A firm foundation and common ground for the understanding of concepts and techniques is very important for any journey. Through this chapter on ML fundamentals, we have tried to achieve precisely this. Before getting started with the basics of deep learning, transfer learning, and more advanced concepts, it is imperative that we form a solid foundation of ML concepts. In this chapter, we have covered quite a bit of ground and provided important pointers to study concepts in more details.
We began the chapter by understanding why machine learning is important and how it is a completely different paradigm. We briefly discussed the relationship between AI, ML, and deep learning. The chapter then moved on to present different ML techniques such as supervised, unsupervised, and reinforcement learning. We discussed in detail which different supervised and unsupervised methods are commonly used.
The chapter also included a quick introduction to the CRISP-DM model for ML project workflows along with ML pipelines. We also discussed EDA of the battles dataset from the fantasyland of Game of Thrones to apply different concepts and learn about the importance of EDA. Toward the end of the chapter, feature extraction and engineering and feature selection were introduced.
In the coming chapters, we will build upon these concepts and eventually apply the learning in chapters concerning different real-world use cases. Welcome onboard!
