The Handbook of Data Science and AI

Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring: 

- A comprehensive overview of the various fields of application of data science

- Case studies from practice to make the described concepts tangible

- Practical examples to help you carry out simple data analysis projects

The book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term. 

Contains these current issues:

- Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms.

- Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice

- Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies

- Computer vision: How can we gain insights from images and videos with data science?

- Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios.

- ML and AI in production: How to turn experimentation into a working data science product?

- Presenting your results: Essential presentation techniques for data scientists