Anaconda und Jupyter Notebook installieren

# Verify installation in terminal (not Python)
# conda --version
# Output: conda 23.11.0

# Also verify Python
# python --version
# Output: Python 3.11.x

# Run these commands in your terminal

# Create a new environment named 'ml_course' with Python 3.11
# conda create -n ml_course python=3.11

# Activate the environment
# conda activate ml_course

# Your prompt will change to show (ml_course)
# All pip/conda installs now go into this environment only

# Deactivate when done
# conda deactivate

# Install all core ML packages in one command
# conda install -n ml_course numpy pandas scikit-learn matplotlib seaborn jupyter

# Or use pip if you prefer
# pip install numpy pandas scikit-learn matplotlib seaborn notebook

# Verify in Python
import numpy as np
import pandas as pd
import sklearn
print('numpy:', np.__version__)
print('pandas:', pd.__version__)
print('sklearn:', sklearn.__version__)

# In terminal (after conda activate ml_course):
# jupyter notebook

# This opens http://localhost:8888 in your browser
# Create a new notebook and run this test cell:

import numpy as np
import pandas as pd

print('Environment is working!')
print('NumPy array:', np.array([1, 2, 3]) * 2)

# Install JupyterLab
# pip install jupyterlab

# Launch JupyterLab
# jupyter lab

# Key JupyterLab shortcuts:
# Shift+Enter  -> run cell and move to next
# Ctrl+Enter   -> run cell, stay on same cell
# A            -> insert cell above (command mode)
# B            -> insert cell below (command mode)
# M            -> convert cell to Markdown
# D, D         -> delete cell

# Useful magic commands in Jupyter

# Show all variables in memory
# %who

# Time how long a cell takes to run
# %time sum(range(1_000_000))

# See a matplotlib plot inline (no extra plt.show() needed)
# %matplotlib inline

# Reload external modules automatically when they change
# %load_ext autoreload
# %autoreload 2

# List all conda environments
# conda env list

# Export current environment to YAML
# conda env export > environment.yml

# Create environment from YAML (for teammates)
# conda env create -f environment.yml

# Remove an environment you no longer need
# conda env remove -n old_project

# Update a package
# conda update scikit-learn

# Full stack verification cell
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Quick smoke test
X = np.random.randn(100, 2)
y = X[:, 0] * 2 + np.random.randn(100) * 0.1

model = LinearRegression()
model.fit(X, y)
print('All imports OK. Model trained successfully.')
print('Coefficient 0:', round(model.coef_[0], 2))  # should be ~2.0