes practical applications, offering step-by-step explanations for implementing algorithms like decision trees, neural networks, and clustering methods. With a focus on problem-solving, it bridges theoretical knowledge and real-world use cases, making it an accessible resource for beginners and a solid refresher...

**Data Science Essentials: A Practical Guide** is a comprehensive resource designed for beginners and professionals seeking to build a strong foundation in data science. Covering key concepts such as data preprocessing, exploratory data analysis, machine learning, and model evaluation, the book provides step-by-step explanations with real-world examples. It emphasizes practical applications using Python and popular libraries like Pandas, NumPy, and Scikit-Learn. With a focus on hands-on learning, this guide helps readers develop the skills needed to analyze data effectively and make...

**Data Science Essentials: Ein praktischer Leitfaden** ist eine umfassende Ressource für Anfänger und Profis, die eine solide Grundlage in der Datenwissenschaft schaffen wollen. Das Buch deckt Schlüsselkonzepte wie Datenvorverarbeitung, explorative Datenanalyse, maschinelles Lernen und Modellevaluierung ab und bietet schrittweise Erklärungen mit Beispielen aus der Praxis. Der Schwerpunkt liegt auf praktischen Anwendungen mit Python und beliebten Bibliotheken wie Pandas, NumPy und Scikit-Learn. Mit dem Schwerpunkt auf praktischem Lernen hilft dieses Handbuch den Lesern, die Fähigkeiten zu...

**Data Science Essentials : Un guide pratique** est une ressource complète conçue pour les débutants et les professionnels qui cherchent à établir des bases solides en science des données. Couvrant des concepts clés tels que le prétraitement des données, l'analyse exploratoire des données, l'apprentissage automatique et l'évaluation des modèles, le livre fournit des explications étape par étape avec des exemples du monde réel. Il met l'accent sur les applications pratiques en utilisant Python et des bibliothèques populaires comme Pandas, NumPy et Scikit-Learn. En mettant...