Python for AI

Python for AI refers to the application of the Python programming language as the primary tool for building artificial intelligence systems. Its dominance in the field is due to a powerful combination of a simple, readable syntax that facilitates rapid prototyping and an extensive ecosystem of specialized libraries. This toolkit includes foundational libraries like NumPy and Pandas for data manipulation, Scikit-learn for a wide array of machine learning algorithms, and comprehensive frameworks such as TensorFlow and PyTorch that provide the essential building blocks for developing, training, and deploying complex deep learning models, making it the de facto standard for both researchers and industry professionals.

Python Fundamentals for AI

1.1.

Setting Up the Python Environment

1.1.1.

Python Installation

1.1.1.1.

Understanding Python Versions

1.1.1.2.

Downloading Python from Official Sources

1.1.1.3.

Installing Python on Windows

1.1.1.4.

Installing Python on macOS

1.1.1.5.

Installing Python on Linux

1.1.1.6.

Verifying Installation Success

1.1.1.7.

Understanding PATH Configuration

1.1.2.

Package Management with pip

1.1.2.1.

Understanding pip Basics

1.1.2.2.

Installing Packages

1.1.2.3.

Upgrading Packages

1.1.2.4.

Uninstalling Packages

1.1.2.5.

Listing Installed Packages

1.1.2.6.

Using requirements.txt Files

1.1.2.7.

Understanding Package Dependencies

1.1.2.8.

Working with Different Package Versions

1.1.3.

Virtual Environments

1.1.3.1.

Understanding Virtual Environment Concepts

1.1.3.2.

Creating Virtual Environments with venv

1.1.3.3.

Activating Virtual Environments

1.1.3.4.

Deactivating Virtual Environments

1.1.3.5.

Managing Dependencies in Virtual Environments

1.1.3.6.

Working with Multiple Python Versions

1.1.3.7.

Virtual Environment Best Practices

1.1.4.

Development Environment Setup

1.1.4.1.

Installing Jupyter Notebook

1.1.4.2.

Installing JupyterLab

1.1.4.3.

Basic Jupyter Notebook Operations

1.1.4.4.

Understanding Jupyter Cells

1.1.4.5.

Using Markdown in Jupyter

1.1.4.6.

Jupyter Keyboard Shortcuts

1.1.4.7.

Popular Python IDEs Overview

1.1.4.8.

Setting Up VS Code for Python

1.1.4.9.

Setting Up PyCharm for Python

1.2.

Core Python Syntax and Concepts

1.2.1.

Variables and Assignment

1.2.1.1.

Variable Declaration and Naming

1.2.1.2.

Variable Naming Conventions

1.2.1.3.

Understanding Variable Scope

1.2.1.4.

Memory Management Basics

1.2.2.

Data Types

1.2.2.1.

Numeric Types

1.2.2.1.1.

Integers

1.2.2.1.2.

Floating Point Numbers

1.2.2.1.3.

Complex Numbers

1.2.2.1.4.

Type Conversion Between Numeric Types

1.2.2.1.5.

Arithmetic Operations

1.2.2.1.6.

Mathematical Functions

1.2.2.2.

String Data Type

1.2.2.2.1.

String Creation and Literals

1.2.2.2.2.

String Indexing

1.2.2.2.3.

String Slicing

1.2.2.2.4.

String Methods

1.2.2.2.5.

String Formatting with f-strings

1.2.2.2.6.

String Formatting with format()

1.2.2.2.7.

1.2.2.2.8.

1.2.2.3.

1.2.2.3.1.

1.2.2.3.2.

1.2.2.3.3.

Truthy and Falsy Values

1.2.2.3.4.

Boolean Conversion

1.2.3.

Operators

1.2.3.1.

Arithmetic Operators

1.2.3.1.1.

Addition and Subtraction

1.2.3.1.2.

Multiplication and Division

1.2.3.1.3.

1.2.3.1.4.

1.2.3.1.5.

