This book explicitly gives the reader layman’s introduction to machine learning with implementation in python libraries particularly using scikit learn and Tensor flow. We will learn about machine learning and its subset deep learning in detail along with program codes that will give a good overview for the developers. We will also discuss in detail about different machine learning algorithms like support vector machine, Linear regression method in detail with python examples. In the second part of the book, we will deal with neural networks and implement them using Tensor Flow. This book is easily understood and deals with complex concepts explained in a simple way such that beginners can understand it easily. Here we describe the most important topics explained in the book in no particular order:

–  A brief introduction to machine learning with a small known history and

terminology that is closely related to machine learning.

–  We will then give a brief project structure of machine learning that can be

used to understand the process that goes on with a data science project.

–  Then the book describes in detail about regularization and how to fit a

model into the data.

 –  In the next chapter, we will deal with gradient descent and optimization

with python implementation.

–  We will then learn about feature engineering, data preprocessing methods,

cross-validation, and hyperparameter tuning in detail with python code

implementation.

–  The last section of the first part deals with machine learning algorithms

and their implementation in detail.

–  The second part starts with a brief introduction to neural networks and

neurons

–  The next two chapters will help us understand the complexity and

importance of neural networks. We will also build a neural network using

python in this chapter.

–  The last chapter deals with huge data sets like webpages. We will introduce

page ranking algorithm and its simplicity.

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