They save us from having to write everything from scratch, which is great because who has time for that?
One popular library is NumPy, which can handle linear algebra, Fourier transforms, and more. It’s faster and easier to use than most other Python libraries, making it ideal for machine learning projects. For example, let’s say you want to create a model that predicts the price of a house based on its size and location. You could use NumPy to manipulate the data and improve your model’s performance.
Another popular library is Scikit-learn, which supports most classic supervised and unsupervised learning algorithms. It can also be used for data mining, modeling, and analysis. This makes it a great choice if you’re new to machine learning or just want an easy-to-use library that doesn’t require too much coding knowledge.
Pandas is another Python library that’s built on top of NumPy and can prepare high-level data sets for machine learning projects. It relies on two types of data structures, one-dimensional (series) and two-dimensional (DataFrame), which makes it applicable in a variety of industries like finance, engineering, and statistics.
If you’re interested in deep learning models, TensorFlow is the way to go. This open-source library specializes in differentiable programming, meaning it can automatically compute a function’s derivatives within high-level language. Both machine learning and deep learning models are easily developed and evaluated with TensorFlow’s flexible architecture and framework.
Seaborn is another Python library that’s based on Matplotlib but features Pandas’ data structures. It generates plots of learning data, making it an effective choice if you also use it for marketing and data analysis. Theano is a Python library that focuses on numerical computation and can optimize and evaluate mathematical models and matrix calculations using multi-dimensional arrays to create ML models.
Keras is designed specifically for developing neural networks for machine learning applications. It runs on top of Theano or TensorFlow to train neural networks, making it flexible, portable, user-friendly, and easily integrated with multiple functions. PyTorch is an open-source library based on the C programming language framework Torch that’s mainly used in ML applications involving natural language processing or computer vision.

So basically, Python libraries for machine learning are collections of modules that contain useful codes and functions to simplify building machine learning models. NumPy can handle linear algebra, Fourier transforms, and more, making it ideal for machine learning projects. Scikit-learn supports most classic supervised and unsupervised learning algorithms and is great for beginners or those who don’t want too much coding knowledge. Pandas prepares high-level data sets for machine learning projects and can be used in finance, engineering, and statistics. TensorFlow specializes in differentiable programming and is ideal for deep learning models. Seaborn generates plots of learning data and is effective for marketing and data analysis. Theano optimizes and evaluates mathematical models using multi-dimensional arrays to create ML models. Keras runs on top of Theano or TensorFlow to train neural networks, making it flexible, portable, user-friendly, and easily integrated with multiple functions. PyTorch is an open-source library based on the C programming language framework Torch that’s mainly used in ML applications involving natural language processing or computer vision.