Most learning paradigms that are followed in other books or content about ML follow a bottom-up approach. This approach starts from the bottom and works its way up. The approach first covers the theoretical elements, such as mathematical introductions to the algorithm, the evolution of the algorithm, variations, and parameters that the algorithm takes, and then delves into the application of the ML algorithm specific to a dataset. This may be a good approach; however, it takes longer really to see the results produced by the algorithm. It needs a lot of perseverance on the part of the learner to be patient and wait until the practical application of the algorithm is covered. In most cases, practitioners and certain classes of industry professionals working on ML are really interested in the practical aspects and they want to experience the power of the algorithm. For these people, the focus is not the theoretical foundations of the algorithm, but it is the practical application. The bottom-up approach works counterproductively in this case.
The learning paradigm followed in this book to teach several ML algorithms is opposite to the bottom-up approach. It rather follows a very practical top-down approach. The focus of this approach is learning by coding.
Each chapter of the book will focus on learning a particular class of ML algorithm. To start with, the chapter focuses on how to use the algorithm in various situations and how to obtain results from the algorithm in practice. Once the practical application of the algorithm is demonstrated using code and a dataset, gradually, the rest of the chapter unveils the theoretical details/concepts of the algorithms experienced in the chapter thus far. All theoretical details will be ensured to be covered only in as much detail as is required to understand the code and to apply the algorithm on any new datasets. This ensures that we get to learn the focused application areas of the algorithms rather than unwanted theoretical aspects that are of less importance applied in the ML world.
