Essential Data Analytics, Data Science, and AI
Apress (Verlag)
979-8-8688-1069-5 (ISBN)
The book starts by introducing data analytics, data science, and artificial intelligence. It illustrates real-world applications, and, it addresses the ethical considerations tied to AI. It also explores ways to gain data for practice and real-world scenarios, including the concept of synthetic data. Next, it uncovers Extract, Transform, Load (ETL) processes and explains how to implement them using Python. Further, it covers artificial intelligence and the pivotal role played by machine learning models. It explains feature engineering, the distinction between algorithms and models, and how to harness their power to make predictions. Moving forward, it discusses how to assess machine learning models after their creation, with insights into various evaluation techniques. It emphasizes the crucial aspects of model deployment, including the pros and cons of on-device versus cloud-based solutions. It concludes with real-world examples and encourages embracing AI while dispelling fears, and fostering an appreciation for the transformative potential of these technologies.
Whether you’re a beginner or an experienced professional, this book offers valuable insights that will expand your horizons in the world of data and AI.
What you will learn:
What are Synthetic data and Telemetry data
How to analyze data using programming languages like Python and Tableau.
What is feature engineering
What are the practical Implications of Artificial Intelligence
Who this book is for:
Data analysts, scientists, and engineers seeking to enhance their skills, explore advanced concepts, and stay up-to-date with ethics. Business leaders and decision-makers across industries are interested in understanding the transformative potential and ethical implications of data analytics and AI in their organizations.
Maxine Attobrah holds a bachelor’s degree in Electrical Engineering from the University of Massachusetts – Amherst. Maxine’s career began as an Electronic Flight Controls Engineer at a leading global security, defense, and aerospace contractor company, where she was responsible for developing and testing control system software to enhance helicopter piloting. Subsequently, Maxine pursued further education, earning master’s degrees in Electrical & Computer Engineering and Engineering & Technology Innovation Management from Carnegie Mellon University. Maxine started her career after graduating at a major global consulting firm as a Data Scientist and has since transitioned to the role of an AI/ML Engineer. Currently, she serves as a Lead AI/ML Engineer at this firm. This book was prepared by the author in her personal capacity. The views and opinions expressed in this book are those of the author and do not necessarily reflect the official policy, opinion, or position of their present or past employers.
Chapter 1: Introduction.- Chapter 2: Obtaining Data.- Chapter 3: ETL Pipeline.- Chapter 4: Exploratory Data Analysis.- Chapter 5: Machine Learning Models.- Chapter 6: Evaluating Models.- Chapter 7: When To Use Machine Learning Models.- Chapter 8: Where Machine Learning Models Live.- Chapter 9: Telemetry.- Chapter 10: Adversaries and Abuse.- Chapter 11: Working With Models.
| Erscheinungsdatum | 24.12.2024 |
|---|---|
| Zusatzinfo | 58 Illustrations, black and white; XX, 211 p. 58 illus. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Artificial Intelligence • data analytics • Data Science • Data Visualizations • Hugging Face • machine learning • ML Flow • Pandas • Python |
| ISBN-13 | 979-8-8688-1069-5 / 9798868810695 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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