Bücher von Sinan Ozdemir

This book bridges the gap between mathematics and computer science to show you how to gain actionable insights from your data. You'll explore the entire data science pipeline while learning effective data mining techniques and the fundamentals of computational mathematics and statistics to create powerful data visualizations.

Transform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your dataKey Features:Learn practical data science combined with data theory to gain maximum insights from dataDiscover methods for deploying actionable machine learning pipelines while mitigating biases in data and modelsExplore actionable case studies to put your new skills to use immediatelyPurchase of the print or Kindle book includes a free PDF eBookBook Description:Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.Starting with cleaning and preparation, you'll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you'll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You'll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you'll explore medium-level data governance, including data provenance, privacy, and deletion request handling.By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.What You Will Learn:Master the fundamentals steps of data science through practical examplesBridge the gap between math and programming using advanced statistics and MLHarness probability, calculus, and models for effective data controlExplore transformative modern ML with large language modelsEvaluate ML success with impactful metrics and MLOpsCreate compelling visuals that convey actionable insightsQuantify and mitigate biases in data and ML modelsWho this book is for:If you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you'll find this book useful. Familiarity with Python programming will further enhance your learning experience.

Der Schnellstart in die praktische Arbeit mit LLMs Das Buch bietet einen Überblick über zentrale Konzepte und Techniken von LLMs wie z.B. ChatGPT und zeigt das Potenzial von Open-Source- und Closed-Source-Modellen Es erläutert, wie Large Language Models funktionieren und wie sie für Aufgaben des Natural Language Processing (NLP) genutzt werden Auch für interessierte Nicht-Data-Scientists mit Python-Kenntnissen verständlich Themen z.B.: die ChatGPT-API, Prompt-Engineering, Chatbot-Personas, Cloud-Bereitstellung; deckt auch GPT-4 ab Large Language Models (LLMs) wie ChatGPT zeigen erstaunliche Fähigkeiten, aber ihre Größe und Komplexität halten viele Praktiker*innen davon ab, sie in ihren eigenen Anwendungen einzusetzen. In dieser Einführung räumt Data Scientist und KI-Unternehmer Sinan Ozdemir diese Hindernisse aus dem Weg und bietet einen Leitfaden für den Einsatz von LLMs zur Lösung praktischer NLP-Probleme. Sinan Ozdemir hat alles zusammengestellt, was Sie für den Einstieg brauchen, auch wenn Sie noch keine Erfahrung mit LLMs haben: Schritt-für-Schritt-Anleitungen, Best Practices, Fallstudien aus der Praxis, Übungsaufgaben und vieles mehr. Gleichzeitig bietet er Einblicke in die Funktionsweise von LLMs, um Sie bei der Auswahl von Modellen, Datenformaten und Parametern zu unterstützen. Auf der begleitenden Website des Autors finden Sie weitere Ressourcen, darunter Beispieldatensätze und Code für die Arbeit mit Open-Source- und Closed-Source-LLMs.

The book will allow readers to implement smart solutions to their existing cybersecurity products and effectively build intelligent solutions which cater to the needs of the future. By the end of this book, you will be able to build, apply, and evaluate machine learning algorithms to identify various cybersecurity potential threats.

Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective.