Learn Python Programming: The Crash Course to Learn Python Language and its Application for Data Science with Python 3. A Beginner's Guide to Python Machine Learning with Practical Examples

Mark J Branson

ISBN 10: 1710673001 / ISBN 13: 9781710673005

Published by Independently Published, 2019

About this Item

This item is printed on demand. New copy - Usually dispatched within 5-9 working days 308.

Seller Inventory # C9781710673005

Bibliographic Details

• Title: Learn Python Programming: The Crash Course to Learn Python Language and its Application for Data Science with Python 3. A Beginner's Guide to Python Machine Learning with Practical Examples
• Publisher: Independently Published
• Publication Date: 2019
• Language: English
• ISBN 10: 1710673001
• ISBN 13: 9781710673005
• Binding: Paperback / softback
• Condition: New

About this title

Synopsis

If one asks about "Machine Learning”, you will probably imagine a robot or something like the Exterminator. Machine Learning is not only involved in robotics, but also in many other applications. You can also imagine something as if spam filters being one of the first applications in Machine Learning that helps improve the lives of millions of people. In this chapter, you will learn what Machine Learning is, and how it works.

What is Machine Learning?

Machine Learning is the practice of computer programming to learn from data. In the example above, the program can easily determine if the given is important or if it is "spam" (unwanted email). In Machine Learning, the data is known as training sets or examples.

Why Machine Learning?

Let us assume that you would like to write the filter program without using Machine Learning methods. In this case, you will have to follow the following steps:

In the beginning, you would have a look at what the unwanted emails look like. You could select them by the words or phrases they use, such as "Sign up now," "free," and many more, and in addition to patterns that are used in the names of the senders or in the body of the email.

Second, you would write an algorithm to detect the patterns you have seen, and then the software would indicate the emails as "unwanted" if a number of those patterns is found.

Finally, you would try the program, and you would rearrange the first two steps until the results are good enough.

Because the program is not software, they contain a very long list of rules that are difficult to maintain. However, if you develop the same software using Machine Learning, you can maintain it properly.

Additionally, the senders of the emails can change the templates of the emails so that a word like "4You" is now "for you" since their emails have been determined as "unwanted". Programs that use traditional techniques would need to be updated, which means that, if there were other changes, you would need to update your codes repeatedly.

On the other hand, a program that uses these techniques will automatically detect these changes made by users, and begin to indicate them as "unwanted" without the need for you to do it manually.

In addition, we can use the program to solve problems that are very complex software that does not use it. For example, speech recognition, when you say "one" or "two", the program should be able to distinguish the difference. Therefore, for this task, you will have to develop an algorithm that measures the sound.

In the end, Machine Learning will help us learn, and Machine Learning algorithms can help us see what we have learned.

So why are you waiting? BUY NOW this book and clear the way towards machine learning.