AI / ML

Python for Machine Learning

Python ML libraries

Python for Machine Learning

Python is the most popular programming language for machine learning and data science. Its simplicity, extensive libraries, and active community make it ideal for ML projects.

Why Python for ML?

  • Simple Syntax: Easy to learn and read
  • Rich Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch
  • Large Community: Extensive support and resources
  • Versatile: Good for data processing, visualization, and ML

Essential Python Libraries

  • NumPy: Numerical computing and arrays
  • Pandas: Data manipulation and analysis
  • Matplotlib: Data visualization
  • Scikit-learn: Machine learning algorithms
  • TensorFlow/PyTorch: Deep learning frameworks

Install ML Libraries

Install essential libraries using pip:

pip install numpy pandas matplotlib scikit-learn

Basic ML Workflow

  1. Import libraries
  2. Load and explore data
  3. Preprocess data
  4. Split data (train/test)
  5. Choose and train a model
  6. Evaluate the model
  7. Make predictions

Simple Example

import numpy as np
from sklearn.linear_model import LinearRegression

# Sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

# Create and train model
model = LinearRegression()
model.fit(X, y)

# Make prediction
prediction = model.predict([[6]])
print(prediction)  # [12.]