10 Must-Know Python Libraries for Machine Learning in 2024

As we progress through 2024, machine learning (ML) continues to evolve at a rapid pace. Python, with its rich ecosystem of libraries, remains at the forefront of ML development. In this post, we’ll explore the top 10 Python libraries dominating the ML scene in 2024, how the field has changed since 2020, and the key trends that have emerged.

The Top 10 Python Libraries for Machine Learning in 2024

Core ML and Deep Learning Frameworks

1. TensorFlow: Google’s open-source library for deep learning and neural networks.
2. PyTorch: Facebook’s flexible deep learning platform known for its dynamic computational graphs.
3. scikit-learn: A versatile library for classical machine learning algorithms and data mining.
4. Keras: High-level neural networks API, now integrated with TensorFlow.
5. Other Notable Libraries: XGBoost, LightGBM, JAX, FastAI, PyCaret

Data Manipulation and Numerical Computing

1. NumPy: The fundamental package for scientific computing with Python.
2. Pandas: Powerful data manipulation and analysis library.
3. Equally Important: SciPy, Dask

Visualization and Plotting

1. Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations.
2. Hugging Face Transformers: State-of-the-art natural language processing models and tools.
3. NLTK: Comprehensive suite of libraries and programs for symbolic and statistical natural language processing.
4. spaCy: Industrial-strength natural language processing library.

Worth Mentioning: Optuna for hyperparameter optimization

Data scientists and machine learning engineers can develop a comprehensive toolset that can address a variety of machine learning difficulties by becoming adept with libraries from these areas. Although concentrating on the top ten libraries will cover the majority of use cases, getting to know the additional libraries mentioned can give you access to specialized tools that will further improve your machine learning skills.

This carefully chosen collection of libraries is intended to broaden your machine learning arsenal and keep you proficient at the forefront of the field for data scientists of all skill levels. These trends are likely to continue influencing the Python machine learning ecosystem in the future, with an emphasis on increasing the accessibility of powerful ML techniques, enhancing their performance and scalability, and adjusting to new paradigms in AI research.