Top Machine Learning Libraries You Should Learn in 2025

Anudeep Mahavadi

ML Skills for 2025: Start with the essential libraries powering modern machine learning and real-world AI projects.

TensorFlow: A scalable library for building deep learning models used in production-grade applications.

PyTorch: A flexible, research-friendly framework known for intuitive model building and fast experimentation.

Scikit-Learn: A beginner-friendly library offering tools for classification, regression, clustering, and preprocessing.

Keras: A high-level API that simplifies deep learning workflows and speeds up model prototyping.

XGBoost: A powerful library for gradient boosting that excels in competitions and structured data tasks.

LightGBM: A fast, efficient boosting framework ideal for large datasets and high-performance ML pipelines.

Hugging Face Transformers: A leading library for NLP, enabling state-of-the-art text, speech, and vision models.

Final Insight: Mastering these libraries builds a strong ML foundation and prepares you for future AI advancements.

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