Enterprise Automation with Python: Automate Excel, Web, Documents, Emails, and Various Workloads with Easy-to-code Python Scripts (English Edition)

 

500% improvement in productivity by automating tedious chores

Key Features

● Contains numerous automation projects, including excel sheets, word documents, pdf files, websites, and messenger.

● Provides a strong understanding of Automation, its fundamentals, and strategies to implement it across organizations.

● Nurtures various techniques to improvise operational efficiency around repetitive business processes.

Description

Written by ZappyAI's founder, this book gives real-world examples of how readers can implement automation in their businesses and business-as-usual tasks. Through a series of real-world examples and live demonstrations, this book shows how to automate various tasks using Python scripts.

This book gives solutions to everyday automation needs and repetitive tasks at work every day. Readers will be able to discover the most typical business process that can be automated and write simple Python scripts to turn them automated. This book will teach you how to create, read, change, and extract data from Excel documents using Python programming. Readers can extract data from websites, PDF documents, Gmail, Outlook, and WhatsApp chats. Text extraction from photos and scanned documents is also smartly accomplished in this book.

The final section will examine techniques for extending your Python scripting skills and constructing complicated end-to-end process automation. Throughout the book, readers will be utterly captivated by how to automate their tedious tasks and enhance their organisations' productivity by 500 percent.

What you will learn

● Learn to write automation scripts by learning how to use different Python scripts.

● Find out how to look at the business process to automate it.

● Create, read, edit, and extract data from spreadsheets, documents, and PDFs.

● Control mouse and keyboard activities, as well as automate several desktop apps.

● Examine strategies for automating the downloading and extraction of data from the Internet and websites.

● Organize the real-time transfer and reading of Gmail, Outlook, and WhatsApp messages.

Who this book is for

Software Engineers, Business Analysts, Automation Engineers, QA Engineers, and anyone who wants to simplify and automate their tedious work with simple yet powerful Python scripts. Readers with little or no technical experience can also use the strategies discussed in this book to automate day-to-day tasks.

Table of Contents

1. Setting up the automation environment

2. Fundamentals of Python

3. Automation mindset – Python as a tool for automation

4. Automating Excel-based tasks

5. Automating Web-based tasks

6. Automating File-based tasks

7. Automating Email, Messenger Applications and Messages

8. GUI - Keyboard and Mouse Automation

9. Image based Automations

10. Creating Time and Event-based Automations

11. Writing Complex Automations

Hard Copy: Enterprise Automation with Python: Automate Excel, Web, Documents, Emails, and Various Workloads with Easy-to-code Python Scripts (English Edition)

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Mastering Python Network Automation: Automating Container Orchestration, Configuration, and Networking with Terraform, Calico, HAProxy, and Istio

 

Numerous sample programs & examples demonstrating the application of python tools to streamline network automation

With "Mastering Python Network Automation," you can streamline container orchestration, configuration management, and resilient networking with Python and its libraries, allowing you to emerge as a skilled network engineer or a strong DevOps professional.

From the ground up, this guide walks readers through setting up a network automation lab using the NS3 network simulator and Python programming. This includes the installation of NS3, as well as python libraries like nornir, paramiko, netmiko, and PyEZ, as well as the configuration of ports, hosts, and servers. This book will teach you the skills to become a proficient automation developer who can test and fix any bugs in automation scripts. This book examines the emergence of the service mesh as a solution to the problems associated with service-to-service communication over time.

This book walks you through automating various container-related tasks in Python and its libraries, including container orchestration, service discovery, load balancing, container storage management, container performance monitoring, and rolling updates. Calico and Istio are two well-known service mesh tools, and you'll find out how to set them up and configure them to manage traffic routing, security, and monitoring. Additional topics covered in this book include the automation of network policies, the routing of workloads, and the collection and tracking of metrics, logs, and traces. You'll also pick up some tips and tricks for collecting and visualizing Istio metrics with the help of tools like Grafana.

Key Learnings

Use of Istio for cluster traffic management, traffic routing, and service mesh

implementation.

Utilizing Cilium and Calico to solve pod networking and automate network policy

and workload routing.

Monitoring and managing Kubernetes clusters with etcd and HAProxy load

balancers and container storage.

Establishing network automation lab with tools like NS3 emulator, Python, Virtual

Environment, and VS Code.

Establishing connectivity between hosts, port connectivity, SSH connectivity,

python libraries, NS3, and network encryption.

Table of Content

Python Essentials for Networks

File Handling and Modules in Python

Preparing Network Automation Lab

Configuring Libraries and Lab Components

Code, Test & Validate Network Automation

Automation of Configuration Management

Managing Docker and Container Networks

Orchestrating Container & Workloads

Pod Networking

Implementing Service Mesh

Audience

"Mastering Python Network Automation" is an essential guide for network engineers, DevOps professionals, and developers who want to streamline container orchestration and resilient networking with the help of Terraform, Calico, and Istio. Knowing Python and the basics of networking is sufficient to pursue this book.

Hard Copy: Mastering Python Network Automation: Automating Container Orchestration, Configuration, and Networking with Terraform, Calico, HAProxy, and Istio

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Python Programming for Beginners: The Complete Python Coding Crash Course - Boost Your Growth with an Innovative Ultra-Fast Learning Framework and Exclusive Hands-On Interactive Exercises & Projects

 

Ready to Dive into Python and Supercharge Your Growth & Career?
Interested in Python, but afraid it might be too challenging?
Thinking about taking the programming leap but don't know where to start?
Are you a pro in another language but want to add Python to your toolkit?

You are in the right place!

You no longer have to waste your time and attention learning Python from lengthy books, expensive online courses, or very complex Python tutorials. This guide offers you an incredible and unmissable opportunity!

Smart and Fast Learning

The best topics, lined up in the best order, helping you learn quickly and smartly. No information overload, just what you need to know.

Say Goodbye to Confusion

With bite-sized chunks of knowledge and easy step-by-step guidance you'll go from "What's Python?" to "Wow, I'm actually coding!" in no time.

Hands-on Powerful and Fun Exercises and Projects

Packed with selected exercises and projects to help you master what you've learned. You'll get to test your skills in real-world situations right away, like creating a weather dashboard, a simple chatbot, or a GUI application!

