Python Course Overview

Introduction

• Instructor: Mosh
• Course focus: Learn Python programming and build projects.
• Python uses: Popular in automation, AI, building applications, websites (e.g., Instagram, Dropbox).

Course Structure

• Core concepts of Python.
• Three projects to be built:
  1. Website for an imaginary grocery store using Django.
  2. Machine learning project to predict music preferences based on profiles.
  3. Automation tools for repetitive tasks (e.g., processing spreadsheets).

Setting Up Python

Downloading Python

1. Visit Python.org
2. Click on Downloads, download Python 3.x.
3. Windows: Check "Add Python to PATH" during installation.
4. Mac: Follow setup wizard to install.

Installing Code Editor

• Recommended: PyCharm (IDE for Python).

1. Download from JetBrains.com/pycharm.
2. Install Community Edition (free version).

Writing Your First Python Program

1. Create a new project in PyCharm.
2. Create a Python file (e.g., app.py).
3. Write print('Your Name') to display name.
4. Run the program.

Drawing with Python

• Using print statements to draw shapes (e.g., a dog).
• Understanding how Python executes code line by line.

Learning Python Basics

Variables

• Definition: Variables store data in memory (e.g., price = 10).
• Types: Integer, Float, String, Boolean.

Input/Output

• Use input() to receive user input.
• Use print() to display output.

Control Flow

• Conditional statements (if, else, elif).
• Loops: for and while loops.

Functions

• Definition: Functions are reusable code blocks.
• Parameters and return values.

Lists

• Creating and manipulating lists.
• Common list methods (e.g., append, insert, remove, sort).

Dictionaries

• Storing key-value pairs.
• Accessing and modifying dictionaries.

Classes and Objects

• Defining classes and creating objects.
• Understanding attributes and methods.

Inheritance

• Using inheritance to extend functionality of classes.

Modules and Packages

• Organizing code into modules and packages.
• Importing modules and using functions from them.

Error Handling

• Using try and except blocks to handle exceptions.

Machine Learning Introduction

Steps in a Machine Learning Project

1. Import data.
2. Clean data.
3. Split data into training and testing sets.
4. Create a model using an algorithm.
5. Train the model and make predictions.
6. Evaluate the model.

Key Libraries

• Numpy, Pandas, Matplotlib, Scikit-learn.

Working with Jupyter

• Setting up a Jupyter notebook for data analysis.

Final Project with Django

• Introduction to building web applications using Django framework.
• Overview of creating a Django project and managing it with manage.py.

Conclusion

• Recap of key concepts learned in the course.
• Encouragement to practice and explore further applications of Python.