This quote may seem exaggerated to the core and difficult to digest, yet, with the pace at which technology and science are improving, who knows? We as a species have always dreamt of creating intelligent, self-aware machines. With recent advancements in research, technology, and the democratization of computing power, artificial intelligence (AI), machine learning (ML), and deep learning have gotten enormous attention and hype amongst technologists and the population in general. Though Hollywood's promised future is debatable, we have started to see and use glimpses of intelligent systems in our daily lives. From intelligent conversational engines, such as Google Now, Siri, Alexa, and Cortana, to self-driving cars, we are gradually accepting such smart technologies in our daily routines.
As we step into the new era of learning machines, it is important to understand that the fundamental ideas and concepts have existed for some time and have constantly been improved upon by intelligent people across the planet. It is well known that 90% of the world's data has been created in just the last couple of years, and we continue to create far more data at ever increasing rates. The realm of ML, deep learning, and AI helps us utilize these massive amounts of data to solve various real-world problems.
This book is divided into three sections. In this first section, we will get started with the basic concepts and terminologies associated with AI, ML, and deep learning, followed by in-depth details on deep learning architectures.
This chapter provides our readers with a quick primer on the basic concepts of ML before we get started with deep learning in subsequent chapters. This chapter covers the following aspects:
- Introduction to ML
- ML methodologies
- CRISP-DM—workflow for ML projects
- ML pipelines
- Exploratory data analysis
- Feature extraction and engineering
- Feature selection
Every chapter of the book builds upon concepts and techniques from the previous chapters. Readers who are well-versed with the basics of ML and deep learning may pick and choose the topics as they deem necessary, yet it is advised to go through the chapters sequentially. The code for this chapter is available for quick reference in the Chapter 1 folder in the GitHub repository at https://github.com/dipanjanS/hands-on-transfer-learning-with-python which you can refer to as needed to follow along with the chapter.