A comprehensive book for beginners and a game changer with:

Everyday Language: ditching techy jargon and speaking your language

Safety Nets: Stuck on a code? We've got troubleshooting tips and common fixes right where you need them.

Aha-moments guaranteed to get your curiosity back!

Here is a tiny fraction of what you'll discover:

- What is Python

- How to install python and what is the best distribution

- What are data types and variables

- How to work with numbers in Python

- What operators there are in Python and when to use them

- How to manipulate Strings

- How to implement Program Flow Controls

- How to implement loops in Python

- What are Python lists, tuples, sets, dictionaries, and how to use them

- How to create modules and functions

- How to program according to the Object-Oriented paradigm

- How to create classes

- Version Control & Github

- What are and how to use Inheritance, Polymorphism, Abstraction and Encapsulation

- Exclusive projects and plenty of exercises

And much much more!

This isn't just a book; it's a personal growth and career booster! Learning Python opens doors to new jobs, freelancing, and even building your own apps or games. The possibilities are endless! Whether you're a complete beginner, or a programming wizard looking to add another feather to your cap, this guide has something for everyone. Take the leap, learn from the PROS and outsmart the competition.

Ready to Jump In? Your path to becoming a Python pro starts here. Don't Wait any longer! Click 'Buy Now' to Get Started!

👉 With this book, you will also gain access to 4 additional exclusive resources, providing you with an extra edge:

✅ Mindset Blueprint for Developers

✅ Essential Coding Interview Questions

✅ Mastering Productivity for Programmers

✅ Python Cheat Sheet

Hard Copy: Python Programming for Beginners: The Complete Python Coding Crash Course - Boost Your Growth with an Innovative Ultra-Fast Learning Framework and Exclusive Hands-On Interactive Exercises & Projects

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Python QuickStart Guide: The Simplified Beginner's Guide to Python Programming Using Hands-On Projects and Real-World Applications (QuickStart Guides™ - Technology)

 

Learn Python fundamentals that can be used in any programming setting – use the guidance in this book to program your own game in a unique and practical Python learning experience.

#1 New Release and #1 Best-Seller!

Learning Python opens the door to a world of programming possibilities.

From AI and machine learning to video game, app, and web development, Python is a critical behind-the-scenes component of everyday technology.

Python powers the services of household names like Google, Netflix, and Spotify along with tech pioneers like NASA, IBM, and Intel. Put simply, Python is the in-demand and easy-to-learn programming language that gets stuff done.

In Python QuickStart Guide, senior developer and programmer Robert Oliver lays out the quickest and most accessible path yet to the mastery of Python fundamentals.

Distilling his experience drawn from over two decades of working with Python and other programming languages, Robert’s clear voice and writing present a practical, hands-on approach that anyone, at any experience level, can use to become a Python programmer.

It doesn’t matter if you are a new or existing programmer, a job seeker looking for a career change or promotion, or just someone who wants to learn how to automate basic tasks with Python—Robert’s step-by-step approach, complete with a hands-on companion Python video game project, is the perfect starting point to master Python fundamentals!

Python QuickStart Guide is Perfect for:

New or experienced programmers looking to enhance their career opportunities with an in-demand programming language

Job seekers who want to supercharge their resumes and increase their value in the job marketplace

Students or recent college grads who have their sights set on a lucrative position in the tech industry

Full stack developers or programmers who need to round out their programming skills to take on new projects

Coding or programming bootcamp students looking for supplemental learning material

Anyone who wants to explore the world of programming, use Python to automate tedious tasks, or enhance their resume and future-proof their skills!

Python QuickStart Guide Explains

The best practical approach to learning Python—follow along with the exercises in the book to program your own video game and learn along the way

How to master Python building blocks and build a robust set of programming skills at your own pace

How to avoid common pitfalls new programmers face, how to debug code, and how to eliminate frustrating errors

Coding best practices that anyone can use to level up their programming skills using Python or any other programming language

You Will Learn

How to Use Python – Practical Examples, Code Snippets, Plus Follow Along to Code Your Own Game!

Python Fundamentals – How to Use Python for Web Design and Interfacing with GitHub, SQL, and Other Applications

Object-Oriented Programming Principles – Managing Data, Scripts, Logic, Inputs, Outputs, and More!

Programming Essentials – Debugging, Producing Clean Code, Best Practices, Time-Savers, and Tips

Python Next Steps –Testing, Optimization, Speed Improvements, Integrations with Other Applications, and More!

*Lifetime Access To Free Python Programming Digital Assets*

In addition to the follow-along Python game included with the exercises in the book, Python QuickStart Guide comes with a library of references and cheat sheets to help you go beyond the book and get the most out of your Python learning experience.

QuickStart Guides are books for beginners, written by experts.

Hard Copy: Python QuickStart Guide: The Simplified Beginner's Guide to Python Programming Using Hands-On Projects and Real-World Applications (QuickStart Guides™ - Technology)

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Python Coding challenge - Day 122 | What is the output of the following Python Code?

 

Code: 

age = 5

if True:

    age = 6

print(age)

Solution and Explanation: 

age = 5: This line initializes a variable named age and assigns it the value 5.

if True:: This line begins an if statement with the condition True. Since True is always true, the code block following this line will always be executed.

Inside the if block:

age = 6

This line updates the value of the age variable to 6. This happens because the code inside the if block is executed when the condition is true.

print(age): This line prints the current value of the age variable. Since the if block was executed, and the value of age was set to 6 within that block, the output of this print statement will be 6.

So, the output of the code will be:

6

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5 ways to swap two numbers in Python

 

# 5 ways to swap two numbers in Python

# 1. Using a Temporary Variable:

a = 5

b = 10

temp = a

a = b

b = temp

print("After swapping: a =", a, ", b =", b)

#clcoding.com

# 2. Without Using a Temporary Variable :

a = 5

b = 10

a = a + b

b = a - b

a = a - b

print("After swapping: a =", a, ", b =", b)

#clcoding.com

# 3. Using Tuple Unpacking:

a = 5

b = 10

a, b = b, a

print("After swapping: a =", a, ", b =", b)

#clcoding.com

# 4. Using XOR bitwise operation:

a = 5

b = 10

a = a ^ b

b = a ^ b

a = a ^ b

print("After swapping: a =", a, ", b =", b)

#clcoding.com

# 5. Using Arithmetic Operators in a Single Line:

a = 5

b = 10

a, b = b, a + b - a

print("After swapping: a =", a, ", b =", b)

#clcoding.com

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Python Coding challenge - Day 121 | What is the output of the following Python Code?

