Phyton Programming Bible: [3 in 1] The Complete Crash Course to Learn and Explore Python beyond the Basic

Introduction to Python
	What is Python?
		Brief history and development of Python
		Features and strengths of Python
	Why learns Python?
		Real-world applications of Python
		Career opportunities with Python:
	Installing Python
		Windows:
		macOS:
		Linux:
		Configuring the Python Environment:
	Python Development Environments
		Choosing the Right IDE for Your Needs
Chapter 1: Basic Concepts
	Data Types
	Variables
	Operators
	Basic Data Structures
	Control Flow
Functions and Modules
	Functions and Parameters
		Defining and Calling Functions
		Positional and Keyword Arguments
	Returning Values
		Multiple Return Values
	Built-in Functions
	Importing Modules
		Overview of Python Modules
		Importing Modules in Your Code
	Creating and Using Your Own Modules
		Creating a Custom Python Module
		Using a Custom Python Module
		Organizing Your Code with Modules
Chapter 2: Input and Output
	Standard Input/Output
		Basic input/output with Python
	Reading and writing to the console
	Reading and Writing Files
		Reading text and binary files with Python
		Writing data to files
	Error Handling
		Handling exceptions with try/except blocks
		Raising your own exceptions
Chapter 3: Object-Oriented Programming
	Classes and Objects
	Methods and Attributes
	Inheritance
		The Benefits of Inheriting Properties and Methods From Parent Classes
		Creating child classes
	Polymorphism
		Using polymorphism in Python
		Polymorphism in Inheritance
		Overriding Methods
Chapter 4: Advanced Topics
	Regular Expressions
		Overview of Regular Expressions:
		Using Regular Expressions in Python:
	Lambda Functions
		Introduction to Lambda Functions:
	List Comprehensions
		Creating Lists with List Comprehensions:
		Advanced List Comprehension Techniques:
	Decorators
		Overview of Decorators in Python:
		Creating and Using Decorators:
	Generators
		Overview of Generators in Python:
		Creating and Using Generators:
Chapter 1: Python Libraries and Applications
	NumPy
		Overview of NumPy:
		Using NumPy for numerical computations:
	Pandas
		Overview of Pandas
		Using Pandas for data manipulation and analysis:
	Matplotlib
		Overview of Matplotlib:
		Creating data visualizations with Matplotlib:
	Flask
		Overview of Flask
		Building web applications with Flask
	Django
		Overview of Django
		Building web applications with Django
Chapter 2: Working with APIs
	What are APIs?
		Types of APIs
	HTTP Requests and Responses
		Overview of HTTP protocol
		Sending and receiving HTTP requests with Python
	JSON Data Format
		Introduction to JSON
		Parsing and creating JSON data in Python
	Accessing APIs with Python
		Using the Requests library to access APIs
		Authentication with APIs
	Examples of Popular APIs
		Twitter API
		OpenWeatherMap API
		Google Maps API
Chapter 3: Data Analysis and Visualization
	Reading Data with Pandas
		Importing data into Pandas
		Working with different data formats
	Data Cleaning and Preparation
		Handling missing data
		Data normalization and scaling
	Exploratory Data Analysis
		Summary statistics and visualizations
		Data profiling and exploration techniques
	Visualizing Data with Matplotlib and Seaborn
		Creating charts and graphs with Matplotlib
		Using Seaborn for advanced visualization
	Basic Statistical Analysis with Python
		Descriptive Statistics
		Hypothesis Testing
Chapter 4: Machine Learning with Python
	Overview of Machine Learning
		Types of Machine Learning Algorithms
	Supervised and Unsupervised Learning
		Supervised Learning
		Unsupervised Learning
		Difference between Supervised and Unsupervised Learning
	Scikit-Learn Library
		Using Scikit-Learn for machine learning tasks
		Examples of using Scikit-Learn for machine learning tasks
	Common Machine Learning Algorithms
	Applications of Machine Learning in Python
Chapter 5: Web Scraping with Python
	What is Web Scraping?
	How to Use Python for Web Scraping
		Requests Library
		BeautifulSoup Library
	Scraping Data from Websites
		Step 1: Send a GET Request
		Step 2: Parse the HTML
		Step 3: Extract Data
	Data Extraction and Cleaning
		Regular Expressions
		String Manipulation
Chapter 6: Data Science with Python
	Introduction to Data Science
	Working with Data Frames in Python
	Data Visualization with Matplotlib and Seaborn
	Exploratory Data Analysis and Statistical Analysis
	Linear and Logistic Regression Analysis
Chapter 7: Web Development with Python
	Introduction to web development with Python
	Creating dynamic websites using Flask and Django
	Building web applications with Python
Chapter 8: Testing and Debugging in Python
	Why Testing and Debugging is Important
		Types of Testing in Python
		Unit Testing with Pytest
		Debugging Techniques in Python
		Profiling Python Code
Chapter 9: Networking with Python
	Introduction to networking in Python
	Basic networking concepts
	Socket programming with Python
	Client-server communication in Python
	Networking libraries in Python (e.g. Twisted, Scapy)
Chapter 10: Game Development with Python
	Introduction to Game Development with Python
	Pygame library for Game Development
	Creating Games with Python
	Physics Simulation in Python Game Development
	Game Design Principles and Strategies
Chapter 11: Cybersecurity with Python
	Introduction to Cybersecurity with Python
	Cryptography and Encryption in Python
	Network Security with Python
	Web Security with Python
	Threat Detection and Response with Python
Chapter 12: Big Data with Python
	Introduction to Big Data and Python
	Processing Big Data with Python
	Working with Hadoop and Spark using Python
	Storing and Managing Big Data with Python
	Data Visualization and Analysis for Big Data with Python
Chapter 13: Natural Language Processing with Python
	Introduction to natural language processing:
	Text pre-processing and cleaning with Python:
	Sentiment analysis with Python:
	Named entity recognition with Python:
	Topic modeling with Python:
BOOK 3: MASTERING PYTHON LIKE A PRO
Chapter 1: Deep Learning with Python
	Introduction to deep learning
	Neural network basics
	Keras library for deep learning with Python
	Convolutional neural networks for image processing
	Recurrent neural networks for natural language processing
Chapter 2: Cloud Computing with Python
	Introduction to Cloud Computing with Python
	Cloud Computing Platforms (e.g. AWS, Google Cloud, Azure)
	Managing Cloud Infrastructure with Python
	Deploying Python Applications to the Cloud
	Big Data Processing in the Cloud with Python
Chapter 3: GUI Programming with Python
	Introduction to GUI programming with Python
	Tkinter library for GUI programming with Python
	Building desktop applications with Python
	Designing user interfaces with Python
	Event-driven programming in GUI programming with Python
Chapter 4: Mobile App Development with Python
	Introduction to Mobile App Development with Python
	Kivy Library for Mobile App Development with Python
	Building Cross-Platform Mobile Apps with Python
	User Interface Design for Mobile Apps with Python
	Mobile App Deployment with Python
Chapter 5: Future Work and Next Steps
	Review of Python Basics
	Tips for Continued Learning and Practice
	Future Directions and Applications for Python
		Applications of Python in different fields:
	Appendix: Python Reference
		Python Version
		Syntax
		Data Types
		Variables
		Operators
		String Methods
		Date and Time
		File Handling
		Exception Handling
Conclusion