1.1 Python 3.21.26

 

Why Python for AI

• Python is the most predominant language for data science, machine learning and AI
  • Rich ecosystem with robust libraries and modules
  • Simple syntax compared to Java, C++, or other languages
  • Emphasizes code readability and reduced development costs
  • Cross-platform compatibility
  • Strong community support and open source ecosystem
• Companies using Python: Netflix, Spotify, Google, and many tech startups
• Python integrates easily with other technologies

How AI is Changing Code

AI is reshaping the coding landscape through four key areas:

1. Code generation - Give natural language instructions to AI to generate code
   • Demonstrated with Gemini analyzing California housing dataset
   • AI generated complete data analysis pipeline from simple prompt
2. Bug detection and debugging - AI can identify and fix code errors
3. Low-code and no-code platforms - Drag and drop interfaces without writing code
4. Predictive coding and smart suggestions - AI completes code as you type
   • Similar to autocomplete but for entire code blocks

• AI models are probabilistic, not deterministic
  • Can make mistakes and hallucinate (create false information)
  • Solutions include prompt engineering, fine-tuning, and governance strategies

Python IDE

Three main IDEs covered for this class:

1. Visual Studio Code - Local installation, lightweight
2. Jupyter Notebook - Through Anaconda, heavy installation
3. Google Colab - Browser-based, cloud hosted, recommended

Install and setup VSCode

Installation process:

1. Download from official website based on operating system
2. Accept agreement and follow installation prompts
3. Optional: Check box to create desktop icon
4. Sign in with GitHub or Google for AI features (Copilot)
   • Can skip registration initially
5. Extensions available for enhanced functionality

Installing Jupyter

• Requires Anaconda installation (heavy, requires significant disk space)
• Alternative: Use web version or stick with VSCode/Colab
• Not mandatory if comfortable with other IDEs
• Provides notebook experience similar to Colab but locally hosted

Collab by Google

• Browser-based, cloud environment
• No local installation required
• Free tier includes:
  • 12.67 GB RAM
  • 107 GB disk space
  • Built-in Gemini AI assistant
• Access through Gmail account → Google Drive
• Recommended for class due to:
  • No package conflicts
  • No local storage requirements
  • Persistent access across devices

Python: Identifier

Naming rules for variables, functions, classes, and modules:

Valid characteristics:

• Combination of letters (upper/lowercase), digits (0-9), underscores
• Any length but cannot start with digits
• Case sensitive (lowercase ‘a’ ≠ uppercase ‘A’)

Invalid characteristics:

• Special symbols (@, #, $, %) cannot be used
• Keywords (global, class) cannot be used as identifiers

Examples:

• Valid: myClass, var_1, count, first_name
• Invalid: 1variable, class@new, global

Collab using Gemini w errors

• Gemini integrated directly into Google Colab
• Can explain error messages and suggest fixes
• Demonstrated indentation error example:
  • Python requires proper indentation after if statements
  • IDE automatically handles indentation when typing colons

Comments

Two types of comments for human-readable code documentation:

1. Single line comments - Use # (hash/pound sign)
   • Explain what the following line of code does
   • Example: # Import modules and packages
2. Multiple line comments - Use triple quotes (“”" or ‘’')
   • For longer explanations spanning multiple lines
   • Comments are ignored by program execution, meant for human readers

Input and Output

Python provides two primary functions:

1. input() - Takes user input
   • Waits for user to enter data
   • Example: name = input("Enter your name: ")
2. print() - Displays output
   • Shows results of program processing
   • Example: print("Hello", name)

• Demonstrated interactive program taking first name, last name, and displaying greeting

Python variables

Variables store data values with different types:

Assignment examples:

• x = 10 (integer)
• name = "Alice" (string)
• price = 99.99 (float)
• is_active = True (boolean)

Multiple variable assignment:

• Single line: a, b, c = 1, "hello", 3.14
• Separate with commas for efficiency

Data Types

Scalar data types:

• Integer: 123, 10, 23
• Float: 1.2, 99.99 (decimal numbers)
• Boolean: True, False
• Complex: 2+6j

Aggregate data types:

• String: “ABC”, “Alice”
• Set: {2, 3, 4, ‘abc’}
• List: [12, ‘a’, 9.9]
• Tuple: (3.3, ‘a’, 45)
• Dictionary: {‘a’: 1, ‘b’: 3}
• Data type of any object can be checked using built-in function type()

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