 

The provided code defines a function called calc that takes a variable number of arguments using the *args syntax. The function calculates the product of the number of arguments and the last argument in the given sequence. Let's break down the code:

def calc(*args):

    count = len(args)

    elem = args[count - 1]

    return count * elem

print(calc(2, 2, 1, 3))

Here's how the function works:

*args in the function definition allows the function to accept any number of arguments. All the arguments passed to the function are collected into a tuple named args.

count is assigned the length of the args tuple, which gives the number of arguments passed to the function.

elem is assigned the value of the last element in the args tuple (i.e., args[count - 1]).

The function returns the product of count and elem.

The print(calc(2, 2, 1, 3)) statement calls the calc function with the arguments 2, 2, 1, 3.

Now, let's substitute the values:

count is 4 (number of arguments).

elem is 3 (the last argument).

The function returns count * elem, which is 4 * 3 = 12.

Therefore, the final result printed by print(calc(2, 2, 1, 3)) is 12.

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Python for Data Analysis

 

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.

Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.

Use the Jupyter notebook and IPython shell for exploratory computing

Learn basic and advanced features in NumPy

Get started with data analysis tools in the pandas library

Use flexible tools to load, clean, transform, merge, and reshape data

Create informative visualizations with matplotlib

Apply the pandas groupby facility to slice, dice, and summarize datasets

Analyze and manipulate regular and irregular time series data

Learn how to solve real-world data analysis problems with thorough, detailed examples

Hard Copy: Python for Data Analysis

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The Python Bible for Beginners: A Step-By-Step Guide to Master Coding from Scratch in Less Than 7 Days and Become the Expert that Top Companies Vie to Hire

 

Are you ready to conquer any coding challenge, become an invaluable asset in the tech industry, and finally land your highly paid dream job with ease?

Then keep reading and dive into the fascinating world of Python programming with the only comprehensive step-by-step guide designed to transform complete beginners into highly skilled programmers in less than 7 days.

The Python Bible for Beginners is more than just a programming book; it's a roadmap to success in the ever-evolving field of software development. Nicholas Kimmel, senior data analyst for one of the biggest American tech companies, has condensed everything you need in a simple and clear way, leaving no room for confusion or frustration. His book stands apart as a true gem in the world of programming: as you journey through the chapters, you'll seamlessly transition from basic concepts to mastering advanced libraries and features.

Here’s a tiny fraction of what you'll find:

Foundational Skills: Start from scratch, learning Python syntax, basic functions, and how to think like an expert programmer.

Step-by-Step Tutorials: Follow clear and complete explanations (with code snippets), where complex concepts are broken down into easy-to-understand language.

Advanced Concepts and Libraries: Delve into hot topics like data analysis, machine learning, and cutting-edge visual and computational methods using Python's vast array of powerful libraries.

Expert Tips and Best Practices: Discover industry secrets and best practices that will set you apart in the job market.

Real-World Examples and Exercises: Reinforce learning and build practical skills with exercises for every level that will test your preparation and help you fix the key concepts in your memory through application.

------

Who Should Read This Book?

Students or Aspiring Programmers: No prior experience? No problem. This book is tailored for absolute beginners.

Current Developers: Looking to switch to Python or sharpen your existing skills? This book will elevate your coding abilities.

Career Advancers: Stand out in your workplace or job market by mastering one of the most sought-after skills in the tech industry. 

Hard Copy: The Python Bible for Beginners: A Step-By-Step Guide to Master Coding from Scratch in Less Than 7 Days and Become the Expert that Top Companies Vie to Hire

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Deep Learning with Python: A Comprehensive guide to Building and Training Deep Neural Networks using Python and popular Deep Learning Frameworks

 

Deep Learning with Python is a comprehensive guide to building and training deep neural networks using Python and popular deep learning frameworks. Whether you are a beginner or an experienced data scientist, this book provides a detailed understanding of the theory and practical implementation of deep learning.

Starting with an introduction to deep learning, the book covers essential topics such as neural network architecture, training and optimization, regularization, and transfer learning. It also covers popular deep learning frameworks such as TensorFlow, Keras, and PyTorch.

The book includes practical examples and step-by-step instructions to help you build and train deep neural networks for a variety of applications, including image and speech recognition, natural language processing, and time series analysis. You will also learn how to use advanced techniques such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.

With its comprehensive coverage of deep learning and practical examples, this book is an essential resource for anyone interested in building and training deep neural networks using Python and popular deep learning frameworks.

Hard Copy: Deep Learning with Python: A Comprehensive guide to Building and Training Deep Neural Networks using Python and popular Deep Learning Frameworks

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Python Programming and SQL: 5 books in 1 - The #1 Coding Course from Beginner to Advanced. Learn it Well & Fast (2024) (Computer Programming)

 

Supercharge Your Career with Python programming and SQL: The #1 Coding Course from Beginner to Advanced (2024)

Are you looking to turbocharge your career prospects? Do you want to gain the skills that are in high demand in today's job market?

Whether you're a complete beginner or an experienced programmer, this #1 bestseller book is designed to make your learning journey simple, regardless of your current skills. It aims to guide you seamlessly through the content and fast-track your career in no time.

This 5-in-1 guide covers both Python and SQL fundamental and advanced concepts, ensuring that you not only gain a comprehensive understanding but also stand out among your peers and stay ahead of the competition:

✅ Step-by-Step Instructions: This easy-to-understand guide provides step-by-step instructions, making it effortless to grasp Python and SQL fundamentals.

✅ Fast Learning Curve: Progress rapidly from beginner to advanced levels with our carefully crafted curriculum. Gain confidence to tackle coding challenges.

✅ Boost Your Career: Acquire sought-after skills desired by employers, making you stand out in the job market. Get job-ready and attractive to potential employers.

✅ Competitive Edge: Stand out among peers with our cutting-edge course covering fundamentals and advanced concepts. Your coding proficiency will make you invaluable to any organization.

✅ Versatile Job Opportunities: Python and SQL open doors in tech, data analysis, web development, and more. Stay ahead of the competition.

✅ Start Writing Your Own Programs: Empower yourself to create efficient code, unleash creativity, and achieve peak performance.

✅ Real-World Projects: Gain practical experience through hands-on projects, showcasing your coding expertise effectively.

✅ Expert Guidance: Acquire practical skills and knowledge from expert guidance to become a proficient programmer.

Here are just a few things you'll learn in Python programming and SQL:

Get started with Python programming, covering variables, functions, loops, and conditionals

Discover how to work with data in Python, including data types, structures, and manipulation techniques

Learn different data structures, such as sequences, tuples, lists, matrices, and dictionaries

Understand conditional statements and their role in decision making

Discover object-oriented programming (OOP) and learn how to define classes and methods.

Discover the art of exception handling, ensuring robust and error-free code

Explore the power of algorithms, information processing and master the essential features of algorithms

Master file processing in Python, including opening, reading, writing, and appending files, etc.

Master SQL essentials such as basics of SQL, data types, statements, and clauses

Work with databases using SQL, including creating, modifying, and deleting tables and records.

Learn powerful queries: Perform joins, unions, ordering, grouping, and utilize aliases for advanced SQL queries

Explore efficient data management: Navigate MySQL, work with databases, tables, and views

Advanced techniques: explore stored procedures, indexing, truncating, and working with triggers

Master data optimization: Fine-tune SQL queries for optimal performance and efficiency

Gain practical skills and techniques that you can directly apply in you career

And much, much more...

Whether you are a novice or an experienced programmer, this guide aims to make it simple for you to begin your journey and fast-track your career in no time.

Hard Copy: Python Programming and SQL: 5 books in 1 - The #1 Coding Course from Beginner to Advanced. Learn it Well & Fast (2024) (Computer Programming)

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Ultimate Step by Step Guide to ChatGPT Using Python: 90 Day Plan to Make Passive Income with Generative AI (Ultimate Step by Step Guide to Machine Learning Book 4)

 

Unlock the Future of AI!

Delve into the world of Generative AI with Daneyal Anis' groundbreaking book, "The Ultimate Step by Step Guide to ChatGPT Using Python". If you've ever been intrigued by how machine learning, data science, and artificial intelligence can be harnessed for tangible results, this guide is your key.

In today's digital age, the fields of Artificial Intelligence (AI), Machine Learning (ML), and Data Science are not just buzzwords; they are the foundational pillars that drive innovations across industries. From big tech giants to emerging startups, AI-powered solutions are the backbone of breakthroughs.

Here's what you'll discover within this comprehensive guide:

How the union of Python, the most popular language in data science, and GPT is revolutionizing the tech space.

Deep dives into the power and potential of GPT - learning its strengths, nuances, and applications.

Strategies for monetizing your AI and ML skills, unveiling the golden opportunities that await in the AI space.

Building robust AI portfolios and utilizing automation tools for efficiency and scalability.

Crafting AI profiles, including creating dynamic chatbots using ChatGPT.

Navigating the ethical considerations and responsibilities in the AI domain.

Beyond just the knowledge, this guide is crafted to action. That's why Daneyal also offers an exclusive 90-Day Plan to make passive income using Generative AI, leading you from the theoretical to practical monetization of your skills. Plus, get exclusive access to an in-depth Step by Step Course for those wanting a hands-on learning experience.

Editorial Reviews

The Digital Era is here, and AI is at its forefront. Equip yourself with the knowledge, tools, and strategies to not only participate in this revolution but also to thrive and lead. With "The Ultimate Step by Step Guide to ChatGPT Using Python", your transformative journey in the realm of AI is set on a promising path.

Hard Copy: Ultimate Step by Step Guide to ChatGPT Using Python: 90 Day Plan to Make Passive Income with Generative AI (Ultimate Step by Step Guide to Machine Learning Book 4)

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Deep Learning with Python, Second Edition

Unlock the groundbreaking advances of deep learning with this extensively revised edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.

In Deep Learning with Python, Second Edition you will learn:

    Deep learning from first principles

    Image classification & image segmentation

    Timeseries forecasting

    Text classification and machine translation

    Text generation, neural style transfer, and image generation

Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach, even if you have no background in mathematics or data science. 

About the book

Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp illustrations, and clear examples. You’ll pick up the skills to start developing deep-learning applications.

What's inside

    Deep learning from first principles

    Image classification and image segmentation

    Time series forecasting

    Text classification and machine translation

    Text generation, neural style transfer, and image generation

About the reader

For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.

About the author

François Chollet is a software engineer at Google and creator of the Keras deep-learning library.

Table of Contents

1  What is deep learning?

2 The mathematical building blocks of neural networks

3 Introduction to Keras and TensorFlow

4 Getting started with neural networks: Classification and regression

5 Fundamentals of machine learning

6 The universal workflow of machine learning

7 Working with Keras: A deep dive

8 Introduction to deep learning for computer vision

9 Advanced deep learning for computer vision

10 Deep learning for timeseries

11 Deep learning for text

12 Generative deep learning

13 Best practices for the real world

14 Conclusions

Hard Copy: Deep Learning with Python, Second Edition

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Python Coding challenge - Day 120 | What is the output of the following Python Code?

 

Code :

def sum(num):

    if num == 1:

        return 1

    return num + sum(num - 1)

print(sum(5))

Solution and Explanation:

Here's how the function works:

The function sum takes a parameter num.
The base case is defined with if num == 1:. If num is 1, the function returns 1.
If the base case is not met, the function returns num + sum(num - 1). This is the recursive step, where the sum of the current num and the sum of the numbers from 1 to num - 1 is calculated.
The print(sum(5)) statement calls the sum function with the argument 5 and prints the result.
Let's trace the function call for sum(5):

sum(5) returns 5 + sum(4)
sum(4) returns 4 + sum(3)
sum(3) returns 3 + sum(2)
sum(2) returns 2 + sum(1)
sum(1) returns 1 (base case)
Now we substitute these values back:

sum(2) returns 2 + 1 = 3
sum(3) returns 3 + 3 = 6
sum(4) returns 4 + 6 = 10
sum(5) returns 5 + 10 = 15
So, the final result printed by print(sum(5)) is 15.

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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

 

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more

Discover modern causal inference techniques for average and heterogenous treatment effect estimation

Explore and leverage traditional and modern causal discovery methods

Book Description

Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.

You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code.

Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms.

The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.

What you will learn

Master the fundamental concepts of causal inference

Decipher the mysteries of structural causal models

Unleash the power of the 4-step causal inference process in Python

Explore advanced uplift modeling techniques

Unlock the secrets of modern causal discovery using Python

Use causal inference for social impact and community benefit

Who this book is for

This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.

Table of Contents

Causality – Hey, We Have Machine Learning, So Why Even Bother?

Judea Pearl and the Ladder of Causation

Regression, Observations, and Interventions

Graphical Models

Forks, Chains, and Immoralities

Nodes, Edges, and Statistical (In)dependence

The Four-Step Process of Causal Inference

Causal Models – Assumptions and Challenges

Causal Inference and Machine Learning – from Matching to Meta-Learners

Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More

Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond

Can I Have a Causal Graph, Please?

Causal Discovery and Machine Learning – from Assumptions to Applications

Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond

Epilogue

Hard Copy: Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

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Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

 

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

Learn applied machine learning with a solid foundation in theory

Clear, intuitive explanations take you deep into the theory and practice of Python machine learning

Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices

Book Description

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.

Why PyTorch?

PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.

You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).

This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

Explore frameworks, models, and techniques for machines to 'learn' from data

Use scikit-learn for machine learning and PyTorch for deep learning

Train machine learning classifiers on images, text, and more

Build and train neural networks, transformers, and boosting algorithms

Discover best practices for evaluating and tuning models

Predict continuous target outcomes using regression analysis

Dig deeper into textual and social media data using sentiment analysis

Who this book is for

If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.

Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.

Table of Contents

Giving Computers the Ability to Learn from Data

Training Simple Machine Learning Algorithms for Classification

A Tour of Machine Learning Classifiers Using Scikit-Learn

Building Good Training Datasets – Data Preprocessing

Compressing Data via Dimensionality Reduction

Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Combining Different Models for Ensemble Learning

Applying Machine Learning to Sentiment Analysis

Predicting Continuous Target Variables with Regression Analysis

Working with Unlabeled Data – Clustering Analysis

Hard Copy : Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition 3rd Edition

 

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Purchase of the print or Kindle book includes a free eBook in the PDF format.

Key Features

Third edition of the bestselling, widely acclaimed Python machine learning book

Clear and intuitive explanations take you deep into the theory and practice of Python machine learning

Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices

Book Description

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

Master the frameworks, models, and techniques that enable machines to 'learn' from data

Use scikit-learn for machine learning and TensorFlow for deep learning

Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more

Build and train neural networks, GANs, and other models

Discover best practices for evaluating and tuning models

Predict continuous target outcomes using regression analysis

Dig deeper into textual and social media data using sentiment analysis

Who this book is for

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

Table of Contents

Giving Computers the Ability to Learn from Data

Training Simple Machine Learning Algorithms for Classification

A Tour of Machine Learning Classifiers Using scikit-learn

Building Good Training Datasets – Data Preprocessing

Compressing Data via Dimensionality Reduction

Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Combining Different Models for Ensemble Learning

Applying Machine Learning to Sentiment Analysis

Embedding a Machine Learning Model into a Web Application

Predicting Continuous Target Variables with Regression Analysis

Working with Unlabeled Data – Clustering Analysis

Implementing a Multilayer Artificial Neural Network from Scratch

Parallelizing Neural Network Training with TensorFlow

Hard Copy: Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition 3rd Edition

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Python Coding challenge - Day 119 | What is the output of the following Python Code?

 

Code: 

value = 7 and 8

result = "Even" if value % 2 == 0 else "Odd"

print(result)

Solution and Explanation: 

The variable value is assigned the result of the logical AND operation between 7 and 8. In Python, the and operator returns the last true value or the first false value. In this case, since both 7 and 8 are considered true in a boolean context, value will be assigned the last true value, which is 8.

Then, the code checks if value % 2 == 0 (i.e., if value is even) and assigns "Even" to result if true, otherwise "Odd". Since 8 is even, the output of the code will be:

Even

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Data Analysis Foundations with Python: Master Python and Data Analysis using NumPy, Pandas, Matplotlib, and Seaborn: A Hands-On Guide with Projects and Case Studies.

 

Unlock the Full Potential of Data Analysis with Python—All in One Comprehensive Guide!

Are you an aspiring data scientist or analyst with a passion for exploring the vast possibilities of Python-based data analysis? If so, you're in luck because "Data Analysis Foundations with Python" is the perfect guide for you.

This comprehensive and immersive book will not only provide you with a hands-on approach but also offer a detailed exploration of the fascinating world of Python-based data analysis. Whether you're a beginner or an experienced professional, this book will take you on a journey that will deepen your understanding and expand your skills in the field.

✅ Include a Free Repository Code with all code blocks used in this book.

✅ This free resource allows you to copy and paste the book code for easy manipulation.

✅ Free premium customer support.

From Basics to Mastery: A Structured Learning Journey

This book is not just a mere compilation of Python codes and data sets. It goes beyond that, offering a comprehensive course that will guide you from being a Python beginner to becoming a highly skilled Data Analyst.

Throughout this course, you will not only acquire essential Python skills, but also gain practical experience in data manipulation techniques and learn about the latest advancements in machine learning. With its well-structured content and engaging learning activities, this book ensures that your journey towards becoming a proficient Data Analyst is both seamless and enjoyable.

Three Exceptional Projects and Two In-Depth Case Studies

Project 1: Analyzing Customer Reviews: Learn how to extract, clean, and make sense of textual data from online customer reviews.

Project 2: Predicting House Prices: Delve into the fascinating world of supervised learning, where you'll get to apply complex machine learning models to predict property prices.

Project 3: Building a Recommender System: Uncover the secrets of unsupervised learning as you build and deploy a fully functioning recommender system.

Case Studies for Real-world Insight

Case Study 1: Sales Data Analysis: Unearth the power of Python to transform raw sales data into actionable insights.

Case Study 2: Social Media Sentiment Analysis: Venture into the realm of Natural Language Processing and learn how to analyze public sentiment from social media data.

