About the Author xiii
About the Technical Reviewers xv
Foreword xix
Chapter 1: Introduction 1
Chapter 2: Obtaining Data 7
Chapter 3: ETL Pipeline 27
Chapter 4: Exploratory Data Analysis 47
Chapter 5: Machine Learning Models 75
Chapter 6: Evaluating Models 101
Chapter 7: When to Use Machine Learning Models 123
Chapter 8: Where Machine Learning Models Live 141
Chapter 9: Telemetry 155
Chapter 10: Adversaries and Abuse 167
Chapter 11: Working with Models 181
References 197
Index 201

 In today’s world, understanding data analytics, data science, and artificial intelligence is not just an advantage but a necessity. This book is your thorough guide to learning these innovative fields, designed to make the learning practical and engaging.
 

 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.