10 Best Python Libraries for Machine Learning in 2025

An overview of machine learning

Machine learning is a field in artificial intelligence that deals with using data to train computers to act, behave like humans, and automate human processes. It can also be said to be a subfield of data science since data is gathered, cleaned, and visualized to help in building efficient machine learning models. There are several systems created with machine learning that are used in image classification, computer vision, natural language processing, chatbots, etc.

Machine learning can be divided into three types: supervised, unsupervised, and reinforcement learning.

Supervised machine learning

In supervised machine learning, there is the presence of a target feature in a dataset that we use to derive the rest of the features. This is the feature that the computer will learn in order to predict and create patterns.

There are two types of supervised machine learning: regression and classification. In regression, the target feature is a continuous variable, whereas in classification, the target is a feature with two or more classes that can be obtained after training the model.

Unsupervised machine learning

There is no target feature in unsupervised machine learning. Instead, the computer finds connections between the features and groups them into classes based on their similarities. The only type of unsupervised learning is clustering.

Reinforcement learning

In reinforcement learning, machines tend to learn based on data and the environment. They eventually make decisions based on these and interact with the environment. This type of learning can be seen in the development of robots and AI games like chess.

Top 10 Python machine learning libraries

Python is one of the easiest and most used programming languages for developing AI and ML models. The use of Python machine learning libraries varies from data storage and manipulation to visualization and model development.

The following is a list of some of the best Python libraries for AI and ML.

NumPy

NumPy is an open-source Python machine learning library developed by Travis Oliphant in 2005. It stands for Numerical Python (Num-Py) since it contains a lot of numerical operations that are simplified for use.

NumPy helps in storing and editing data in an n-dimensional array and deriving statistical insights for data science and machine learning. It can create arrays of dimensions greater than 1 and cannot create negative dimension arrays. It also creates opportunities to perform linear algebra and matrix calculations more easily.

The arrays are written in C programming language and are generally faster to retrieve information and alter data. They consume less memory than normal Python lists which enables them to store more than the amount of data that Python lists can. They can also store only homogenous data. These are some of the reasons why NumPy is a preferred data structure over normal Python lists.

NumPy can be installed by typing the command in a notebook or command line interface:

Pip install NumPy 

Or using Anaconda:

Conda install NumPy

In short form, it is normally imported as np:

Import NumPy as np

Pandas

Pandas is an open-source machine learning library in Python created by Wes McKinney in 2008. It was built and integrated with the NumPy package. It can be used to create series and data frames that aid data science in aspects like data cleaning, data analysis, data formatting, etc.

Pandas stores data in two forms: series and data frames. Series are very similar to NumPy arrays as NumPy is integrated into Pandas. However, they are different in that they can be used to store lists of heterogeneous data types. They can define column index explicitly, i.e., set their index values which can be used to access the particular data on the list. Think of them as a spreadsheet column and index values.

Data frames are similar to spreadsheet packages. They are two-dimensional arrays that store homologous and heterogeneous data. They can also store data in rows and columns where each column contains a particular data type.

Data from spreadsheet packages can be converted to data frames and can automate a lot of processes. They provide easier and more flexible tools to locate, edit, and perform operations on data.

Pandas can be installed by typing the command in a notebook or command line interface:

Pip install pandas

Or using Anaconda:

Conda install pandas

It is normally imported as pd:

Import pandas as pd

Matplotlib

Matplotlib is an open-source Python library conceived by John Hunter in 2002. It helps in graph plot creation, visualization of data, and machine learning model performance. It’s a very useful tool in data science and machine learning as it's versatile and helps in gaining insights from data and models using graphs.

Matplotlib is a visualization tool built on top of NumPy. Over the years, it has been used for analytics and machine learning to plot suitable visuals to understand data and models, and determine accuracy. It is quite complex, which is one of the reasons why Seaborn was developed - to make visuals easier and faster. Nevertheless, it's still a great plotting tool.

It can be installed by typing the command in a notebook or command line interface:

Pip install matplotlib

Or using Anaconda:

Conda install matplotlib

In short form, it is normally imported as plt.

Import matplotlib.pyplot as plt.

Seaborn

Created by Michael Waskom in 2012, Seaborn has become widely used today. It is another very useful visualization tool, built and integrated with Matplotlib. It creates extremely clear visuals and is easy to use in data visualization, correlation, and seeing how well a model’s performance works on the test set.