Additional Features

Practical Exercises: Each chapter concludes with practical exercises, designed to test your understanding and apply what you’ve learned in real-world scenarios.

Best Practices and Tips: The final section of the book is devoted to best practices in the field, including code organization and how to continue learning and growing in your data analysis journey.

Who This Book Is For

Whether you're a student who is eager to expand your knowledge, a professional who is seeking to embark on a new career path, or an experienced analyst who is looking to enhance your skills and stay ahead in the industry—this comprehensive book is specifically tailored to meet your needs and provide valuable insights and guidance.

What Are You Waiting For?

Embark on a transformative journey to unlock Python's potential for data analysis. Gain a deep understanding of Python's capabilities and learn how to extract insights from complex datasets using libraries and tools. Develop skills through real-world case studies and hands-on exercises to confidently tackle analytical challenges.

This book equips you with technical knowledge, practical skills, and a growth mindset for continuous learning. Don't miss this opportunity to become a proficient Python data analyst. Get your copy now for unlimited possibilities in data analysis.

Hard Copy: Data Analysis Foundations with Python: Master Python and Data Analysis using NumPy, Pandas, Matplotlib, and Seaborn: A Hands-On Guide with Projects and Case Studies.

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Python for Data Analysts and Scientists: Jump start your career in Data Analysis and Data Science Field

 

This is an excellent book for those who want to Jumpstart their career in Data Analytics and Data Scientist field.

My interest in learning Python script faced a challenging question - “Where shall I start from?”. I browsed through numerous online videos and training materials but with little success. After I agreed to pay a reasonable amount, a training course from a well-known e-learning platform gave me introductory knowledge on Python script. Learning the basic Python commands is one thing, whereas applying them to real life problems is another. For many months, the question - “Which Python commands are important in solving real-life problems?” bothered me a lot. It took me several sleepless nights, and a frantic lookout for a concise list of Python commands from an ocean of online information. My hands-on experiences designing Machine Learning models, performing root cause analysis by statistical hypothesis, and providing consultation as a Data Scientist, helped me learn the most crucial Python commands. The birth of this book is from the thoughts of my struggle in mastering and applying the Python script for resolving numerous challenging tasks. This book concisely lists the essential commands, the data visualization technics, and the statistical knowledge. I have mindfully placed the contents of this book for the day-to-day activities of a Data Analyst and a Data Scientist. This book aims to provide a quick starting platform for those who want to dive into the vast field of Machine Learning and Data Analytics. Further, this book will be a quick reference for those already in this field. With the hope of helping beginners and practitioners, and with a silent prayer of goodwill, I walk you through the simple steps to the proficiency in Python. Let us dive in and enjoy the journey into the world of Python.

Hard Copy: Python for Data Analysts and Scientists: Jump start your career in Data Analysis and Data Science Field

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Python 3: The Comprehensive Guide to Hands-On Python Programming Paperback – September 26, 2022

 

2023 IBPA Benjamin Franklin Award Gold Winner: Professional and Technical Category

Ready to master Python? Learn to write effective code with this award-winning comprehensive guide, whether you’re a beginner or a professional programmer. Review core Python concepts, including functions, modularization, and object orientation and walk through the available data types. Then dive into more advanced topics, such as using Django and working with GUIs. With plenty of code examples throughout, this hands-on reference guide has everything you need to become proficient in Python!

The complete Python 3 handbook

Learn basic Python principles and work with functions, methods, data types, and more

Walk through GUIs, network programming, debugging, optimization, and other advanced topics

Consult and download practical code examples

Coding with Python

Learn about Python syntax and structure. Follow examples to start developing and testing your own programs using downloadable code.

The Standard Library

Explore Python’s built-in library and see how it can be used for a variety of different tasks, from running your mathematical functions to debugging your code.

Advanced Programming Techniques

Already know the basics? Enhance your professional skills with more advanced concepts, including GUIs, Django, scientific computing, and connecting to other languages.

Hard Copy: Python 3: The Comprehensive Guide to Hands-On Python Programming Paperback – September 26, 2022

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Mastering Python Networking: Utilize Python packages and frameworks for network automation, monitoring, cloud, and management, 4th Edition

 

Get to grips with the latest container examples, Python 3 features, GitLab DevOps, network data analysis, and cloud networking to get the most out of Python for network engineering with the latest edition of this bestselling guide

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

Explore the power of the latest Python libraries and frameworks to tackle common and complex network problems efficiently and effectively

Use Python and other open source tools for Network DevOps, automation, management, and monitoring

Use Python 3 to implement advanced network-related features

Book Description

Networks in your infrastructure set the foundation for how your application can be deployed, maintained, and serviced. Python is the ideal language for network engineers to explore tools that were previously available to systems engineers and application developers. In Mastering Python Networking, Fourth edition, you'll embark on a Python-based journey to transition from a traditional network engineer to a network developer ready for the next generation of networks.

This new edition is completely revised and updated to work with the latest Python features and DevOps frameworks. In addition to new chapters on introducing Docker containers and Python 3 Async IO for network engineers, each chapter is updated with the latest libraries with working examples to ensure compatibility and understanding of the concepts.

Starting with a basic overview of Python, the book teaches you how it can interact with both legacy and API-enabled network devices. You will learn to leverage high-level Python packages and frameworks to perform network automation tasks, monitoring, management, and enhanced network security, followed by AWS and Azure cloud networking. You will use Git for code management, GitLab for continuous integration, and Python-based testing tools to verify your network.

What you will learn

Use Python to interact with network devices

Understand Docker as a tool that you can use for the development and deployment

Use Python and various other tools to obtain information from the network

Learn how to use ELK for network data analysis

Utilize Flask and construct high-level API to interact with in-house applications

Discover the new AsyncIO feature and its concepts in Python 3

Explore test-driven development concepts and use PyTest to drive code test coverage

Understand how GitLab can be used with DevOps practices in networking

Who this book is for

Mastering Python Networking, Fourth edition is for network engineers, developers, and SREs who want to learn Python for network automation, programmability, monitoring, cloud, and data analysis. Network engineers who want to transition from manual to automation-based networks using the latest DevOps tools will also get a lot of useful information from this book.

Basic familiarity with Python programming and networking-related concepts such as Transmission Control Protocol/Internet Protocol (TCP/IP) will be helpful in getting the most out of this book.