1.2.3.1.6.

1.2.3.2.

1.2.3.2.1.

1.2.3.2.2.

1.2.3.2.3.

Greater Than and Less Than

1.2.3.2.4.

1.2.3.3.

1.2.3.3.1.

1.2.3.3.2.

1.2.3.3.3.

1.2.3.3.4.

Short-circuit Evaluation

1.2.3.4.

Assignment Operators

1.2.3.4.1.

Basic Assignment

1.2.3.4.2.

Compound Assignment Operators

1.2.3.5.

Identity and Membership Operators

1.2.3.5.1.

is and is not

1.2.3.5.2.

in and not in

1.3.

Python Data Structures

1.3.1.

Lists

1.3.1.1.

Creating Lists

1.3.1.2.

Accessing List Elements

1.3.1.3.

1.3.1.4.

1.3.1.5.

1.3.1.6.

1.3.1.6.1.

1.3.1.6.2.

1.3.1.6.3.

1.3.1.6.4.

1.3.1.6.5.

1.3.1.6.6.

1.3.1.6.7.

1.3.1.6.8.

1.3.1.6.9.

1.3.1.7.

1.3.1.8.

1.3.1.9.

1.3.2.

1.3.2.1.

1.3.2.2.

Tuple Indexing and Slicing

1.3.2.3.

1.3.2.4.

1.3.2.5.

1.3.2.6.

1.3.2.7.

1.3.3.

1.3.3.1.

Creating Dictionaries

1.3.3.2.

Accessing Dictionary Values

1.3.3.3.

Modifying Dictionaries

1.3.3.4.

1.3.3.4.1.

1.3.3.4.2.

1.3.3.4.3.

1.3.3.4.4.

1.3.3.4.5.

1.3.3.4.6.

1.3.3.4.7.

1.3.3.4.8.

1.3.3.5.

Dictionary Comprehensions

1.3.3.6.

1.3.4.

1.3.4.1.

1.3.4.2.

1.3.4.2.1.

1.3.4.2.2.

1.3.4.2.3.

1.3.4.2.4.

1.3.4.3.

1.3.4.3.1.

1.3.4.3.2.

1.3.4.3.3.

1.3.4.3.4.

1.3.4.3.5.

1.3.4.4.

1.3.4.5.

1.3.5.

1.3.5.1.

1.3.5.1.1.

Basic List Comprehensions

1.3.5.1.2.

Conditional List Comprehensions

1.3.5.1.3.

Nested List Comprehensions

1.3.5.2.

Dictionary Comprehensions

1.3.5.2.1.

Basic Dictionary Comprehensions

1.3.5.2.2.

Conditional Dictionary Comprehensions

1.3.5.3.

Set Comprehensions

1.3.5.4.

Generator Expressions

1.4.

Control Flow

1.4.1.

Conditional Statements

1.4.1.1.

1.4.1.2.

1.4.1.3.

1.4.1.4.

1.4.1.5.

Ternary Conditional Expressions

1.4.1.6.

Multiple Conditions

1.4.2.

Loops

1.4.2.1.

for Loops

1.4.2.1.1.

Iterating Over Sequences

1.4.2.1.2.

Using range() Function

1.4.2.1.3.

Iterating Over Dictionaries

1.4.2.1.4.

Iterating with enumerate()

1.4.2.1.5.

1.4.2.2.

1.4.2.2.1.

1.4.2.2.2.

1.4.2.3.

1.4.2.4.

Loop Control Statements

1.4.2.4.1.

1.4.2.4.2.

1.4.2.4.3.

1.4.3.

1.4.3.1.

Understanding Exceptions

1.4.3.2.

try-except Blocks

1.4.3.3.

Handling Multiple Exceptions

1.4.3.4.

else Clause in Exception Handling

1.4.3.5.

1.4.3.6.

1.4.3.7.

1.5.

1.5.1.

1.5.1.1.

1.5.1.2.

1.5.1.3.

Function Naming Conventions

1.5.1.4.

Function Documentation

1.5.2.

Parameters and Arguments

1.5.2.1.

Positional Parameters

1.5.2.2.

Keyword Parameters

1.5.2.3.

Default Parameters

1.5.2.4.

Variable-length Arguments

1.5.2.4.1.

*args

1.5.2.4.2.

**kwargs

1.5.2.5.

Keyword-only Arguments

1.5.2.6.

Positional-only Arguments

1.5.3.

Return Values

1.5.3.1.

Returning Single Values

1.5.3.2.

Returning Multiple Values

1.5.3.3.

Return Statement Behavior

1.5.4.

1.5.4.1.

1.5.4.2.

1.5.4.3.

1.5.4.4.

1.5.5.

Advanced Function Concepts

1.5.5.1.

Lambda Functions

1.5.5.2.

Higher-order Functions

1.5.5.3.

Functions as First-class Objects

1.5.5.4.

Decorators

1.5.5.5.

Closures

1.5.6.

Built-in Functions for Data Processing

1.5.6.1.

1.5.6.2.

1.5.6.3.

1.5.6.4.

1.5.6.5.

1.6.

Object-Oriented Programming

1.6.1.

1.6.1.1.

1.6.1.2.

1.6.1.3.

1.6.1.4.

1.6.1.5.

1.6.2.

1.6.2.1.

1.6.2.2.

1.6.2.3.

1.6.2.4.

Method Resolution Order

1.6.3.

1.6.3.1.

1.6.3.2.

1.6.3.3.

1.6.3.4.

1.6.3.5.

Abstract Base Classes

1.6.4.

1.6.4.1.

1.6.4.2.

1.6.4.3.

1.6.4.4.

Getter and Setter Methods

1.6.5.

1.6.5.1.

1.6.5.2.

1.6.5.3.

1.6.6.

1.6.6.1.

1.6.6.2.

1.6.6.3.

1.6.6.4.

1.7.

File Handling and Data Persistence

1.7.1.

1.7.1.1.

1.7.1.2.

1.7.1.2.1.

1.7.1.2.2.

1.7.1.2.3.

Reading with Context Managers

1.7.1.3.

1.7.1.3.1.

1.7.1.3.2.

1.7.1.4.

1.7.1.5.

Working with File Paths

1.7.2.

Structured Data Formats

1.7.2.1.

CSV Files

1.7.2.1.1.

Reading CSV with csv Module

1.7.2.1.2.

1.7.2.1.3.

1.7.2.2.

1.7.2.2.1.

1.7.2.2.2.

1.7.2.2.3.

1.7.2.3.

1.7.2.3.1.

1.7.2.3.2.

1.7.3.

1.7.3.1.

1.7.3.2.

1.7.3.3.

1.8.

1.8.1.

1.8.1.1.

1.8.1.2.

1.8.1.3.

from...import Statements

1.8.1.4.

Module Search Path

1.8.1.5.

Reloading Modules

1.8.2.

Standard Library Modules

1.8.2.1.

1.8.2.2.

1.8.2.3.

1.8.2.4.

1.8.2.5.

1.8.2.6.

1.8.3.

Creating Custom Modules

1.8.3.1.

1.8.3.2.

1.8.3.3.

1.8.4.

1.8.4.1.

1.8.4.2.

1.8.4.3.

1.8.4.4.

1.8.4.5.

1.9.

Error Handling and Debugging

1.9.1.

1.9.1.1.

1.9.1.2.

1.9.1.3.

1.9.2.

1.9.2.1.

1.9.2.2.

Using Python Debugger (pdb)

1.9.2.3.

IDE Debugging Features

1.9.3.

Testing Basics

1.9.3.1.

Unit Testing Concepts

1.9.3.2.

unittest Module

1.9.3.3.

Writing Test Cases

1.9.3.4.

Test-driven Development

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2. Essential Libraries for Data Science and AI