Building and deploying Machine Learning models requires specialized tools and libraries. Some of the most popular ones include:

Scikit-learn – A Python library for machine learning algorithms (classification, regression, clustering).
TensorFlow & PyTorch – Deep learning frameworks used for neural networks and AI applications.
Keras – A user-friendly API for deep learning, running on top of TensorFlow.
OpenCV – A powerful library for computer vision and image processing.
XGBoost & LightGBM – Optimized gradient boosting algorithms for structured data analysis.
Google Cloud AI & AWS Machine Learning – Cloud-based AI platforms for scalable model deployment.

These tools empower developers, researchers, and businesses to create and deploy intelligent models efficiently.