Table of Contents

Review of TCP/IP Protocol Suite and Python

Low-Level Network Device Interactions

APIs and Intent-Driven Networking

The Python Automation Framework – Ansible

Docker Containers for Network Engineers

Network Security with Python

Network Monitoring with Python - Part 1

Network Monitoring with Python - Part 2

Building Network Web Services with Python

Introduction to AsyncIO

AWS Cloud Networking

Azure Cloud Networking

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Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis 1st Edition

 

Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas

Key Features

Use powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial data

Explore unique recipes for financial data analysis and processing with Python

Estimate popular financial models such as CAPM and GARCH using a problem-solution approach

Book Description

Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries.

In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks.

By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach.

What you will learn

Download and preprocess financial data from different sources

Backtest the performance of automatic trading strategies in a real-world setting

Estimate financial econometrics models in Python and interpret their results

Use Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessment

Improve the performance of financial models with the latest Python libraries

Apply machine learning and deep learning techniques to solve different financial problems

Understand the different approaches used to model financial time series data

Who this book is for

This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.

Table of Contents

Financial Data and Preprocessing

Technical Analysis in Python

Time Series Modelling

Multi-factor Models

Modeling Volatility with GARCH class models

Monte Carlo Simulations in Finance

Asset Allocation in Python

Identifying Credit Default with Machine Learning

Advanced Machine Learning Models in Finance

Deep Learning in Finance

Hard Copy: Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis 1st Edition

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Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights

 

Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricks

Key Features

Get well-versed with various data cleaning techniques to reveal key insights

Manipulate data of different complexities to shape them into the right form as per your business needs

Clean, monitor, and validate large data volumes to diagnose problems before moving on to data analysis

Book Description

Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data.

By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it.

What you will learn

Find out how to read and analyze data from a variety of sources

Produce summaries of the attributes of data frames, columns, and rows

Filter data and select columns of interest that satisfy given criteria

Address messy data issues, including working with dates and missing values

Improve your productivity in Python pandas by using method chaining

Use visualizations to gain additional insights and identify potential data issues

Enhance your ability to learn what is going on in your data

Build user-defined functions and classes to automate data cleaning

Who this book is for

This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book.

Table of Contents

Anticipating Data Cleaning Issues when Importing Tabular Data into pandas

Anticipating Data Cleaning Issues when Importing HTML and JSON into Pandas

Taking the Measure of Your Data

Identifying Issues in Subsets of Data

Using Visualizations for Exploratory Data Analysis

Cleaning and Wrangling Data with Pandas Data Series Operations

Fixing Messy Data When Aggregating

Addressing Data Issues When Combining Data Frames

Tidying and Reshaping Data

User Defined Functions and Classes to Automate Data Cleaning

Hard Copy: Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights

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Python Basics: A Practical Introduction to Python 3

 

Make the Leap From Beginner to Intermediate in Python…

Python Basics: A Practical Introduction to Python 3

Your Complete Python Curriculum—With Exercises, Interactive Quizzes, and Sample Projects

What should you learn about Python in the beginning to get a strong foundation? With Python Basics, you’ll not only cover the core concepts you really need to know, but you’ll also learn them in the most efficient order with the help of practical exercises and interactive quizzes. You’ll know enough to be dangerous with Python, fast!

Who Should Read This Book

If you’re new to Python, you’ll get a practical, step-by-step roadmap on developing your foundational skills. You’ll be introduced to each concept and language feature in a logical order. Every step in this curriculum is explained and illustrated with short, clear code samples. Our goal with this book is to educate, not to impress or intimidate.

If you’re familiar with some basic programming concepts, you’ll get a clear and well-tested introduction to Python. This is a practical introduction to Python that jumps right into the meat and potatoes without sacrificing substance. If you have prior experience with languages like VBA, PowerShell, R, Perl, C, C++, C#, Java, or Swift the numerous exercises within each chapter will fast-track your progress.

If you’re a seasoned developer, you’ll get a Python 3 crash course that brings you up to speed with modern Python programming. Mix and match the chapters that interest you the most and use the interactive quizzes and review exercises to check your learning progress as you go along.

If you’re a self-starter completely new to coding, you’ll get practical and motivating examples. You’ll begin by installing Python and setting up a coding environment on your computer from scratch, and then continue from there. We’ll get you coding right away so that you become competent and knowledgeable enough to solve real-world problems, fast. Develop a passion for programming by solving interesting problems with Python every day!

If you’re looking to break into a coding or data-science career, you’ll pick up the practical foundations with this book. We won’t just dump a boat load of theoretical information on you so you can “sink or swim”—instead you’ll learn from hands-on, practical examples one step at a time. Each concept is broken down for you so you’ll always know what you can do with it in practical terms.

If you’re interested in teaching others “how to Python,” this will be your guidebook. If you’re looking to stoke the coding flame in your coworkers, kids, or relatives—use our material to teach them. All the sequencing has been done for you so you’ll always know what to cover next and how to explain it.

What Python Developers Say About The Book:

“Go forth and learn this amazing language using this great book.” — Michael Kennedy, Talk Python

“The wording is casual, easy to understand, and makes the information flow well.” — Thomas Wong, Pythonista

“I floundered for a long time trying to teach myself. I slogged through dozens of incomplete online tutorials. I snoozed through hours of boring screencasts. I gave up on countless crufty books from big-time publishers. And then I found Real Python. The easy-to-follow, step-by-step instructions break the big concepts down into bite-sized chunks written in plain English. The authors never forget their audience and are consistently thorough and detailed in their explanations. I’m up and running now, but I constantly refer to the material for guidance.” — Jared Nielsen, Pythonista

Hard Copy : Python Basics: A Practical Introduction to Python 3

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Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

 

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python

Key Features

Get up and running with artificial intelligence in no time using hands-on problem-solving recipes

Explore popular Python libraries and tools to build AI solutions for images, text, sounds, and images

Implement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much more

Book Description

Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research.

Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems.

By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.

What you will learn

Implement data preprocessing steps and optimize model hyperparameters

Delve into representational learning with adversarial autoencoders

Use active learning, recommenders, knowledge embedding, and SAT solvers

Get to grips with probabilistic modeling with TensorFlow probability

Run object detection, text-to-speech conversion, and text and music generation

Apply swarm algorithms, multi-agent systems, and graph networks

Go from proof of concept to production by deploying models as microservices

Understand how to use modern AI in practice

Who this book is for

This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.

