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Python Fundamentals

Mastering Python Fundamentals

Mastering Python fundamentals will empower contribution to meaningful AI-driven projects and initiatives.

Introduction

Python is an open-source Machine Learning (ML) programming language. It is dominant and widely adopted by the developer community. Its English-like syntax makes it accessible for novice users. Python’s architecture is based on reusable code expressed through functions, expediting ML model development. Developers often need to code only 10–15% of lines for desired output.

More on Functions

  • Built-in – predefined functions such as print(), len(), sum(), max(), min(), input(), int(), float(), str().
  • User-defined – created with the def keyword followed by the function name.
  • Lambda – anonymous single-line functions used for simple tasks like sorting, mapping, filtering.
  • Recursive – functions that call themselves to break complex problems into smaller, self-similar ones.

Role of Python in Generative AI

  • Primary Language for AI – simplicity and readability make Python the go-to language supported by extensive libraries.
  • Generative Models – implementation of GAUs, VAEs, Transformers for images, text, and audio.
  • Integration & Deployment – seamless integration with APIs, web servers, and cloud platforms.
  • Extensive Libraries – critical for data manipulation and AI development tasks.
  • Vibrant Community – active and abundant educational resources supporting ongoing learning.

Five Characteristics of Python Object-Orientation

  • Classes – blueprints to define attributes and methods.
  • Encapsulation – promotes data integrity and modularity by controlling access through methods.
  • Inheritance – allows code reuse by inheriting attributes and methods from existing classes.
  • Polymorphism – objects adapt behavior based on methods used.
  • Abstraction – hides complexity, exposing only essential features.

Popular Python Libraries

  • NUMPY – array operations, vectorization, broadcasting; foundation of scientific computing.
  • PANDAS – DataFrames and Series for structured data analysis, ideal for ML preprocessing.
  • MATPLOTLIB – visualization library for static, interactive, and animated plots.
  • SCIKIT-LEARN – ML tasks including classification, regression, clustering, dimensionality reduction.
  • TENSORFLOW – ML and neural networks with Keras API, used for NLP, image recognition, and simulations.

Python Data Elements

Values can be Immutable, Mutable, Ordered, Unordered, Indexed, or Duplicative. Core data types include:

  • Numeric – int, float, complex. Used in math, engineering, measurement, logic.
  • Boolean – True / False. Used for conditions, control flow, validation.
  • Binary – bytes, bytearray, memoryview. Used for communication, memory management, encoding.
  • String – str. Immutable, ordered, indexed. Used for text, NLP, file handling, web content.

Popular Data Structures

  • Tuples (()) – Immutable, ordered, indexed, heterogeneous. Used for data integrity and memory efficiency.
  • Sets ({}) – Mutable, unordered, unique elements. Used for deduplication and set operations.
  • Lists ([]) – Mutable, ordered, indexed, heterogeneous. Used for collections, iteration, and data handling.
  • Dictionaries ({key:value}) – Mutable, unordered. Used for mapping, frequency counts, and APIs.