Top 9 Python Libraries for Machine Learning in 2024

Machine learning is the most algorithm-intense field in computer science. Gone are those days when people had to code all algorithms for machine learning. Thanks to Python and it’s libraries, modules, and frameworks.

Python machine learning libraries have grown to become the most preferred language for machine learning algorithm implementations. Learning Python is essential to master data science and machine learning. Let’s have a look at the main Python libraries used for machine learning.

Top Python Machine Learning Libraries

1) NumPy

NumPy is a well known general-purpose array-processing package. An extensive collection of high complexity mathematical functions make NumPy powerful to process large multi-dimensional arrays and matrices. NumPy is very useful for handling linear algebra, Fourier transforms, and random numbers. Other libraries like TensorFlow uses NumPy at the backend for manipulating tensors.

With NumPy, you can define arbitrary data types and easily integrate with most databases. NumPy can also serve as an efficient multi-dimensional container for any generic data that is in any datatype. The key features of NumPy include powerful N-dimensional array object, broadcasting functions, and out-of-box tools to integrate C/C++ and Fortran code.

Its key features are as below:

• Supports n-dimensional arrays to enable vectorization, indexing, and broadcasting operations.
• Supports Fourier transforms mathematical functions, linear algebra methods, and random number generators.
• Implementable on different computing platforms, including distributed and GPU computing.
• Easy-to-use high-level syntax with the optimized Python code to provide high speed and flexibility.
• In addition to that, NumPy enables the numerical operations of plenty of libraries associated with data science, data visualization, image processing, quantum computing, signal processing, geographic processing, bioinformatics, etc. So, it is one of the versatile machine learning libraries.

2) SciPy

With machine learning growing at supersonic speed, many Python developers were creating python libraries for machine learning, especially for scientific and analytical computing. Travis Oliphant, Eric Jones, and Pearu Peterson in 2001 decided to merge most of these bits and pieces codes and standardize it. The resulting library was then named as SciPy library. 

The current development of the SciPy library is supported and sponsored by an open community of developers and distributed under the free BSD license.

The SciPy library offers modules for linear algebra, image optimization, integration interpolation, special functions, Fast Fourier transform, signal and image processing, Ordinary Differential Equation (ODE) solving, and other computational tasks in science and analytics.

The underlying data structure used by SciPy is a multi-dimensional array provided by the NumPy module. SciPy depends on NumPy for the array manipulation subroutines. The SciPy library was built to work with NumPy arrays along with providing user-friendly and efficient numerical functions.

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One of the unique features of SciPy is that its functions are useful in maths and other sciences. Some of its extensively used functions are optimization functions, statistical functions, and signal processing. It supports functions for finding the numerical solute to integrals. So you can solve differential equations and optimization.

The following areas of SciPy’s applications make it one of the popular machine learning libraries.

• Multidimensional image processing
• Solves Fourier transforms, and differential equations
• Its optimized algorithms help you to efficiently and reliably perform linear algebra calculations

3) Scikit-learn

In 2007, David Cournapeau developed the Scikit-learn library as part of the Google Summer of Code project. In 2010 INRIA involved and did the public release in January 2010. Skikit-learn was built on top of two Python libraries – NumPy and SciPy and has become the most popular Python machine learning library for developing machine learning algorithms.  

Scikit-learn has a wide range of supervised and unsupervised learning algorithms that works on a consistent interface in Python. The library can also be used for data-mining and data analysis. The main machine learning functions that the Scikit-learn library can handle are classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.

Many ML enthusiasts and data scientists use scikit-learn in their AI journey. Essentially, it is an all-inclusive machine learning framework. Occasionally, many people overlook it because of the prevalence of more cutting-edge Python libraries and frameworks. However, it is still a powerful library and efficiently solves complex Machine Learning tasks.

The following features of scikit-learn make it one of the best machine learning libraries in Python:

• Easy to use for precise predictive data analysis
• Simplifies solving complex ML problems like classification, preprocessing, clustering, regression, model selection, and dimensionality reduction
• Plenty of inbuilt machine learning algorithms
• Helps build a fundamental to advanced level ML model
• Developed on top of prevalent libraries like SciPy, NumPy, and Matplotlib

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4) Theano

Theano is a python machine learning library that can act as an optimizing compiler for evaluating and manipulating mathematical expressions and matrix calculations. Built on NumPy, Theano exhibits a tight integration with NumPy and has a very similar interface. Theano can work on Graphics Processing Unit (GPU) and CPU.

