SCIKIT-LEARN ALCHEMSITS

Basic Python

Features Section

Python Basics: A Complete Beginner’s Guide

Python is a simple, powerful, and versatile high-level programming language. Created by Guido van Rossum in 1991, it is now one of the world’s most popular languages. It is widely used in web development, data science, AI/ML, automation, and scientific computing.

Key Features:

  • It features a clean and readable syntax that makes it easy to learn.
  • It is open-source, free, and community-driven.
  • It does not require semicolons or curly braces; instead, it uses indentation to define code blocks.

Modes of Operation

You can write and run Python code in two main ways:

  • Interactive Mode: Best for quick testing, exploring functions, and debugging. Start it by typing python or python3 in the terminal to run commands one at a time.
  • Script Mode: Best for building applications and larger projects. Write your code in a .py file and execute the whole program by typing python filename.py.

Python Syntax & Fundamentals

Comments Comments make code readable and help explain logic, and they are ignored by Python when the code runs.

  • Single-line comments: Start the line with a # symbol.
  • Multi-line comments (Docstrings): Enclose the text in triple quotes (""" or ''').

Variables & Data Types Python does not require explicit type declarations for variables. Common data types include:

  • String: Text characters (e.g., "Alice").
  • Integer: Whole numbers (e.g., 25).
  • Float: Decimal numbers (e.g., 5.6).
  • Boolean: True or False values (e.g., True).

Operators

Python supports standard mathematical and comparative operations.

Arithmetic Operators

  • Addition (+), Subtraction (-), Multiplication (*), Division (/).
  • Floor Division (//): Divides and rounds down to the nearest whole number (e.g., 10 // 3 results in 3).
  • Modulus (%): Returns the remainder of a division (e.g., 10 % 3 results in 1).
  • Exponent (**): Raises a number to a power (e.g., 2 ** 3 results in 8).

Comparison Operators These compare values and return either True or False.

  • Equal to (==) and Not equal to (!=).
  • Greater than (>) and Less than (<).
  • Greater or equal (>=) and Less or equal (<=).

Control Flow: Conditionals & Loops

Conditional Statements Conditionals control the flow of a program based on specific criteria.

  • if: Executes a block of code only when its condition is True.
  • elif: Checks another condition if the preceding if statement was False.
  • else: Runs a block of code when none of the above conditions are True.

Looping Statements

  • for loops: Used to iterate over sequences like lists, strings, or a specific range of numbers (e.g., range(1, 6)).
  • while loops: Continue to run a block of code as long as a specified condition remains True. The break keyword can be used to exit the loop early.

Functions

Functions are defined using the def keyword and can send back a value using return.

  • Parameters: Functions accept arguments that can be positional (matched by order), default (having fallback values), or keyword (named when calling the function).

Common Built-in Functions

  • print(): Displays output.
  • input(): Reads user input.
  • len(): Returns the length of an object.
  • type(): Checks the data type of a value.
  • Conversion functions like int() and str() convert values to integers and strings, respectively.

Python Libraries & Ecosystem

Libraries extend Python’s native capabilities and are typically installed using pip install.

  • NumPy: Used for numerical computing and array operations.
  • Pandas: Used for data manipulation, DataFrames, and handling CSV/Excel files.
  • Matplotlib: Used for data visualization, charting, and plotting graphs.
  • Flask / Django: Popular frameworks for web development.
  • Requests: Handles HTTP requests and API calls.

Deep Dive: Scikit-learn Scikit-learn is the primary machine learning library used in this coursework. It covers several core ML capabilities:

  • Classification: Categorizing data, such as spam detection.
  • Regression: Predicting continuous values, like prices or temperatures.
  • Clustering: Grouping similar data points, like customer segmentation.
  • Preprocessing: Cleaning and preparing data for models.