Best Python Libraries for Machine Learning in 2025

Introduction

Python is the most widely used programming language for machine learning (ML) and artificial intelligence (AI) due to its vast ecosystem of libraries. Whether you’re working on deep learning, supervised learning, unsupervised learning, or reinforcement learning, Python has specialized libraries to streamline model development.

In this tutorial you will learn about the best Python libraries for machine learning, comparing their features, use cases, and how to install them. You’ll also learn about lightweight vs. deep learning libraries, and trade-offs between TensorFlow, PyTorch, and Scikit-learn.

Why Use Python for Machine Learning?

Python has emerged as the preferred language for machine learning due to its unique combination of features that facilitate the development and deployment of AI models. The key factors contributing to Python’s popularity in machine learning are:

• Extensive Libraries: Python offers a wide range of pre-built machine learning frameworks, such as TensorFlow, PyTorch, and Scikit-learn, which simplify the process of model development by providing pre-implemented algorithms and tools. These libraries enable developers to focus on building models rather than starting from scratch.

• Ease of Use: Python’s simple syntax and readability make it an ideal language for rapid prototyping and experimentation. This ease of use allows data scientists and machine learning engineers to quickly test and validate their ideas, accelerating the development process.

• Scalability: Python’s versatility enables it to support both small-scale experiments and large-scale, enterprise-level AI applications. Whether you’re working on a proof-of-concept or deploying a model in production, Python’s scalability ensures that it can handle the demands of your project.

• Active Community: Python’s machine learning community is highly active, with extensive documentation, tutorials, and GitHub repositories available for various libraries and frameworks. This community support ensures that developers can easily find resources and assistance when needed, reducing the barriers to entry and accelerating project timelines.

For an introduction to Python programming, check out our Python Tutorial.

Top Python Libraries for Machine Learning

Here are some of the most widely used Python libraries for ML, categorized by their use cases:

Let’s learn how to implement each of these in Python.

TensorFlow

TensorFlow is a powerful open-source library for machine learning and deep learning. It is best suited for production models and is known for its high performance, scalability, and support for TPU/GPU.

To install TensorFlow, you can use pip:

pip install tensorflow

Here’s an example of using TensorFlow:

import tensorflow as tf
from tensorflow import keras

# Load the fashion mnist dataset
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

# Preprocess the data
train_images = train_images / 255.0
test_images = test_images / 255.0

# Build the model
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10)
])

# Compile the model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# Train the model
model.fit(train_images, train_labels, epochs=10)

# Evaluate the model
test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
print('\nTest accuracy:', test_acc)

This sample example demonstrates using TensorFlow to train a simple neural network on the Fashion MNIST dataset. The Fashion MNIST dataset is a collection of images of clothing items, and the goal is to classify these images into one of ten categories.

Here’s a step-by-step breakdown of what the code does:

1. Loads the Fashion MNIST dataset, which is a built-in dataset in TensorFlow.
2. Preprocesses the data by normalizing the pixel values to be between 0 and 1.
3. Builds a simple neural network model using the Sequential API in Keras. The model consists of three layers: Flatten, Dense, and Dense.
4. Compiles the model by specifying the optimizer, loss function, and evaluation metric.
5. Trains the model using the training data for 10 epochs.
6. Evaluates the model using the testing data and prints the test accuracy.

This example is a basic demonstration of how to use TensorFlow to train a neural network on a classification problem. It can be used as a starting point for more complex machine learning tasks.

PyTorch

PyTorch is another popular library for deep learning and research. It is known for its dynamic computation graphs and easy debugging.

To install PyTorch, you can use pip:

pip install torch

Here’s an example of using PyTorch:

import torch
import torch.nn as nn
import torch.optim as optim

# Define a simple neural network
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc = nn.Linear(10, 1)

    def forward(self, x):
        return self.fc(x)

# Create an instance of the network
net = Net()

# Define a loss function and an optimizer
criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=0.01)

# Create some random data
input = torch.randn(1, 10)
target = torch.randn(1, 1)

# Train the network
for epoch in range(100):
    optimizer.zero_grad()
    output = net(input)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()

In this example, we define a simple neural network, create some random data, and train the network using a simple optimization algorithm.

Scikit-learn

Scikit-learn is a traditional machine learning library that is best suited for tasks like classification, regression, and clustering. It is known for its simple API, built-in models, and feature engineering capabilities.

To install Scikit-learn, you can use pip:

pip install scikit-learn

Here’s an example of using Scikit-learn for a simple classification task:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load the iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)

# Predict on the test set
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy}")

In this example, we first load the iris dataset, which is a classic multi-class classification problem.

We then split the dataset into training and testing sets. Next, we initialize and train a logistic regression model on the training set. After training, we use the model to predict the labels for the test set. Finally, we evaluate the model’s performance by calculating its accuracy on the test set.

