Book Image

The Python Workshop - Second Edition

By : Corey Wade, Mario Corchero Jiménez, Andrew Bird, Dr. Lau Cher Han, Graham Lee
4.7 (3)
Book Image

The Python Workshop - Second Edition

4.7 (3)
By: Corey Wade, Mario Corchero Jiménez, Andrew Bird, Dr. Lau Cher Han, Graham Lee

Overview of this book

Python is among the most popular programming languages in the world. It’s ideal for beginners because it’s easy to read and write, and for developers, because it’s widely available with a strong support community, extensive documentation, and phenomenal libraries – both built-in and user-contributed. This project-based course has been designed by a team of expert authors to get you up and running with Python. You’ll work though engaging projects that’ll enable you to leverage your newfound Python skills efficiently in technical jobs, personal projects, and job interviews. The book will help you gain an edge in data science, web development, and software development, preparing you to tackle real-world challenges in Python and pursue advanced topics on your own. Throughout the chapters, each component has been explicitly designed to engage and stimulate different parts of the brain so that you can retain and apply what you learn in the practical context with maximum impact. By completing the course from start to finish, you’ll walk away feeling capable of tackling any real-world Python development problem.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
1
Chapter 1: Python Fundamentals – Math, Strings, Conditionals, and Loops
Overview
Introduction
Technical requirements
Python as a calculator
Strings – concatenation, methods, and input()
String interpolation
String indexing and slicing
Slicing
Booleans and conditionals
Loops
Summary
Free Chapter
2
Chapter 2: Python Data Structures
Overview
Introduction
Technical requirements
The power of lists
List methods
Matrix operations
Dictionary keys and values
Dictionary methods
Tuples
A survey of sets
Choosing types
Summary
3
Chapter 3: Executing Python – Programs, Algorithms, and Functions
Overview
Introduction
Technical requirements
Python scripts and modules
Python algorithms
Basic functions
Iterative functions
Recursive functions
Dynamic programming
Helper functions
Variable scope
Lambda functions
Summary
4
Chapter 4: Extending Python, Files, Errors, and Graphs
Overview
Introduction
Technical requirements
Reading files
Writing files
Preparing for debugging (defensive code)
Plotting techniques
The don’ts of plotting graphs
Summary
5
Chapter 5: Constructing Python – Classes and Methods
Overview
Introduction
Technical requirements
Classes and objects
Defining classes
The __init__ method
Methods
Properties
Inheritance
Summary
6
Chapter 6: The Standard Library
Overview
Introduction
Technical requirements
The importance of the Standard Library
Working with dates and times
Interacting with the OS
Using the subprocess module
Logging in Python
Using collections in Python
Using functools
Summary
7
Chapter 7: Becoming Pythonic
Overview
Introduction
Technical requirements
Using list comprehensions
Set and dictionary comprehensions
Using defaultdict to get default values
Creating custom iterators
Leveraging itertools
Lazy evaluations with generators
Using regular expressions
Summary
8
Chapter 8: Software Development
Overview
Introduction
Technical requirements
How to debug
Automated testing
Creating a pip package
Creating documentation the easy way
Source code management
Summary
9
Chapter 9: Practical Python – Advanced Topics
Overview
Introduction
Technical requirements
Developing collaboratively
Dependency management
Deploying code into production
Running code in parallel with multiprocessing
Parsing command-line arguments in scripts
Performance and profiling
Profiling code
Summary
10
Chapter 10: Data Analytics with pandas and NumPy
Overview
Introduction
Technical requirements
NumPy and basic stats
Matrices
The pandas library
Working with big data
Null values
Creating statistical graphs
Summary
11
Chapter 11: Machine Learning
Overview
Introduction
Technical requirements
Introduction to linear regression
Testing data with cross-validation
Regularization – Ridge and Lasso
K-nearest neighbors, decision trees, and random forests
Classification models
Boosting algorithms
Summary
12
Chapter 12: Deep Learning with Python
Overview
Introduction
Technical requirements
Introduction to deep learning
Your first deep learning model
Additional regularization technique – Dropout
Building neural networks for classification
Convolutional neural networks
Summary
13
Chapter 13: The Evolution of Python – Discovering New Python Features
Overview
Introduction
Python Enhancement Proposals
Python 3.7
Python 3.8
Python 3.9
Python 3.10
Python 3.11
Summary
14
Index
Index
Why subscribe?
15
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Convolutional neural networks

Although deep learning performs well on tabular regression and classification datasets, deep learning has a bigger advantage when making predictions from unstructured data such as images or text.

When it comes to classifying images, deep learning shines by analyzing data not one-dimensionally, but two-dimensionally, using convolutional neural networks, or CNNs for short.

Convolutional neural networks are among the strongest machine learning algorithms in the world today for classifying images. In this section, you will learn the basic theory behind convolutions before building your own CNN.

MNIST

MNIST is the name of a famous dataset of handwritten digits from 1998 that has been widely used in computer vision. The dataset consists of 60K training images and 10K test images.

Google Colab includes a smaller sample of 20K training images, along with the 10K test images, that may be directly accessed in a Colab notebook and prepared for machine...

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