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Python for Geeks

You're reading from   Python for Geeks Build production-ready applications using advanced Python concepts and industry best practices

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801070119
Length 546 pages
Edition 1st Edition
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Author (1):
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Muhammad Asif Muhammad Asif
Author Profile Icon Muhammad Asif
Muhammad Asif
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Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Python, beyond the Basics
2. Chapter 1: Optimal Python Development Life Cycle FREE CHAPTER 3. Chapter 2: Using Modularization to Handle Complex Projects 4. Chapter 3: Advanced Object-Oriented Python Programming 5. Section 2: Advanced Programming Concepts
6. Chapter 4: Python Libraries for Advanced Programming 7. Chapter 5: Testing and Automation with Python 8. Chapter 6: Advanced Tips and Tricks in Python 9. Section 3: Scaling beyond a Single Thread
10. Chapter 7: Multiprocessing, Multithreading, and Asynchronous Programming 11. Chapter 8: Scaling out Python Using Clusters 12. Chapter 9: Python Programming for the Cloud 13. Section 4: Using Python for Web, Cloud, and Network Use Cases
14. Chapter 10: Using Python for Web Development and REST API 15. Chapter 11: Using Python for Microservices Development 16. Chapter 12: Building Serverless Functions using Python 17. Chapter 13: Python and Machine Learning 18. Chapter 14: Using Python for Network Automation 19. Other Books You May Enjoy

Building and evaluating a machine learning model

Before we start writing a Python program, we will evaluate the process of building a machine learning model.

Learning about an ML model building process

We discussed the different components of machine learning in the Introducing machine learning section. The machine learning process uses those elements as input to train a model. This process follows a procedure with three main phases, and each phase has several steps in it. These phases are shown here:

Figure 13.2 – Steps of building an ML model using a classic learning approach

Each phase, along with detailed steps of it, is described here:

  • Data analysis: In this phase, we collect raw data and transform it into a form that can be analyzed and then used to train and test a model. We may discard some data, such as records with empty values. Through data analysis, we try to select the features (attributes) that can be used to identify patterns...
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