Keras

Keras is a high-level deep learning API that works with TensorFlow. It is known for its ease of use and is great for prototyping.

To install Keras, you can use pip:

pip install keras

Here’s an example of using Keras for a simple neural network:

from keras.models import Sequential
from keras.layers import Dense

# Define the model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# Compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

This example defines a simple neural network with two hidden layers and a binary output layer. It then compiles the model with a binary cross-entropy loss function and the Adam optimizer.

XGBoost

XGBoost is a library for boosted decision trees and is efficient for structured data. It is known for its high accuracy and efficiency.

To install XGBoost, you can use pip:

pip install xgboost

Here’s an example of using XGBoost for a classification task:

import xgboost as xgb
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load the iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Convert the data into DMatrix format
dtrain = xgb.DMatrix(X_train, y_train)
dtest = xgb.DMatrix(X_test, y_test)

# Define the parameters
params = {
    'max_depth': 3,
    'eta': 0.1,
    'objective': 'multi:softmax',
    'num_class': 3
}

# Train the model
bst = xgb.train(params, dtrain, 10)

# Make predictions
preds = bst.predict(dtest, output_margin=True)

This example loads the iris dataset, splits it into training and testing sets, and trains an XGBoost model for classification. It then makes predictions on the test set.

LightGBM

LightGBM is a library for gradient boosting and is optimized for speed. It is known for being faster than XGBoost.

To install LightGBM, you can use pip:

pip install lightgbm

Here’s an example of using LightGBM for a regression task:

import lightgbm as lgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

# Load the boston housing dataset
boston = load_boston()
X = boston.data
y = boston.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create datasets
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

# Define the parameters
params = {
    'objective': 'regression',
    'metric': 'l2',
    'verbosity': -1,
    'boosting_type': 'gbdt'
}

# Train the model
gbm = lgb.train(params,
                lgb_train,
                valid_sets=lgb_eval,
                early_stopping_rounds=5)

# Make predictions
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)

This example loads the boston housing dataset, splits it into training and testing sets, and trains a LightGBM model for regression. It then makes predictions on the test set.

OpenCV

OpenCV is a library for computer vision and is great for tasks like image and video processing.

To install OpenCV, you can use pip:

pip install opencv-python

Here’s an example of using OpenCV to read and display an image:

import cv2

# Load the image
img = cv2.imread('path/to/your/image.jpg')

# Display the image
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

This example loads an image using OpenCV and displays it on the screen.

Hugging Face Transformers

Hugging Face Transformers is a library for natural language processing (NLP) and is known for its pre-trained transformer models.

To install Hugging Face Transformers, you can use pip:

pip install transformers

Here’s an example of using Hugging Face Transformers for text classification:

from transformers import pipeline

# Load the pipeline
classifier = pipeline('sentiment-analysis')

# Example text
text = "I love this product!"

# Classify the text
result = classifier(text)

print(result)

This example loads a pre-trained sentiment analysis model from Hugging Face Transformers and uses it to classify a piece of text.

AutoML (Auto-sklearn, TPOT)

AutoML libraries like Auto-sklearn and TPOT are great for automated model selection, hyperparameter tuning, and pipeline automation.

To install Auto-sklearn, you can use pip:

pip install auto-sklearn

To install TPOT, you can use pip:

pip install tpot

Here’s an example of using Auto-sklearn for automated model selection and hyperparameter tuning:

from autosklearn.regression import AutoSklearnRegressor
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_boston

# Load the boston housing dataset
boston = load_boston()
X = boston.data
y = boston.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the AutoSklearnRegressor
reg = AutoSklearnRegressor(time_left_for_this_task=120, per_run_time_limit=30)
reg.fit(X_train, y_train)

# Make predictions
y_pred = reg.predict(X_test)

This example loads the boston housing dataset, splits it into training and testing sets, and uses Auto-sklearn to automatically select and tune a regression model. It then makes predictions on the test set.

Stable Baselines3, RLlib

Stable Baselines3 and RLlib are libraries for reinforcement learning and are known for their optimized RL agents.

To install Stable Baselines3, you can use pip:

pip install stable-baselines3

To install RLlib, you can use pip:

pip install ray[rllib]

Here’s an example of using Stable Baselines3 for reinforcement learning:

from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env

# Create a vectorized environment
env = make_vec_env('CartPole-v1', n_envs=4)

# Initialize the model
model = PPO('MlpPolicy', env, verbose=1)

# Train the model
model.learn(total_timesteps=25000)

This example creates a vectorized environment for the CartPole-v1 task, initializes a PPO model, and trains it for 25,000 timesteps.

Supervised vs Unsupervised Learning

Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data. The goal is to learn a mapping between input data and the corresponding output labels, so the model can make predictions on new, unseen data. Supervised learning is used for tasks such as image classification, speech recognition, and sentiment analysis.

Some popular Python libraries for supervised learning are:

• Scikit-learn: Provides a wide range of algorithms for classification, regression, clustering, and more.
• TensorFlow: Supports both shallow and deep learning models for supervised learning tasks.
• PyTorch: Offers a dynamic computation graph and is particularly well-suited for deep learning models.

Unsupervised Learning

Unsupervised learning is a type of machine learning where the model is trained on unlabeled data. The goal is to identify patterns or structure within the data, such as clustering, dimensionality reduction, or anomaly detection. Unsupervised learning is used for tasks such as customer segmentation, recommender systems, and anomaly detection.

Some popular Python libraries for unsupervised learning are:

• Scikit-learn: Offers algorithms for clustering, dimensionality reduction, and density estimation.
• TensorFlow: Supports unsupervised learning models, including autoencoders and generative adversarial networks (GANs).
• PyTorch: Can be used for unsupervised learning tasks, such as autoencoders and GANs, due to its dynamic computation graph.

Lightweight vs. Deep Learning Libraries

When to use Lightweight Libraries?

If you’re working on structured data, regression, or classification tasks, lightweight libraries like Scikit-learn are ideal.

When to use Deep Learning Libraries?

If you’re dealing with computer vision, NLP, or reinforcement learning, deep learning frameworks like TensorFlow or PyTorch are better suited.

Trade-offs Between TensorFlow, PyTorch, and Scikit-learn

For a detailed comparison, read PyTorch vs. TensorFlow.

Emerging Libraries for Specific ML Tasks in 2025

As machine learning continues to evolve, new libraries are emerging to tackle specific tasks and domains. Here are some notable examples:

• Hugging Face Transformers: This library has revolutionized the field of natural language processing (NLP) by providing pre-trained models and a simple interface for a wide range of NLP tasks.
• Optuna: A Bayesian optimization library that simplifies hyperparameter tuning for machine learning models.
• PyTorch Geometric: A library for geometric deep learning, particularly useful for tasks involving 3D data, computer vision, and robotics.
• PyTorch Lightning: A high-level framework that simplifies the process of building, training, and deploying PyTorch models.
• TensorFlow Quantum: A library that integrates TensorFlow with quantum computing, enabling the development of quantum machine learning models.
• PyTorch Serve: A model serving library that streamlines the deployment of PyTorch models in production environments.

FAQs

1. Which is better: TensorFlow or PyTorch?

Both TensorFlow and PyTorch are powerful deep learning frameworks, and the choice between them depends on your specific needs and preferences. Here’s a comparison of the two:

TensorFlow is known for its high performance, scalability, and support for production models, making it a popular choice for large-scale deep learning applications. PyTorch, on the other hand, is praised for its dynamic computation graphs, ease of debugging, and flexibility, making it a favorite among researchers and developers working on cutting-edge deep learning projects.

2. How do I install Scikit-learn in Python?

To install Scikit-learn in Python, you can use pip, the Python package manager. Open a terminal or command prompt and run the following command:

pip install scikit-learn

This will install Scikit-learn and its dependencies.

3. What Python libraries are used for deep learning?

Some popular Python libraries for deep learning are TensorFlow, PyTorch and Keras. TensorFlow and PyTorch are both low-level frameworks that provide a high degree of control over the model architecture and training process. Keras, on the other hand, is a high-level API that provides an easier interface for building deep learning models, especially for those new to deep learning.

4. Can I use multiple ML libraries in one project?

Yes, it is common to use multiple machine learning libraries in a single project. For example, you might use Scikit-learn for data preprocessing and feature engineering, TensorFlow or PyTorch for building and training deep learning models, and OpenCV for computer vision tasks. The choice of libraries depends on the specific requirements of your project and the strengths of each library.

5. What are the best libraries for NLP and reinforcement learning?

For natural language processing (NLP), some of the best libraries are Hugging Face Transformers, NLTK, and spaCy. Hugging Face Transformers provides pre-trained models and a simple interface for a wide range of NLP tasks, while NLTK and spaCy offer tools for text processing, tokenization, and language modeling.

For reinforcement learning, popular libraries include Stable Baselines3, RLlib, and Gym. These libraries provide optimized reinforcement learning agents, environments, and tools for training and evaluating RL models.

Conclusion

Python provides a rich ecosystem of machine learning libraries, from deep learning frameworks like TensorFlow and PyTorch to lightweight tools like Scikit-learn. Choosing the right library depends on the task—whether it’s supervised learning, NLP, or hyperparameter tuning etc.

For further reading, check out:

1. How to Import Modules in Python.

2. PyTorch vs. TensorFlow.