Seaborn graphics are more understandable than Matplotlib’s which makes it very reliable and easy to derive insights from data.

To use Seaborn, you need to have the current version of Matplotlib.

It can be installed by typing the command in a notebook or command line interface:

Pip install seaborn

Or using Anaconda:

Conda install seaborn

In short form, it is normally imported as sns.

Import seaborn as sns

Scikit-learn

Designed by David Cournapeau as a Google Summer of Code project in 2007, Scikit-learn or sklearn is a popular Python machine learning library that contains tools to help train different models. It has a large variety of inbuilt models that can carry out classification, regression, and clustering techniques.

Sklearn is designed primarily for prediction analytics. After data is cleaned and processed, it is separated into a training set and a test set. The training set is used to train the model using its algorithm and is then evaluated on how it performs on the test data. With this, millions of Python and machine learning models are designed.

It can be installed by typing the command in a notebook or command line interface:

Pip install sci-kit learn

Or using Anaconda:

Conda install sci-kit learn

PyTorch

PyTorch is among the most popular libraries. It was created in 2016 by Meta AI with the team consisting of Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, and others.

PyTorch focuses primarily on building and training deep learning models. It contains several tools to create accurate neural network models and produce useful AI programs in Python.

It is mainly applied for deep learning purposes as it is easy to use and test model prototypes before deployment with the help of tensors - arrays of data accelerated or increased in speed by the GPU.

It can be installed by typing the command in a notebook or command line interface:

Pip install torch

Or using Anaconda:

Conda install torch

TensorFlow

TensorFlow is a machine learning library in Python designed by the Google Brain team in 2015. It contains tools to help in the training of machine learning models and building deep learning models with the use of in-built tensors to generate accuracy.

It is one of the leading libraries in deep learning and artificial intelligence systems. With it, deep learning models are pushed into production. It is often compared to PyTorch in the use of deep learning model development.

It can be installed by typing the command in a notebook or command line interface:

Pip install TensorFlow

Or using Anaconda:

Conda install TensorFlow

In short form, it is normally imported as tf.

Import TensorFlow as tf

Keras

Keras is a deep learning library integrated with the TensorFlow library. It was designed by Google engineer, Francois Chollet, in 2015. It was built specifically for training deep learning models using neural networks.

Keras is a widely used package and works with the TensorFlow library to build efficient models. It makes model development smoother and faster using its API.

It can be installed by typing the command in a notebook or command line interface:

Pip install Keras

Or using Anaconda:

Conda install Keras

NLTK

NLTK (Natural Language Toolkit) was designed by Steven Bird, Edward Loper, and others in 2001. It is a package to train computers to learn a natural human language like English. It is used to create chatbots, sentiment analysis models, etc., which gives a computer the ability to process and understand human language.

NLTK provides libraries to remove stop words and punctuations, then converts a sequence of words into arrays so that it can be understood by the computer. Once done, it follows and learns a regular pattern that yields a classification model and makes predictions accurately. It is widely used in automation systems and customer care to assist people and collect data.

It can be installed by typing the command in a notebook or command line interface:

Pip install nltk

Or using Anaconda:

Conda install nltk

Open CV

OpenCV or Open Source Computer Vision Library is another popular machine learning library. It specializes in giving computers the ability to recognize images, segment them, and use them for commercial purposes.

The computer converts images into an array of a given size using the RGB scheme and learns from multiple images to correctly identify a given image. This way, it learns and groups each of these arrays into classes given as the data. It uses neural networks to create a pattern in them which makes it possible to use the computer's camera to analyze objects and correctly scan and identify what or who they are.

OpenCV is a groundbreaking Python machine learning library as it gives computers the power to visualize. This makes it possible to design facial recognition systems, fingerprint recognition systems, and so on.

It can be installed by typing the command in a notebook or command line interface:

Pip install OpenCV

Or using Anaconda:

Conda install OpenCV

The Python machine learning libraries listed here are some of the most useful that are used to process data, clean it, derive insights from it, and build recognizable models to help in business, commerce, medicine, and other industries. Today, machine learning is a high-grossing field in the technology industry, especially with the development of upcoming libraries to build and automate more models. Try these top 10 choices and see how positively they impact your machine learning projects.