12 Best Python Libraries for Machine Learning in 2024

Last Updated on December 1, 2023

Quick Summary:

Python libraries for machine learning are a must-have requirement for enabling any task or streamlining any processes. Machine learning is new-age technology, and accompanying Python makes it potent and capable of executing the most complex tasks in a simplified way. The list of Python libraries for ML is explicitly researched and selected after considering their popularity, usage, and use cases.

Introduction

If you want to give life to your Machine Learning project, Python is your go-to code. IBM’s machine learning technology considers Python the top-most programming language for ML and AI.

For instance, IBM’s CodeFlare uses Python to simplify integration and scalability and deploy machine learning pipelines.

The Machine Learning python library has specific modules for developers to figure out their Machine Learning requirements. Searching for the best python libraries for machine learning is time-consuming if you haven’t worked closely with ML. A certain level of familiarity is required to import the modules which are suitable for your project.

Here are the 10 Best Python Libraries for Machine Learning you should know in 2024.

Best Python Libraries for Machine Learning

1. TensorFlow Python:

TensorFlow Python library was built by Google, primarily for internal use. Later on, considering this library’s potential capabilities, it was also made available for external use.

TensorFlow has won the hearts of Data Scientists and Researchers due to its scalability, versatility, compatibility, and extensive ecosystem. Other reasons that make the Tensorflow library popular are:

Developing, training, and deploying machine learning models
Flexibility with deep, convolutional, and recurrent neural networks
Allows unveiling complex computations in simple data graphs

What is TensorFlow capable of?

Efficient computation: Allows users to leverage parallel computing by dividing work into multiple CPUs or GPUs.
Neural network building blocks: Ensures offering of built layers, functions to activate, and loss functions to build neural networks.
Model visualization: Allows users to leverage TensorBoard for interactive visualization of training metrics and model graphs.
Deployment flexibility: Allows deploying TensorFlow models on platforms like mobile, web, and cloud environments for easy scalability.

What can you implement with TensorFlow?
TensorFlow machine learning for Python brings a lot of possibilities, such as:

Medical imaging, object recognition, facial recognition, and QA in manufacturing
Text classification, language translation, sentiment analysis
Translations and speech recognition
Implement recommendation systems

Well-known Applications using TensorFlow Python

Google Voice Search
Google Photos

Example of TensorFlow Python-

#  Program using TensorFlow
#  for adding two arrays
  
import tensorflow
  
# Initializing constants
x = tensorflow.constant([2, 4, 6])
y = tensorflow.constant([1, 3, 5])
  
# Addition
res = tensorflow.add(x, y)
  
# Initializing Session
sess = tensorflow.Session()
  
# Result
print(sess.run(res))
  
# Closing Session
sess.close()

2. Keras

Keras is considered when developing and evaluating neural networks within deep and machine-learning models. With just a little coding, Keras becomes fully functional to train neural networks.

Keras is great for beginners due to its easy learning curve. Its features include:

Integrating with layers, optimizers, activation functions, and objectives
Modular, extensible, and flexible
Portability and flexibility, ensuring it works across environments
Agile prototyping

What is Keras capable of?

GPU Acceleration & Distributed Computing: Allows leveraging the potential of GPU for boosting training and inference.
Model Evaluation & Metrics: Evaluates performance of trained models and offers common tools for calculating precision, recall, and F1 score.
Preprocessing & Data Augmentation: Offers image data handling functions, including normalization, resizing, and data augmentation techniques like cropping, flipping, and rotation.
Personalization: Create your own neural network models by implementing custom loss functions and activation functions.

What can you implement with Keras?
Keras ML library ensures the following things you can implement:

Sequence Modeling: Enables time series evaluation, music, and speech recognition.
Generative Models: Streamlining data augmentation, generating creative content, and image synthesis tasks.
Object Detection: Allows Faster R-CNN, SSD, and YOLO implementation for object detection models.
Image Classification: Allows using architecture like VGG16, ResNet, or custom architecture for handling images across classes like identifying objects in photographs.

Well-known applications using Keras

Uber
Netflix

Keras is one of the best Python machine-learning libraries for neural networks due to its wide range of potential functionalities.

Our highly skilled and motivated Python developers have a knack to crack complex project models.
Hire Python developer from us to accelerate your Machine Learning projects today!

3. PyTorch

Developed by Facebook’s AI research team, this Python library for machine learning and deep learning allows for developing neural networks and conducting DL research. Considering the compilation of dynamic computation graphs and GPU acceleration, PyTorch becomes one of the go-to Python libraries for practitioners, the DL community, and researchers.

What is PyTorch capable of?

