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Table of Contents

1. Introduction to Python

2. Python Basics

3. Looping in Python

4. Data Structures in Python

5. Functions and Modules in Python

  • Introduction to functions
  • Defining and calling functions
  • Function arguments and return values
  • Introduction to Python modules
  • Importing and using modules

6. File Handling in Python

  • Reading and writing files
  • File modes
  • Working with CSV, JSON files
  • Error and Exception handling

7. Object-Oriented Programming in Python

  • Basics of OOP
  • Classes and Objects
  • Inheritance
  • Polymorphism
  • Encapsulation

8. Advanced Python Concepts

  • Decorators
  • Generators
  • List comprehension
  • Lambda functions
  • Error and Exception handling (Advanced)

9. Python Libraries

  • Introduction to Python libraries
  • NumPy: Numerical Python
  • Pandas: Data Manipulation
  • Matplotlib: Data Visualization

10. Working with Databases

  • Understanding databases
  • SQL and NoSQL databases
  • Python and SQL
  • Python and MongoDB (or other NoSQL databases)

11. Introduction to Web Development with Python

  • Understanding Web Development
  • Flask: a lightweight framework
  • Django: a high-level Python Web framework
  • Building a simple web application

12. Python in Data Science

  • Introduction to Data Science
  • Python’s role in Data Science
  • Working with Data: Collection, Cleaning, and Analysis
  • Machine Learning with Python

13. Python in Machine Learning and AI

  • Basics of Machine Learning and AI
  • Supervised Learning and Unsupervised Learning
  • Deep Learning Basics
  • Libraries: Scikit-Learn, TensorFlow, PyTorch
  • Building a simple ML/AI model

14. Best Practices and Coding Standards

  • PEP 8 and Pythonic coding
  • Code commenting and documentation
  • Testing and debugging
  • Python project structure
  • Version control with Git

15. Building a Python Portfolio

  • Importance of a portfolio
  • Project ideas for a portfolio
  • Showcasing your projects on GitHub
  • How to write an effective README