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Python for Geeks

Python for Geeks

By : Asif
4.5 (20)
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Python for Geeks

Python for Geeks

4.5 (20)
By: Asif

Overview of this book

Python is a multipurpose language that can be used for multiple use cases. Python for Geeks will teach you how to advance in your career with the help of expert tips and tricks. You'll start by exploring the different ways of using Python optimally, both from the design and implementation point of view. Next, you'll understand the life cycle of a large-scale Python project. As you advance, you'll focus on different ways of creating an elegant design by modularizing a Python project and learn best practices and design patterns for using Python. You'll also discover how to scale out Python beyond a single thread and how to implement multiprocessing and multithreading in Python. In addition to this, you'll understand how you can not only use Python to deploy on a single machine but also use clusters in private as well as in public cloud computing environments. You'll then explore data processing techniques, focus on reusable, scalable data pipelines, and learn how to use these advanced techniques for network automation, serverless functions, and machine learning. Finally, you'll focus on strategizing web development design using the techniques and best practices covered in the book. By the end of this Python book, you'll be able to do some serious Python programming for large-scale complex projects.
Table of Contents (20 chapters)
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1
Section 1: Python, beyond the Basics
5
Section 2: Advanced Programming Concepts
9
Section 3: Scaling beyond a Single Thread
13
Section 4: Using Python for Web, Cloud, and Network Use Cases

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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