Python-Getting-Started GitHub Details, Stars and Alternatives

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Python-Getting-Started GitHub Details, Stars and Alternatives | OpenRepoFinder

Choose Your Platform:

# Option 1: Google Colab (Recommended for beginners)
# Just click on any .ipynb file and select "Open in Colab"

# Option 2: Local Jupyter
git clone https://github.com/lovnishverma/Python-Getting-Started.git
cd Python-Getting-Started
jupyter notebook

# Option 3: GitHub Codespaces
# Click "Code" → "Codespaces" → "Create codespace"

Start Learning:
- Complete Beginner? → Start with 🐍_Python_Getting_Started.ipynb
- Know Python Basics? → Jump to NumPY.ipynb
- Ready for ML? → Begin with hello_world_of_ML.ipynb
- Want Deep Learning? → Start the bootcamp series 010_bootcamp.ipynb
Track Your Progress:
- ✅ Complete notebooks in order
- 📝 Take notes in provided PDFs
- 🛠️ Build projects from scratch
- 📊 Join our community discussions

Component	Minimum	Recommended
Python	3.7+	3.9+
RAM	4GB	8GB+
Storage	2GB	5GB+
CPU	Dual-core	Quad-core+
GPU	Not required	CUDA-capable for DL

# Just click any notebook link and select "Open in Colab"
# All dependencies are pre-installed!

# 1. Install Anaconda
wget https://repo.anaconda.com/archive/Anaconda3-latest-Linux-x86_64.sh
bash Anaconda3-latest-Linux-x86_64.sh

# 2. Create environment
conda create -n datascience python=3.9
conda activate datascience

# 3. Install packages
conda install jupyter pandas numpy matplotlib seaborn scikit-learn
pip install tensorflow torch yolov8 onnx

# Pull pre-configured image
docker pull jupyter/datascience-notebook
docker run -p 8888:8888 jupyter/datascience-notebook

# Data Science Stack
numpy>=1.21.0
pandas>=1.3.0
matplotlib>=3.4.0
seaborn>=0.11.0
scikit-learn>=1.0.0

# Deep Learning
tensorflow>=2.8.0
torch>=1.11.0
torchvision>=0.12.0

# Computer Vision
opencv-python>=4.5.0
ultralytics>=8.0.0  # YOLOv8

# MLOps
onnx>=1.12.0
joblib>=1.1.0

# Pro Tips for Colab:
# 1. Enable GPU: Runtime → Change runtime type → GPU
# 2. Mount Google Drive: 
from google.colab import drive
drive.mount('/content/drive')

# 3. Install additional packages:
!pip install package_name

Clone the repository:

git clone https://github.com/lovnishverma/Python-Getting-Started.git
cd Python-Getting-Started

Launch Jupyter:

# Option A: Classic Notebook
jupyter notebook

# Option B: JupyterLab (Modern interface)
jupyter lab

# Option C: VS Code with Jupyter extension
code .

Navigate and run notebooks in your browser

Notebook/File	Description	Duration	Prerequisites
`🐍_Python_Getting_Started.ipynb`	Complete Python syntax, data types, control structures	3-4 hours	None
`python_basics.ipynb`	Recursion, factorial, Fibonacci, string operations	2-3 hours	Basic Python
`140_Basic_Python_Practice_Programs.ipynb`	50+ practice programs for fundamentals	4-5 hours	Python basics
`hello.py`	Basic Python script template	30 min	None

Notebook/File	Description	Duration	Prerequisites
`Object_Oriented_Programming_(OOP).ipynb`	Classes, objects, inheritance, polymorphism	4-5 hours	Python fundamentals
`Oop_Python_Notebook.ipynb`	Hands-on OOP practice and real-world examples	3-4 hours	OOP basics

Notebook/File	Description	Duration	Prerequisites
`NumPY.ipynb`	NumPy arrays, indexing, vectorized operations	3-4 hours	Python basics
`🐼_Python_Pandas.ipynb`	Pandas fundamentals: Series, DataFrames	4-5 hours	NumPy
`Pandas.ipynb`	Advanced Pandas operations and data analysis	3-4 hours	Pandas basics
`6_June_Pandas.ipynb`	IndiaAI Pandas workshop content	2-3 hours	Pandas basics
`pandas_bdds.ipynb`	Zero-to-Hero Pandas comprehensive guide	5-6 hours	Python basics

Notebook/File	Description	Duration	Prerequisites
`Matplotlib_Visualization_with_Python.ipynb`	Core Matplotlib visualizations and customization	4-5 hours	NumPy, Pandas
`Matplotlib_Seaborn.ipynb`	Advanced statistical plots with Seaborn	4-5 hours	Matplotlib basics
`Boxplot.ipynb`	Box plot analysis and statistical interpretation	2-3 hours	Basic statistics

Notebook/File	Description	Duration	Prerequisites
`Scikit_Learn_Machine_Learning_in_Python_.ipynb`	Complete Scikit-Learn tutorial and algorithms	6-8 hours	Data Science stack
`hello_world_of_ML.ipynb`	Introduction to ML concepts and workflow	2-3 hours	Basic statistics
[`Classification_using_Supervised_Learning_Models.ipynb`](Classification_using_Supe

Python-Getting-Started

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🧠 Python for Data Science – Comprehensive Learning Repository

🚀 Ready to Start Your Data Science Journey?

🎯 Your Success Is Our Mission

📋 Table of Contents

🔍 Overview

🎯 What You'll Master:

📈 Learning Statistics:

⚡ Quick Start Guide

🚀 30-Second Setup

🛠️ Prerequisites & Installation

📋 System Requirements

🔧 Installation Options

Option 1: Google Colab (Recommended) 🌟

Option 2: Local Anaconda Setup

Option 3: Docker Setup

📦 Required Libraries

Core Libraries

🏃‍♂️ How to Run Notebooks

🌐 Method 1: Google Colab (Easiest)

💻 Method 2: Local Jupyter

☁️ Method 3: GitHub Codespaces

📂 Repository Structure

🐍 Python Fundamentals

🏗️ Object-Oriented Programming

📊 Data Science Libraries

📈 Data Visualization

🤖 Machine Learning