Tensor Computation: Allows to execute operations on multi-dimensional arrays and bolsters broadcasting and slicing.
Distributed Computing: You can scale deep learning models to extended datasets. Also, it offers data loading, distributed optimization, and communication.
Model Deployment: Offers tools for deployment and integration into environments.

What can you implement with PyTorch?

Reinforcement learning: Integrate with reinforcement learning libraries for training agents software robots to seamlessly interact with environments and solve tasks like game playing, robotics control, and decision-making.
Transfer learning: Allows leveraging pre-trained models like ResNet, BERT, or GPT to fine-tune for particular tasks.

Well-known Applications Using PyTorch

Apple
Samsung Electronics

PyTorch’s GPU acceleration, wide community support, extensive neural network support, and dynamic nature is the result of its popularity for prototyping and deployment.

4. Scikit-Learn

Scikit-Learn is a Python library for machine learning that implements classification, regression, clustering, dimensionality reduction, and model selection.

This Python library ensures an intuitive user-interface for all ML tasks. Unlike other libraries, this one is simple, efficient, and reusable, which is the best for newbies and professionals.

What is Scikit-Learn capable of?

Data Preprocessing: Offers functionalities for managing missing values, handling data, encoding categorical variables, and feature extraction technique.
Supervised learning: Allows models to learn from labeled data for making predictions.
Unsupervised learning: Allows finding patterns, relationships, or structures with unlabeled data.

What can you implement with Scikit-Learn?

Classification: Allows classifying data or predicting class labels using algorithms like gradient boosting methods and random forests.
Regression: Predict target variables and implement linear regression, decision tree regression, polynomial regression, and random forest regression.
Clustering: Allows leveraging clustering algorithms to identify patterns and classify identical data points without class labels.
Dimensionality reduction: Allows dimensionality using different techniques.

Well-known Applications using Scikit-Learn

Spotify
Inria

5. Matplotlib

Matplotlib is explicitly built for visualizing data using set of tools that can create static, animated, and interactive visualizations. With this data visualization library by Python, you can view data in plots, charts, histograms, scatter plots, and bars.

It is also not directly related to ML, just like Pandas. Matplolib is considered a convenient tool utilized when developers visualize data patterns. The primary usage of Matpolib is to produce 2-dimensional graphs and plots. Pyplot module makes plotting more convenient because it offers features and tools for controlling line styles, font properties, and many more.

What is Matplotlib capable of?

Line plots: Allows you to visualize relationship between two variables to see trends, patterns, and changes.
Bar plots: Allows you to showcase different bar sizes to compare data.
Pie charts: Represents percentage as a whole with personalization, including color and labels.
3D plots: Allows visualization of data in three-dimensional landscape.
Annotations & labels: Allows you to add annotations, texts, and labels.

What can you implement with Matplotlib?

Geographic Maps: Eensures you can visualize data of geographic region and allows to put pointers, lines, and polygons
Heatmaps: Allows visualizing data in 2D grid and create heatmaps for displaying correlation, matrices, or matrix like structure.
Histograms: Visualizes division of data into bins and showcasing count or frequency falling within each bin.

Matplotlib is widely used in finance, scientific research, machine learning, data analysis, and many such fields. This machine learning library for Python ensures you can visualize and understand data thoroughly, leaving all the confusion behind.

Example of Python Matpolib

#  Program to form a linear plot
  
# import packages & modules
import matplotlib.pyplot as matplot
import numpy 
  
# Initializing data
a = numpy.linspace(0, 4, 10)
  
# Plotting data
matplot.plot(a, a, label ='linear')
  
# Adding legend
matplot.legend()
  
# Showing plot
matplot.show()

Future-proof your Python library requirements so that you can focus more on your core business activities.

Consult Now

6. NumPy

NumPy (NUMerical PYthon) is a library used to process the Python NumPy array. It consists of highly complex mathematical functions that make NumPy powerful when dealing with substantial multidimensional matrices and arrays.

It is well-known for handling linear algebra and Fourier series transformations. A library like TensorFlow utilizes NumPy at the backend to manipulate tensors.

What is NumPy capable of?

Array creation & manipulation: Allows to create arrays of different shapes and sizess from lists, tuples, or already existing arrays.
Linear Algebra operations: Performs functions like matrix manipulation, matrix decomposition, and calculating eigencvalues.
Random number generation: Enables generation of random numbers for probablity distribution, which helps in simulating data, gathering random samples, and bootstrapping.

What can you implement with NumPy?

Data visualization: Enabling effective data exploration using NumPy helps businesses preprocess information.
Machine learning: Enhances performance of machine learning models and stores, trains, and tests data.
Data Manipulation: Preprocess and transform data, handle unrecorded figures, and restructure data

NumPy is a fundamental library for data analysis, modeling tasks, and numerical computation. For data driven decisions, this is one of the best python libraries for machine learning.

Example of NumPy-

#Sorting 2-D array
import numpy 

#Initializing 2-D array
arr = numpy.array([[11, 3], [21, 14]])

#Sorting array and printing output
print(numpy.sort(arr))

7. SciPy

SciPy (SCIentific PYthon) is an open-source Python for machine learning library that mainly focuses on scientific computing, which is concerned with engineering, math, and science. It has many similarities with the paid tool called MatLab. SciPy is one of the rich Python Machine Learning libraries for linear algebra, Fourier Transforms, specific functions, image processing, and many more.

What is SciPy capable of?

Integration & optimization: ensures solving complex problems, simulating systems, and optimizing parameters.
Sparse Matrix operation: This matrix are important to represent large datasets like network graphs, high dimensional data, and physical simulations
Open for integration with other libraries: Leverages the functionalities of other python libraries for machine learning like Matplotlib, Pandas, and TensorFlow.

What can you implement with SciPy?

High Performance: Ensures boost in performance with this python library for machine learning and enables handling large datasets & CPU-intensive tasks.
Platform dependencies: Runs across operating systems and works on any environment.
Interoperability: Compatible with other scientific computing libraries in Python to concrete libraries and make swift data exchange.

SciPy benefits the businesses most because of its high performance efficiency and extensive documentation. Besides, the wide community support and seamless integration are other perks you can reap with SciPy.

Example of Python SciPy-

#Finding Cubic Root 
from scipy.special import cbrt 

#Initializing 
res = cbrt([343, 1331])

#Print output
print(res)

8. Theano

Theano provides tools that define, execute, and optimize mathematical models and expressions with multi-dimensional arrays. To detect and diagnose various error types, utilizing Theano in unit-testing and self-verification is recommended. Theano is the most versatile Python AI library used for large-scale computing projects but comfortable and specific enough to be implemented by individuals in their projects.

What is Theano capable of?

GPU computing: Leverages graphic processing units for delivering high performance and trains large-scale neural networks using parallel execution on NVIDIA GPUs.
Symbolic express evaluation: Builds computational graph representing expressions and their dependencies to enhance the performance.
Deep learning: Makes development of neural networks swifter using Keras and DL capabilities are still in use

Cross-platform compatibility: Works on different operating systems and can also run on CPUs and GPUs.

What can you implement with Theano?

Stability optimization: Determines unsteady expression and then utilize steady expression to rectify the former ones.
Speed optimization: Leverages current GPU to implement its parts of expressions in your CPU or GPU.
Symbolic differentiation: Finds derivates of the function using symbols rather than numbers by manipulating the mathematical expressions and implementing the calculus rules.

Though all the limelight of being a potential Python library for machine learning has been taken over by TensorFlow and PyTorch, Theano still remains the first choice when it comes to numerical computation, symbolic mathematics, and deep learning.

Example of Theano Python-

# Program to computing a Logistic 
# Function
  
import theano as t
import theano.tensor as Th
a = Th.dmatrix('a')
b = 1 / (1 + Th.exp(-a))
logistic = t.function([a], b)
logistic([[0, 1], [-1, -2]])

Well-known applications using Theano

Zetaops
Vuclip

9. Pandas

Pandas is a well-known library used for extensive data analysis. We all know that preparing a dataset before training is the principal activity. Python Pandas provides high-level tools and data structures in this scenario. It was mainly developed for extracting and organizing data. In addition to this, it also offers inbuilt functions and methods to group, combine, and filter datasets.

What is Pandas capable of?

Data handling: Handles two types of data (Series & DataFrame) and offers comprehensive functions like reading, writing, and manipulating data across file formats.
Data analysis: Offers descriptive statistics, summary statistics, and data profiling to calculate statistics like mean, median, and mode.

High performance: Offers efficient array operations and optimizes algorithms for data manipulation.

What can you implement with Pandas?

Data preparation: Requires managing unrecorded values, encoding, and scaling to process data and prepare them in a streamlined manner.
Data integration: Enables easy data interchange between libraries and integrates them with other libraries.
Flexibility & efficiency: Offers data structures and optimized algorithms for swift data manipulation and enable vectorized operations to boost computations.

Pandas is one of the top Python libraries for machine learning enabling data analysis with accurate estimates. With Pandas, you can be sure of handling data, cleaning, preprocessing, analysis, and transformation tasks.

Example of Python Pandas-

# Program to implement 
# Pandas DataFrame  

import pandas 
 
data_set = {
“words_written” : [1450, 3450, 1340]
“hours” : [1, 2, 1]  
}
res = pandas.DataFrame(data_set, index = [“Monday”, “Tuesday”, “Wednesday”])
 
print(res)

10. Mlpack

MLPack focuses on ease-of-use, scalability, and speed. The key benefit of using the mplack library is to get an extensible, fast, and flexible way of implementing ML algorithms. Although meant for C++, bindings for mlpack is available for Go, Julia, Python, and R programming languages. It also features simple command-line programs and C++ classes integrated into large-scale ML solutions.

What is MLPack capable of?

Scalability: Leverages modern day computational techniques and parallel computing to male optimal use of hardware resources.
Integration: Allows users to utilize MLPack’s functions along with their programming environment.
Common-line interface: Allows users to leverage CLI through its terminal, enabling ML algorithms without hassle programming steps.
Documentation: Enough documentation for leveraging the library efficiently.

What can you implement with MLPack?

Recommender System: Allows building recommender systems to offers products to users based on their behavior on online platform.
Neural networks: Allows image classification, natural language processing, and sequence modeling.
Anamoly detection: Detects and prevents anomalies in the data and offer solutions

MLPack works from small to large applications and focuses on improving performance, scalability, and integrations. MLPack library for machine learning doesn’t compromise on user experience and the quality of the products.

11. Plotly

Plotly is a Python library for machine learning that enables the creation of interactive and dynamic visualizations. This library for ML allows you to create graphs, heat maps, and more.

What is Plotly capable of?

Interactive visualization: Allows you to add interactivity like zooming, panning, hovering, and clicking events to make the user experience intuitive and dig out more profound insights.
Multiple output formats: Allows you to generate visualizations across formats, enabling visualization on different platforms.
Personalization: Personalize visualization like colors, fonts, annotations, axes, and legends to match design preferences.
Collaboration: Allowing access to multiple users to work on exact visualization.

What can you implement with Plotly?
Plotly Python library is also similar to Matplotlib since it presents data in charts, heatmaps, and histograms.

Interactive dashboard: Enable building interactive dashboards to visualize data, filter them, and interact with real-time information
Candlestick charts: Allows creating candlestick charts that are mostly and widely used in the finance sector.
3D plots: This enables creating plots to visualize data in a 3D format using scatter, surface, and contour plots.

Plotly Python library for machine learning ensures data presentation and evaluation are interactive and understandable. Using Plotly, there won’t be any hassle in reading and interpreting data efficiently and effectively.

12. Beautiful Soup

Unlike Python libraries, Beautiful Soup is a Python package for machine learning, mainly leveraged for data gathering and web scraping. This Python package parses HTML documents and XML for preparing manipulation. With Beautiful Soup, you will be able to extract data from web pages in a convenient method using HTML/XML structure by accessing attributes and elements.

What is Beautiful Soup capable of?

Searching and filtering: Allows filtering and searching capabilities for finding particular elements/content within HTML documents.
Data extraction: Allows you to fetch data from elements like text, attributes, or tags. Access data using Python syntax and manipulate it for analysis..
Encoding detection and correction: Converts document to Unicode for handling multiple characters..
Error handling: Handles imperfect HTML/XML document that has missing tags, improper structure, and irregularities.

What can you implement with Beautiful Soup?

Text Mining: executes text mining tasks like sentiment analysis, language processing, and keyword extraction for social platform engagements, forums, and articles.
Web Content Analysis: Analyze the content structure of the web pages, like headings, titles, and timestamps.
XML Processing: Useful in managing, extracting, and manipulating XML data for varied use cases.

Beautiful Soup is a Python package for machine learning, which is advanced and does the most sluggish tasks in the fastest way possible.

Conclusion

By leveraging these important Python libraries for Machine Learning, you would be able to accomplish your large-scale and individual projects’ needs and requirements.

I hope that the purpose of landing on this blog has served you well with ML Python libraries. Although, there can still be odds when you cannot choose and implement libraries. It is better and time-saving to reach out to the community or get in touch with us at [email protected] to hire ML developer.

Frequently Asked Questions (FAQs)

What are the modules and packages in Python?

Python code in run time is contained in a module dedicated to user-specific code. To function in the run time with ease, the package modifies the user-interpreted code

Which Python library is the best for privacy-preserving ML?

Rosetta, a privacy-preserving framework based on TensorFlow, is the best privacy-preserving Machine Learning library.

Which version of Python is best for ML?

Anaconda and Miniconda are free, open-source projects that include 1400+ packages in the repository. These versions have become the most known Python distributions widely used in various companies and research laboratories for data science and machine learning. They are free and open-source projects and currently include 1400+ packages in the repository.

Does Tesla use PyTorch or TensorFlow?

Tesla uses PyTorch to develop full self-driving capabilities for its vehicles, including AutoPilot and Smart Summon. PyTorch is particularly created to scale up from research prototyping to product development.