The Best Python Libraries for Machine Learning and AI: Features & Applications

Many Python libraries are used for machine learning. Some of the most widely used libraries include Scikit-learn (or Sklearn) for simple and traditional tasks; TensorFlow and PyTorch; Keras as a high-level neural networks API; Pandas for data manipulation; NumPy for numerical operations; and Matplotlib/Seaborn for data visualization.

While NumPy was not designed specifically for machine learning, it is commonly used in ML projects. It is a foundational library used for numerical operations in Python. NumPy supports large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these elements.

While it ultimately depends on your business needs, PyTorch has gained more popularity than TensorFlow and offers several advantages. This includes a dynamic computational graph that facilitates intuitive model development and debugging. Its Pythonic and user-friendly API makes it accessible for researchers and developers, while its popularity in the research community ensures a wealth of cutting-edge models and resources. PyTorch's flexibility and ease of use make it an excellent choice for prototyping and experimentation in machine learning projects. However, TensorFlow is still used in extensive projects with big deployment requirements.

You can think of TensorFlow as the superhero for advanced jobs, while Scikit-learn is the friendly guide for basic tasks. TensorFlow is a comprehensive platform for crafting and honing complex neural networks, well-suited for handling hefty datasets and ensuring scalability in deployment. Its versatility spans multiple domains, from healthcare to finance, and from image and speech recognition to NLP tasks. On the other hand, Scikit-learn (Sklearn) is great for simpler tasks when your information is well organized. Sometimes, people use both for different parts of their projects.