Working on GPU architecture yields faster results. Theano can perform data-intensive computations up to 140x faster on GPU than on a CPU. Theano can automatically avoid errors and bugs when dealing with logarithmic and exponential functions. Theano has built-in tools for unit-testing and validation, thereby avoiding bugs and problems. 

Theano’s fast speeds give a competitive edge to C projects for problem-solving tasks that involve huge amounts of data. It makes most GPUs perform better than C language on a CPU.

It efficiently accepts structures and transforms them into extremely efficient code which uses NumPy and a few native libraries. Primarily, it is designed to deal with various computations demanded by huge neural network algorithms utilized in Deep Learning. Therefore, it is one of the popular machine learning libraries in Python, as well as deep learning.

Here are some prominent benefits of using Theano:

1. Stability Optimization:

It can determine some unsteady expressions and can use steadier expressions to solve them

2. Execution Speed Optimization:

It uses the latest GPUs and implements parts of expressions in your GPU or CPU. So, it is faster than Python.

   3. Symbolic Differentiation:

It automatically creates symbolic graphs for computing gradients.

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5) TensorFlow

TensorFlow was developed for Google’s internal use by the Google Brain team. Its first release came in November 2015 under Apache License 2.0. TensorFlow is a popular computational framework for creating machine learning models. TensorFlow supports a variety of different toolkits for constructing models at varying levels of abstraction.

TensorFlow exposes a very stable Python and C++ APIs. It can expose, backward compatible APIs for other languages too, but they might be unstable. TensorFlow has a flexible architecture with which it can run on a variety of computational platforms CPUs, GPUs, and TPUs. TPU stands for Tensor processing unit, a hardware chip built around TensorFlow for machine learning and artificial intelligence.

TensorFlow empowers some of the largest contemporary AI models globally. Alternatively, it is recognized as an end-to-end Deep Learning and Machine Learning library to solve practical challenges.

The following key features of TensorFlow make it one of the best machine learning libraries Python:

• Comprehensive control on developing a machine learning model and robust neural network
• Deploy models on cloud, web, mobile, or edge devices through TFX, TensorFlow.js, and TensorFlow Lite
• Supports abundant extensions and libraries for solving complex problems
• Supports different tools for integration of Responsible AI and ML solutions

Hope the Above Top 5 machine learning libraries in Python are clear, lets look at the other list of python libraries for machine learning.

6) Keras

Keras has over 200,000 users as of November 2017. Keras is an open-source library used for neural networks and machine learning. Keras can run on top of TensorFlow, Theano, Microsoft Cognitive Toolkit, R, or PlaidML. Keras also can run efficiently on CPU and GPU. 

Keras works with neural-network building blocks like layers, objectives, activation functions, and optimizers. Keras also have a bunch of features to work on images and text images that comes handy when writing Deep Neural Network code.

Apart from the standard neural network, Keras supports convolutional and recurrent neural networks. 

It was released in 2015 and by now, it is a cutting-edge open-source Python deep learning framework and API. It is identical to Tensorflow in several aspects. But it is designed with a human-based approach to make DL and ML accessible and easy for everybody.

You can conclude that Keras is one of the versatile machine learning libraries Python because it includes:

• Everything that TensorFlow provides but presents in easy to understand format.
• Quickly runs various DL iterations with full deployment proficiencies.
• Support large TPUs and GPU clusters which facilitate commercial Python machine learning.
• It is used in various applications, including natural language processing, computer vision, reinforcement learning, and generative deep learning. So, it is useful for graph, structured, audio, and time series data.

7) PyTorch

PyTorch has a range of tools and libraries that support computer vision, machine learning, and natural language processing. The PyTorch library is open-source and is based on the Torch library. The most significant advantage of PyTorch library is it’s ease of learning and using.

PyTorch can smoothly integrate with the python data science stack, including NumPy. You will hardly make out a difference between NumPy and PyTorch. PyTorch also allows developers to perform computations on Tensors. PyTorch has a robust framework to build computational graphs on the go and even change them in runtime. Other advantages of PyTorch include multi GPU support, simplified preprocessors, and custom data loaders. 

Facebook released PyTorch as a powerful competitor of TensorFlow in 2016. It has now attained huge popularity among deep learning and machine learning researchers. Various aspects of PyTorch suggest that it is one of the outstanding and best Python libraries for machine learning. Here are some of its key capabilities.

• Fully support the development of customized deep neural networks
• Production-ready with TorchServe
• Supports distributed computing through the torch.distributed backend
• Supports various extensions and tools to solve complex problems
• Compatible on all leading cloud platforms for extensible deployment
• Also supported on GitHub as an open-source Python framework

8) Pandas

Pandas are turning up to be the most popular Python library that is used for data analysis with support for fast, flexible, and expressive data structures designed to work on both “relational” or “labeled” data. Pandas today is an inevitable library for solving practical, real-world data analysis in Python. Pandas is highly stable, providing highly optimized performance. The backend code is purely written in C or Python. 

The two main types of data structures used by pandas are :

• Series (1-dimensional)
• DataFrame (2-dimensional)

These two put together can handle a vast majority of data requirements and use cases from most sectors like science, statistics, social, finance, and of course, analytics and other areas of engineering. 

Pandas support and perform well with different kinds of data including the below :

• Tabular data with columns of heterogeneous data. For instance, consider the data coming from the SQL table or Excel spreadsheet.
• Ordered and unordered time series data. The frequency of time series need not be fixed, unlike other libraries and tools. Pandas is exceptionally robust in handling uneven time-series data
• Arbitrary matrix data with the homogeneous or heterogeneous type of data in the rows and columns
• Any other form of statistical or observational data sets. The data need not be labeled at all. Pandas data structure can process it even without labeling. 

It was launched as an open-source Python library in 2009. Currently, it has become one of the favourite Python libraries for machine learning among many ML enthusiasts. The reason is it offers some robust techniques for data analysis and data manipulation. This library is extensively used in academia. Moreover, it supports different commercial domains like business and web analytics, economics, statistics, neuroscience, finance, advertising, etc. It also works as a foundational library for many advanced Python libraries.

Here are some of its key features:

• Handles missing data
• Handles time series data
• Supports indexing, slicing, reshaping, subsetting, joining, and merging of large datasets
• Offers optimized code for Python using C and Cython
• Powerful DataFrame object for broad data manipulation support

9) Matplotlib

Matplotlib is a data visualization library that is used for 2D plotting to produce publication-quality image plots and figures in a variety of formats. The library helps to generate histograms, plots, error charts, scatter plots, bar charts with just a few lines of code.

It provides a MATLAB-like interface and is exceptionally user-friendly. It works by using standard GUI toolkits like GTK+, wxPython, Tkinter, or Qt to provide an object-oriented API that helps programmers to embed graphs and plots into their applications. 

It is the oldest Python machine learning library. However, it is still not obsolete. It is one of the most innovative data visualization libraries for Python. So, the ML community admires it.

The following features of the Matplotlib library make it a famous Python machine learning among the ML community:

• Its interactive charts and plots allow fascinating data storytelling
• Offers an extensive list of plots appropriate for a particular use case
• Charts and plots are customizable and exportable to various file formats
• Offers embeddable visualizations with different GUI applications
• Various Python frameworks and libraries extend Matplotlib

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Why Python library used in machine learning

• Ease of Use: Python’s syntax is simple and readable, making it accessible for both beginners and experienced developers.
• Extensive Libraries: Libraries like TensorFlow, Keras, scikit-learn, and PyTorch provide robust tools for machine learning tasks, from preprocessing to model deployment.
• Community Support: Python has a large, active community that contributes to a wealth of resources, tutorials, and support for troubleshooting.
• Integration: Python integrates well with other languages and technologies, facilitating data analysis, web development, and machine learning workflows.
• Flexibility: Python’s flexibility allows for the easy combination of different machine learning and data processing techniques.

Benefits Of Using Python For Data Science

Python libraries for machine learning has emerged as a go-to language for data science, offering a multitude of benefits that cater to the needs of professionals and learners alike.

• Simplicity and Readability

One of the key advantages of python libraries for machine learning in data science is its simplicity. The language is designed with readability in mind, resembling the English language closely. This characteristic makes it an excellent choice for beginners, ensuring that even someone at a 6th-grade level can grasp the basics swiftly.

• Extensive Libraries

Python boasts an extensive collection of ML libraries that significantly expedite the data science workflow. Libraries such as NumPy for numerical operations, Pandas for data manipulation, and Matplotlib for data visualization provide ready-to-use functions, reducing the need for coding from scratch. This streamlines the entire data analysis process, making it more efficient.

• Versatility

Python’s versatility is another standout feature. It can seamlessly integrate with other languages and technologies, allowing data scientists to leverage existing systems effortlessly. Whether it’s connecting to databases, working with web APIs, or incorporating machine learning models, ML libraries python adaptability enhances its utility in various data science applications.

• Community Support

The robust machine learning tools python community contributes significantly to its appeal in data science. With a vast and active user base, developers and data scientists can easily find support, resources, and solutions to challenges they encounter. This collaborative environment fosters learning and growth, making ML libraries python an excellent choice for those entering the field.

• Data Visualization

Effective communication of insights is crucial in data science, and Python excels in this aspect. ML libraries like Matplotlib, Seaborn, and Plotly enable users to create visually compelling graphs and charts. This not only aids in understanding complex data but also facilitates conveying findings to diverse audiences, making machine learning with python a powerful tool for data storytelling.

• Scalability

As data science projects evolve, scalability becomes paramount. Python’s scalability is evident in its ability to handle both small-scale data analysis tasks and large-scale, enterprise-level projects. This adaptability ensures that Python remains a reliable choice as data science requirements grow and become more complex.

• Ease of Integration with Big Data Technologies

In the era of big data, Python’s compatibility with major big data technologies, such as Apache Hadoop and Spark, is a significant advantage. Data scientists can seamlessly integrate python libraries into their big data workflows, allowing them to analyze and derive insights from massive datasets efficiently.

Benefits of Learning Python for Non-Programmers

Learning machine learning with python holds numerous benefits for non-programmers, offering a gateway into the world of coding without overwhelming complexities. Whether you’re a student, professional, or hobbyist, machine learning modules user-friendly nature and versatility make it an ideal starting point for those new to programming.

• Ease of Learning

Python’s syntax is designed to be clear and readable, resembling plain English. For non-programmers, this means a gentler learning curve. The simplicity of Python allows beginners to focus on understanding fundamental programming concepts without getting bogged down in convoluted syntax.

• Versatility and Applicability

Python’s versatility extends across various domains, making it a valuable asset for non-programmers. From web development and data analysis to artificial intelligence and automation, Python finds applications in diverse fields. This adaptability ensures that learners can explore different areas of interest and tailor their programming journey according to their preferences.

• Abundance of Resources

A vast and supportive community surrounds Python, providing an abundance of resources for learners. Numerous online tutorials, forums, and documentation make it easy for non-programmers to seek guidance and find solutions to challenges they may encounter. The wealth of resources fosters a collaborative learning environment, enhancing the overall learning experience.

• Community and Collaboration

Python’s popularity has led to the formation of a vibrant and welcoming community. For non-programmers, this means access to a network of experienced developers willing to share knowledge and assist with problem-solving. Engaging with this community not only aids learning but also introduces individuals to the collaborative nature of programming.

• Extensive Library Support

Python boasts an extensive collection of machine learning libraries and frameworks, simplifying complex tasks for non-programmers. These pre-built modules enable users to leverage powerful functionalities without delving into intricate code. This accessibility allows beginners to accomplish tasks efficiently, boosting confidence and motivation.

• Applicability in Data Science and Analysis

For those interested in data science, Python’s popularity in this domain is a major advantage. Its libraries, such as NumPy and Pandas, provide robust tools for data manipulation and analysis. Non-programmers can easily grasp these tools, opening doors to opportunities in the rapidly growing field of data science.

• Automation and Productivity

Learning Python introduces non-programmers to the world of automation. The language’s simplicity facilitates the creation of scripts to automate repetitive tasks, enhancing efficiency and productivity. This practical aspect is especially appealing to individuals seeking ways to streamline their workflows in various professional or personal settings.

• Career Opportunities

Acquiring Python skills enhances non-programmers’ employability across industries. Many organizations value Python proficiency due to its widespread use and versatility. Learning Python provides individuals with a valuable skill set, making them competitive candidates in job markets where programming knowledge is increasingly in demand.

How To Choose The Right Python Libraries For Your Needs?

Choosing the right machine learning libraries for your needs is a crucial step in developing efficient and effective programs. Python’s extensive library ecosystem can be overwhelming, but a strategic approach ensures you select the tools that align with your specific requirements.

• Identify Your Project Requirements

Begin by clearly defining your project’s objectives and requirements. Understanding the problem you aim to solve or the tasks you need to accomplish will guide your library selection. For instance, if you’re working on a machine learning project, libraries like TensorFlow or Scikit-learn might be relevant.

• Research and Understand Options

Conduct thorough research on available libraries within the domain of your project. Explore documentation, user reviews, and community support to gauge the effectiveness and ease of use. This step ensures that you make informed decisions based on the library’s features and capabilities.

• Consider Library Compatibility

Check the compatibility of the libraries with your Python version and other dependencies. Compatibility issues can hinder your project’s progress and lead to unnecessary complications. Choose libraries used in machine learning that seamlessly integrate with your existing technology stack to avoid roadblocks during implementation.

• Evaluate Performance

Assess the performance of potential libraries used in machine learning by considering factors such as execution speed and resource utilization. Depending on your project’s nature, you may prioritize speed, efficiency, or memory usage. Understanding these aspects helps in selecting a library that aligns with your performance expectations.

• Community Support and Documentation

Opt for deep learning with python with active communities and comprehensive documentation. A vibrant community ensures ongoing development, support, and troubleshooting resources. Well-documented libraries make it easier for you to understand and utilize the functionalities, reducing the learning curve.

• Scalability and Maintenance

Consider the scalability of the deep learning with python in the long run. Choose tools that can accommodate future expansion or modifications to your project. Additionally, evaluate the maintenance aspect – libraries with regular updates and a history of addressing issues promptly are preferable for sustained development.

• Test the Libraries

Before making a final decision, conduct small-scale tests with the shortlisted libraries. This hands-on approach allows you to experience the ease of use, functionality, and performance firsthand. Testing also reveals potential challenges or advantages that may not be apparent through documentation alone.

• Seek Expert Advice

If you encounter difficulty in choosing between libraries, seek advice from experienced developers or experts in the field. Their insights can provide valuable perspectives and help you make an informed decision based on practical considerations.

Future Of Python For Data Science

• Versatility and Accessibility

Python’s versatility is a cornerstone of its enduring popularity. It serves as a multipurpose language, making it well-suited for a wide range of applications, including data science. Its syntax, resembling plain English, enhances accessibility, making it an ideal choice for both beginners and seasoned professionals.

• Rich Ecosystem of Libraries

The extensive collection of libraries and frameworks in Python, such as NumPy, TensorFlow python, Pandas, and Scikit-Learn, forms a robust foundation for data science tasks. These libraries simplify complex operations, accelerate development, and contribute significantly to the language’s prominence in the data science community.

• Machine Learning Dominance

Python’s dominance in the field of machine learning is a key driver for its future in data science. With libraries like TensorFlow and PyTorch, Python facilitates the development and deployment of machine learning models. Its adaptability to the evolving landscape of artificial intelligence ensures that Python will remain at the forefront of cutting-edge advancements.

• Community Support and Collaboration

The thriving Python community is a testament to the language’s enduring success. The open-source nature of Python encourages collaborative development, leading to continuous improvements and innovations. This strong community support ensures that Python remains relevant and well-maintained, reinforcing its position in data science.

• Integration with Big Data Technologies

As organizations grapple with vast amounts of data, Python seamlessly integrates with big data technologies such as Apache Spark and Hadoop. This adaptability positions Python as a go-to language for handling and analyzing large datasets, a crucial aspect of contemporary data science.

• Growing Job Market Demand

The increasing demand for data science professionals translates into a burgeoning job market for Python experts. The language’s prevalence in data science roles across various industries, from healthcare to finance, underscores its indispensability. As this demand continues to rise, proficiency in Python will likely become a valuable skillset.

• Continuous Development and Updates

Python’s commitment to regular updates and improvements ensures that it stays relevant in the face of evolving technological landscapes. The language’s adaptability to incorporate the latest features and address emerging challenges solidifies its place in the future of data science.

Conclusion

Python is the go-to language when it comes to data science and machine learning and there are multiple reasons to choose python for data science. 

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Python has an active community that most developers create libraries for their own purposes and later release it to the public for their benefit. Here are some of the common machine learning libraries used by Python developers. If you want to update your data science skills, check out IIIT-B’s Executive PG Programme in Data Science program.