Scikit-Learn Boss in 90 Days

Day 2: Setting Up Environment

Python Environment Setup

📑 Table of Contents

🌟 Welcome to Day 2
🖥️ Installing Python and Dependencies
- Python Installation
- Package Managers (pip, conda)
- Virtual Environments
🔧 Setting Up Your Machine Learning Workspace
- Anaconda Distribution
- Miniconda
- Using Requirements Files
💻 IDEs and Editors
- VS Code
- PyCharm
- Jupyter Notebook
📁 Organizing Your Projects
- Directory Structure
- Version Control (Git)
🧩 Hands-On Exercises
📚 Resources
💡 Tips and Tricks

1. 🌟 Welcome to Day 2

Welcome to Day 2 of your journey to becoming a Scikit-Learn Boss in 90 Days! 🎉 Today’s focus is on setting up a clean and efficient environment for your machine learning projects. A well-structured environment ensures smoother development, reproducibility, and maintains consistency across projects. Let’s get everything ready to tackle advanced concepts in the days ahead! 🚀

2. 🖥️ Installing Python and Dependencies

📝 Python Installation

Official Python Website: Download and install the latest stable version of Python from python.org.
Check Installation:
```
python3 --version
```

📝 Package Managers (pip, conda)

pip: The default Python package manager.
```
pip install numpy pandas scikit-learn
```
conda: An environment and dependency manager that comes with Anaconda or Miniconda.
```
conda install numpy pandas scikit-learn
```

📝 Virtual Environments

Creation:
```
python3 -m venv my_env
```

Activation:

Linux/MacOS:
```
source my_env/bin/activate
```
Windows:
```
my_env\Scripts\activate
```

Deactivation:
```
deactivate
```

3. 🔧 Setting Up Your Machine Learning Workspace

📝 Anaconda Distribution

All-In-One Package: Comes pre-installed with Python, Jupyter, and popular data science libraries.
Installation: Download from anaconda.com and follow the setup instructions.

📝 Miniconda

Lightweight Alternative: Provides a minimal environment and conda for managing packages and environments.
Installation: Download from docs.conda.io.

📝 Using Requirements Files

requirements.txt:
```
pip install -r requirements.txt
```
environment.yml (for conda):
```
conda env create -f environment.yml
```

4. 💻 IDEs and Editors

📝 VS Code

Extensions: Python, Jupyter, Pylance.
Integrated Terminal: Manage environments and run Python files directly.

📝 PyCharm

Professional Environment: Robust tools for testing, debugging, and refactoring.
Integrated Tools: Virtual environment setup and version control within the IDE.

📝 Jupyter Notebook

Interactive Environment: Perfect for exploration, experimentation, and quick prototyping.
Run in Browser:
```
jupyter notebook
```

5. 📁 Organizing Your Projects

📝 Directory Structure

Example:

project_name/
  data/
  notebooks/
  src/
  tests/
  README.md
  requirements.txt

📝 Version Control (Git)

Initialize Repo:
```
git init
```

Commit Changes:

git add .
git commit -m "Initial commit"

6. 🧩 Hands-On Exercises

📝 Exercise 1: Create and Activate a Virtual Environment

Task: Create a virtual environment named ml_env and activate it. Install numpy, pandas, and scikit-learn inside it.
```
python3 -m venv ml_env
source ml_env/bin/activate
pip install numpy pandas scikit-learn
```

📝 Exercise 2: Set Up a Conda Environment

Task: Using Anaconda or Miniconda, create a conda environment named ml_conda_env with Python 3.9 and install matplotlib and seaborn.
```
conda create --name ml_conda_env python=3.9
conda activate ml_conda_env
conda install matplotlib seaborn
```

📝 Exercise 3: Start a Jupyter Notebook

Task: From within your activated environment, start a Jupyter Notebook and verify you can import sklearn.
```
jupyter notebook
# In a new notebook cell:
import sklearn
print(sklearn.__version__)
```

7. 📚 Resources

8. 💡 Tips and Tricks

💡 Pro Tip

Isolate Your Projects: Keep separate environments for different projects to avoid dependency conflicts.

🛠️ Recommended Tools

Poetry: For dependency management and packaging.
Docker: For containerized, reproducible environments.

🚀 Speed Up Your Setup

Use Environment Files: Automate environment creation with requirements.txt or environment.yml.
Shortcuts: conda activate and conda deactivate for quick environment switching.

🔍 Debugging Setup Issues

Check PATH: Ensure Python and package managers are on your PATH.
Reinstall: If issues persist, reinstall Python or Anaconda.
Community Forums: Leverage Stack Overflow and official docs for troubleshooting.

Scikit-Learn Boss in 90 Days