Python is an incredibly popular programming language for machine learning, AI and data science in general, which makes it all the more important to have an updated list of the best libraries available to help you do your job well. If you’re ready to get started, here are the top 10 Python machine learning libraries to watch out for in 2022. (The top 10 today are listed in no particular order.)

1) Scikit Learn

Scikit-learn is a machine learning library that focuses on data mining, predictive analytics and data science. It's an open source project written in the Python programming language.

2) Pandas

Pandas is a data exploration and analysis library that is built on the Numpy extension of the Python programming language. It provides a robust and efficient set of functions to perform various tasks like: 

- Data manipulation - Data indexing and slicing - Statistical analysis - Plotting data - Resampling methods - Data cleaning and imputation

3) Numpy

Numpy is a library for scientific computing in Python that features NumPy arrays, linear algebra, Fourier transforms, and more. It is used extensively with the scientific Python stack.

4) Seaborn

Seaborn is one of the most popular Python libraries for data visualization and statistical analysis. Seaborn offers a high-level interface to help you explore and visualize your data. It also enables you to integrate many different types of statistical analysis, including linear regression, PCA, clustering, k-means clustering, ANOVA, smoothing, differential expression tests (MANOVA), and more. With Seaborn you can produce publication quality plots with just a few lines of code!

5) Matplotlib

Matplotlib is a python 2D plotting library which produces publication-quality figures in a variety of formats. It can be used in python scripts, the python and ipython shell, web application servers, and various graphical user interface toolkits. Matplotlib can be used to create line plots, histograms, scatter plots, 3D surface plots and contour plots.

6) Statsmodels

Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. Statsmodels can be used as a complement to SciPy/NumPy or can replace them. It offers a wide range of descriptive statistics (including time series analysis), linear regression, nonlinear regression, classical parametric statistics, Bayesian methods, bootstrapping and Monte Carlo simulation.

7) Keras

Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key when doing machine learning research. Keras has the convenience of working with symbolic tensors and being able to seamlessly switch between CPU and GPU computation.

8) SciPy

SciPy is a set of open-source libraries that provide functionality for numerical computation, visualization, and other scientific tasks. SciPy includes modules to do data analysis, optimize code, etc. The most popular module is NumPy, which provides fast N-dimensional array manipulation.

9) TensorFlow

TensorFlow is an open-source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. Originally developed by the Google Brain team within Google's AI organization, it was made available as open source software and has since been adopted by organizations all over the world. TensorFlow is a part of a family of machine learning tools based on data flow graphs.

10) PyBrain

PyBrain is a machine learning library written in Python. It provides various algorithms that can be used to make predictions from data. PyBrain gives you the option of writing your own machine learning algorithm or choosing one that has already been created. In addition, it also has methods to deal with missing data and noise reduction. If you're looking for a customizable machine learning library, then PyBrain might be the right choice for you.