Python: The language of machine learning

Python is a powerful, easy-to-learn language used for web development, data science, AI, automation, and more, making coding simple and creative.

python

Installing Python from Microsoft Store

Window + R -----> Run ---> cmd -------> Type "python" then enter

It will automatically Open Microsoft Store to install python (Note: if you are installing python first time)

Download the latest version for Windows

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Python Package

A Python package is a way of organizing related Python modules (files containing Python code) into a single directory hierarchy. It helps keep your code organized, reusable, and modular.

Why use packages?

  • Organize code into logical sections (like utils, models, views, etc.)
  • Reusability – you can import parts of it in multiple projects
  • Scalability – helps manage large codebases
  • Namespace management – avoid name conflicts

Installing external packages

When people say "Python package" they also often mean packages that are published on PyPI (Python Package Index), which you can install using pip:

pip install requests

Package & Environment Management

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pip

Python’s package installer.

virtualenv / venv

Environment isolation.

conda

Package/environment management (especially for data science).

Web Development in Python

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Flask

Lightweight web framework for building APIs or simple apps.

Django

Full-fledged web framework (ORM, routing, admin panel, etc.).

FastAPI

High-performance API building with automatic documentation.

Numerical & Scientific Computing

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NumPy

Core library for numerical computations, arrays, and linear algebra.

SciPy

Built on NumPy, used for scientific computing like integration, optimization, and statistics.

SymPy

Symbolic mathematics (like algebra, calculus).

QuantLib

Financial quantitative analysis and derivatives pricing.

Data Manipulation & Analysis

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pandas

Data manipulation with DataFrames (similar to R’s data.table or Excel).

Dask

Parallel computing with larger-than-memory datasets using pandas-like syntax.

Vaex

Out-of-core DataFrames for large tabular datasets.

Data Visualization in Python

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matplotlib

Basic plotting library; good for static graphs.

seaborn

Statistical visualizations built on top of matplotlib.

Plotly

Interactive plots for dashboards and web apps.

Altair

Declarative statistical visualization.

Bokeh

Interactive visualizations for modern web browsers.

Machine Learning & Deep Learning

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scikit-learn

Classic machine learning (SVMs, decision trees, clustering).

XGBoost / LightGBM / CatBoost

Gradient boosting libraries for structured data.

TensorFlow

Deep learning, developed by Google.

PyTorch

Deep learning, developed by Facebook (more Pythonic and flexible).

Keras

High-level API for building neural networks (can run on TensorFlow, etc.).

Vaex

Out-of-core DataFrames for large tabular datasets.

Natural Language Processing (NLP)

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nltk

Traditional NLP (tokenization, stemming, POS tagging).

spaCy

Industrial-strength NLP (fast and accurate).

TextBlob

Simple NLP tasks like sentiment analysis.

transformers (Hugging Face)

State-of-the-art models like BERT, GPT, etc.

Web Scraping & APIs

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requests

HTTP requests.

httpx

Async version of requests.

BeautifulSoup

HTML/XML parsing.

Scrapy

Full-featured web crawling framework.

Selenium

Automating browser interaction (for dynamic sites).

Utilities & Miscellaneous

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os / pathlib

File system access.

argparse / click

Command-line interface creation.

datetime / pendulum

Date and time manipulation.

logging

Built-in logging system.

dotenv

Environment variable management.

File & Data Formats

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openpyxl / xlrd / pandas

For Excel files.

csv / json / pickle

For standard data formats.

PyYAML

For YAML files.

h5py

HDF5 format, used in scientific computing.

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