Python Course Overview

Introduction

• Instructor: Mosh Hamedani
• Focus: Learn Python programming from scratch.
• Applications of Python: Automation, AI, web development (e.g., Instagram, Dropbox).
• Course structure includes core concepts and three projects.

Project 1: Grocery Store Website

• Build a website using Django framework.
• Features: Product display and admin area for stock management.

Project 2: Machine Learning

• Write a program to predict music preferences based on user profiles.
• Example: Similar to YouTube recommendations.

Project 3: Automation

• Automate repetitive tasks like processing spreadsheets.
• Example: Process thousands of spreadsheets quickly.

Course Structure

• Designed for beginners; no prior knowledge required.
• Encouragement for practice and hands-on exercises.

Installing Python

1. Go to python.org.
2. Click on Downloads and download the latest version (e.g., Python 3.7.2).
3. Install Python (ensure to add it to PATH on Windows).
4. Install a code editor: PyCharm.
   • Download from JetBrains.
   • Choose community edition (free).
5. Run PyCharm and set up a new project.

Programming Basics

• Create first Python program: print("Your Name").
• Explore data types: integers, floats, strings, booleans.
• Define variables and basic arithmetic operations.

Control Structures

• If Statements: Execute code based on conditions.
• Loops:
  • For Loops: Iterate over sequences.
  • While Loops: Repeat code while a condition is true.

Functions

• Define functions using def keyword.
• Parameters allow passing data into functions.
• Functions can return values using the return statement.

Lists and Dictionaries

• Lists: Ordered collection of items.
• Dictionaries: Key-value pairs for storing related data.
• Methods: Add, remove, and manipulate data.

Classes and Objects

• Classes: Blueprint for creating objects.
• Attributes: Variables stored in an object.
• Methods: Functions defined in a class.
• Inheritance: Base classes provide functionality for derived classes.

Modules and Packages

• Modules: Python files containing code.
• Packages: Directories with multiple modules, identified by __init__.py.
• Importing modules and using their functions.

Error Handling

• Use try and except to handle exceptions and prevent crashes.
• Catch specific exceptions for better control.

Jupyter Notebooks

• Environment for interactive coding and data analysis.
• Useful for machine learning projects, allows easy visualization of data.

Machine Learning Overview

• Steps:
  1. Import and clean the data.
  2. Split data into training and testing sets.
  3. Build a model using an algorithm (e.g., decision trees).
  4. Evaluate model accuracy.

Next Steps

• Build a web application with Django.
• Learn to automate tasks using Python.
• Explore advanced topics in Python and machine learning.