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 andterminology that is closely related to machine learning.
- We will then give a brief project structure of machine learning that can beused to understand the process that goes on with a data science project.
- Then the book describes in detail about regularization and how to fit amodel into the data.
- In the next chapter, we will deal with gradient descent and optimizationwith python implementation.
- We will then learn about feature engineering, data preprocessing methods, cross-validation, and hyperparameter tuning in detail with python codeimplementation.
- The last section of the first part deals with machine learning algorithmsand their implementation in detail.
- The second part starts with a brief introduction to neural networks andneurons
- The next two chapters will help us understand the complexity andimportance of neural networks. We will also build a neural network usingpython in this chapter.
- The last chapter deals with huge data sets like webpages. We will introducepage ranking algorithm and its simplicity.
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