Table of Contents

Getting Started with Artificial Intelligence in Python

Advanced Topics in Supervised Machine Learning

Patterns, Outliers, and Recommendations

Probabilistic Modeling

Heuristic Search Techniques and Logical Inference

Deep Reinforcement Learning

Advanced Image Applications

Working with Moving Images

Deep Learning in Audio and Speech

Natural Language Processing

Artificial Intelligence in Production

Hard Copy: Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

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Distance Measures in Data Science with Algorithms

Distance Measures in data science with algorithms

1. Euclidean Distance:

import numpy as np

def euclidean_distance(p1, p2):

    return np.sqrt(np.sum((p1 - p2) ** 2))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Euclidean distance:", euclidean_distance(point1, point2))

#clcoding.com

Euclidean distance: 2.8284271247461903

2. Manhattan Distance:

import numpy as np

def manhattan_distance(p1, p2):

    return np.sum(np.abs(p1 - p2))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Manhattan distance:", manhattan_distance(point1, point2))

#clcoding.com

Manhattan distance: 4

3. Cosine Similarity:

from scipy.spatial import distance

def cosine_similarity(p1, p2):

    return 1 - distance.cosine(p1, p2)

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Cosine similarity:", cosine_similarity(point1, point2))

#clcoding.com

Cosine similarity: 0.9838699100999074

4. Minkowski Distance:

import numpy as np

def minkowski_distance(p1, p2, r):

    return np.power(np.sum(np.power(np.abs(p1 - p2), r)), 1/r)

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Minkowski distance:", minkowski_distance(point1, point2, 3))

#clcoding.com

Minkowski distance: 2.5198420997897464

5. Chebyshev Distance:

import numpy as np

def chebyshev_distance(p1, p2):

    return np.max(np.abs(p1 - p2))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Chebyshev distance:", chebyshev_distance(point1, point2))

#clcoding.com

Chebyshev distance: 2

6. Hamming Distance:

import jellyfish

def hamming_distance(s1, s2):

    return jellyfish.hamming_distance(s1, s2)

# Example usage

string1 = "hello"

string2 = "hallo"

print("Hamming distance:", hamming_distance(string1, string2))

#clcoding.com

Hamming distance: 1

7. Jaccard Similarity:

def jaccard_similarity(s1, s2):

    set1 = set(s1)

    set2 = set(s2)

    intersection = set1.intersection(set2)

    union = set1.union(set2)

    return len(intersection) / len(union)

# Example usage

string1 = "hello"

string2 = "hallo"

print("Jaccard similarity:", jaccard_similarity(string1, string2))

#clcoding.com

Jaccard similarity: 0.6

8. Sørensen-Dice Index:

def sorensen_dice_index(s1, s2):

    set1 = set(s1)

    set2 = set(s2)

    intersection = set1.intersection(set2)

    return (2 * len(intersection)) / (len(set1) + len(set2))

# Example usage

string1 = "hello"

string2 = "hallo"

print("Sørensen-Dice index:", sorensen_dice_index(string1, string2))

#clcoding.com

Sørensen-Dice index: 0.75

9. Haversine Distance:

def haversine_distance(lat1, lon1, lat2, lon2):

    R = 6371.0  # Radius of the earth in km

    dLat = np.deg2rad(lat2 - lat1)

    dLon = np.deg2rad(lon2 - lon1)

    a = np.sin(dLat / 2)**2 + np.cos(np.deg2rad(lat1)) * np.cos(np.deg2rad(lat2)) * np.sin(dLon / 2)**2

    c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))

    return R * c

# Example usage

print("Haversine distance:", haversine_distance(51.5074, 0.1278, 40.7128, -74.0060))

#clcoding.com

  Input In [14]

    a = np.sin(dLat / 2)**2 + np.cos(np.deg2rad(lat1)) *

                                                         ^

SyntaxError: invalid syntax

10. Mahalanobis Distance:

from scipy.spatial.distance import cdist

def mahalanobis_distance(X, Y):

    return cdist(X.reshape(1,-1), Y.reshape(1,-1), 'mahalanobis', VI=np.cov(X))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Mahalanobis distance:", mahalanobis_distance(point1, point2))

#clcoding.com

Mahalanobis distance: [[1.41421356]]

11. Pearson Correlation:

from scipy.stats import pearsonr

def pearson_correlation(X, Y):

    return pearsonr(X, Y)[0]

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Pearson correlation:", pearson_correlation(point1, point2))

#clcoding.com

Pearson correlation: 1.0

12. Squared Euclidean Distance:

def squared_euclidean_distance(X, Y):

    return euclidean_distance(X, Y)**2

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Squared Euclidean distance:", squared_euclidean_distance(point1, point2))

#clcoding.com

Squared Euclidean distance: 8.000000000000002

13. Jensen-Shannon Divergence:

def jensen_shannon_divergence(X, Y):

    M = 0.5 * (X + Y)

    return np.sqrt(0.5 * (rel_entr(X, M).sum() + rel_entr(Y, M).sum()))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Jensen-Shannon divergence:", jensen_shannon_divergence(point1, point2))

#clcoding.com

Jensen-Shannon divergence: 0.6569041853099059

14. Chi-Square Distance:

def chi_square_distance(X, Y):

    X = X / np.sum(X)

    Y = Y / np.sum(Y)

    return np.sum((X - Y) ** 2 / (X + Y))

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Chi-Square distance:", chi_square_distance(point1, point2))

#clcoding.com

Chi-Square distance: 0.01923076923076923

15. Spearman Correlation:

from scipy.stats import spearmanr

def spearman_correlation(X, Y):

    return spearmanr(X, Y)[0]

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Spearman correlation:", spearman_correlation(point1, point2))

#clcoding.com

Spearman correlation: 0.9999999999999999

16. Canberra Distance:

from scipy.spatial.distance import canberra

def canberra_distance(X, Y):

    return canberra(X, Y)

# Example usage

point1 = np.array([1, 2])

point2 = np.array([3, 4])

print("Canberra distance:", canberra_distance(point1, point2))

#clcoding.com

Canberra distance: 0.8333333333333333

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