This is the list of topics we have covered. Press on them and visit and learn. All for free.
Table of Contents
- Why learn Python?
- Python’s history and why it’s popular
- Python’s application in different industries
- Setting up your Python environment (Installing Python, IDEs)
- Understanding Python Syntax
- Data Types and Variables
- Operators
- Basic input and output operations
- Control Flow: if, else, and elif statements
- Introduction to loops
- Understanding and working with ‘for’ loops
- Understanding and working with ‘while’ loops
- Nested loops and loop control statements: break, continue, pass
- Introduction to Python data structures
- Lists in Python
- Tuples in Python
- Dictionaries in Python
- Sets 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