Ozdemir Principles of Data Science

A beginner's guide to essential math and coding skills for data fluency and machine learning
3. Auflage 2024
ISBN: 978-1-83763-600-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 326 Seiten

No detailed description available for "Principles of Data Science".

Autoren/Hrsg.

Ozdemir, Sinan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Table of Contents - Data Science Terminology
- Types of Data
- The Five Steps of Data Science
- Basic Mathematics
- Impossible or Improbable – A Gentle Introduction to Probability
- Advanced Probability
- What are the Chances? An Introduction to Statistics
- Advanced Statistics
- Communicating Data
- How to Tell if Your Toaster is Learning – Machine Learning Essentials
- Predictions Don't Grow on Trees, or Do They?
- Introduction to Transfer Learning and Pre-trained Models
- Mitigating Algorithmic Bias and Tackling Model and Data Drift
- AI Governance
- Navigating Real-World Data Science Case Studies in Action

Über Autor(innen)

Ozdemir Sinan:
Sinan is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including "Quick Start Guide to LLMs". Sinan is currently the founder of LoopGenius which uses AI to help people and businesses boost their sales and was previously the founder of the acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a Master's Degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco.