Thinking-data-science-a-data-science-practitioners-guide

Preface
Chapter 1 (Data Science Process) introduces you to the data science process that is
followed by a modern data scientist in developing those highly acclaimed AI
applications. It describes both the traditional and modern approach followed by a
current day data scientist in model building. In today’s world, a data scientist has to
deal with not just the numeric data, but he needs to handle even text and image
datasets. The high-frequency datasets are another major challenge for a data scientist.
After this brief on model building, the chapter introduces you to the full data
science process. As we have a very large number of machine learning algorithms,
which can apply to your datasets, the model development process becomes time
consuming and resource intensive. The chapter introduces you to AutoML that eases
this model development process and hyper-parameter tuning for the selected algorithm.
Finally, it introduces you to the modern approach of using deep neural
networks (DNNs) and transfer learning.
Machine learning is based on data, more the data that you have; it makes learning
better. Let us consider a simple example of identifying a person in a photo, video, or
just in real life. If you have a better knowledge or have more features of that person
known to you, the identification becomes a simple task. However, in machine
learning, the machine does not like this. In fact, we consider having many features
a curse of dimensionality. This is mainly due to two reasons—we, human-beings,
cannot visualize data beyond three dimensions and having many dimensions
demands enormous resources and training times. Chapter 2 (Dimensionality Reduction)
teaches you several techniques for bringing down the dimensions of your
dataset to a manageable level. The chapter gives you an exhaustive coverage of
dimensionality reduction techniques followed by a data scientist.
After we prepare the dataset for machine learning, the data scientist’s major task
is to select an appropriate algorithm for the problem that he is trying to solve. The
Classical Algorithms Overview (Part I) gives you an overview of the various
algorithms that you will study in the next few chapters.
The model development task could be of a regression or classification type.
Regression is a well-studied statistical technique and successfully implemented in
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