Phyton Programming Bible: [3 in 1] The Complete Crash Course to Learn and Explore Python beyond the Basics. Including Examples and Practical Exercises to Master Python from Beginners to Pro

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