Top 15 Python AI & Machine Learning Open Source Projects

Machine learning and artificial intelligence are some of the most advanced topics to learn. So you must employ the best learning methods to make sure you study them effectively and efficiently. 

There are many programming languages you can use in AI and ML implementations, and one of the most popular ones among them is Python. In this article, we’re discussing multiple AI projects in Python, which you should be familiar with if you want to become a professional in this field. 

All of the Python projects we’ve discussed here are open source with broad audiences and users. Being familiar with these projects will help you in learning AI and ML better.

I hope you will learn a lot while working on these python projects. If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIM-K’s Professional Certificate Program in Data Science for Business Decision Making and upskill yourself for the future.

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Python ML & AI Open Source Projects

1. TensorFlow

TensorFlow tops the list of open-source AI projects in Python. It is a product of Google and helps developers in creating and training machine learning models. The engineers and researchers working in Google’s Brain Team created TensorFlow to help them in performing research on machine learning. TensorFlow enabled them to convert prototypes into working products quickly and efficiently. 

With TensorFlow, you can work on your machine learning projects remotely in the cloud, in the browser, or use it in on-premises applications. TensorFlow has thousands of users worldwide, as it is the go-to solution for any AI professional. 

Abstraction is the greatest benefit that TensorFlow offers for machine learning advancement. It helps you to work remotely in the browser, in the cloud, or in on-premises applications.

It provides various workflows with in-built, high-level APIs that enable beginners and professionals to develop ML models in different languages. Models developed using TensorFlow can be implemented on platforms such as the cloud, servers, browsers, mobile, edge devices, and more.

TensorFlow is a cross-platform framework that works on a broad range of hardware, including CPUs, GPUs, mobile, and embedded platforms. Furthermore, you can TensorFlow AI mini projects with source code on Google’s proprietary TPU (TensorFlow Processing Unit) hardware to accelerate the growth of deep learning models.

It is one of those AI python projects with source code that can train and implement deep neural networks for visual recognition, handwritten digit classification, recurrent neural networks, word embeddings, natural language processing (NLP), sequence-to-sequence models for machine translation, and PDE-based simulations.

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2. Keras

Keras is an accessible API for neural networks. It is based in Python, and you can run it on CNTK, TensorFlow as well as Theano. It is written in Python and follows best practices to reduce the cognitive load. It makes working on deep learning projects more efficient. 

The error message feature helps developers in identifying any mistakes and fixing them. As you can run it on top of TensorFlow, you get the benefit of the flexible and versatile application too. This means you can run Keras in your browser, on Android or iOS through TF Lite, as well as through their web API. If you want to work on deep learning projects, you must be familiar with Keras. 

Imagine that you need a deep learning framework that facilitates rapid prototyping, works efficiently on CPUs and GPUs, and supports convolutional and recurrent networks. Keras is the perfect library for implementing open-source AI projects fulfilling these needs.

Keras doesn’t deal with simple low-level operations, unlike independent open-source AI projects. It uses libraries from related deep learning frameworks such as Theano or Tensorflow as backend engines to perform all low-level computations (like convolutions, tensor products, and many more).

Keras is one of those AI mini projects with source code that provides easy and rapid backend access. This is because it boasts ready-to-use interfaces. No need to commit to a specific framework because you can rapidly transit back and forth between the several backends.

Keras also provides a high-level API that looks after developing models, stating layers, and configuring different models. This API’s loss and optimizer functions help you to develop models; the API’s fit function helps you to train the process.

Read: Machine Learning Projects for Beginners

3. Theano

Theano lets you optimize, evaluate, and define mathematical expressions that involve multi-dimensional arrays. It is a Python library and has many features that make it a must-have for any machine learning professional. 

It is optimized for stability and speed and can generate dynamic C code to evaluate expressions quickly. Theano allows you to use NumPy.ndarray in its functions as well, so you get to use the capabilities of NumPy effectively. 

Theano expresses computations using a NumPy -Esque syntax and runs efficiently on CPU or GPU architectures. It is an open-source project developed by the MILA (Montreal Institute for Learning Algorithms) at the Université de Montréal. It is a fundamental library for working on Deep Learning projects and wrapper libraries in Python.

Alternatively, it works as a compiler for performing mathematical expressions in Python. It accepts your data structures and transforms them into efficient code. The resultant code uses NumPy, native code (C++), and efficient native libraries like BLAS. All these components help the code run as quickly as possible on GPUs and CPUs.

It uses clever code optimizations to obtain the maximum possible performance from the hardware. It becomes easy to deal with this AI project with source code if you know the fundamentals of mathematical optimizations in Python code. Moreover, Theano offers detailed installation instructions for major operating systems like Windows, Linux, and OS X.

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4. Scikit-learn

Scikit-learn is a Python-based library of tools you can use for data analysis and data mining. You can reuse it in numerous contexts. It has excellent accessibility, so using it is quite easy as well. Its developers have built it on top of matplotlib, NumPy, and SciPy. 

Some tasks for which you can use Scikit-learn include Clustering, Regression, Classification, Model Selection, Preprocessing, and Dimensionality Reduction. To become a proper AI professional, you must be able to use this library. 

Check out The Trending Python Tutorial Concepts in 2024

5. Chainer

Chainer is a Python-based framework for working on neural networks. It supports multiple network architectures, including recurrent nets, convnets, recursive nets, and feed-forward nets. Apart from that, it allows CUDA computation so you can use a GPU with very few lines of code. 

You can run Chainer on many GPUs too if required. A significant advantage of Chainer is it makes debugging the code very easy, so you won’t have to put much effort in that regard. On Github, Chainer has more than 12,000 commits, so you can understand how popular it is. 

Chainer is an (open source) deep learning framework written using CuPy and NumPy Python libraries. Japanese venture company “Preferred Networks” in collaboration with Microsoft, Intel, IBM, and Nvidia manages its development.

Chainer is flexible and intuitive. You need exclusively designed operations if the network contains complex control flows like loops and conditionals, in the define-and-run approach. But in this approach, the programming language’s native constructs, like for loops and if statements can be utilized to designate such flow. So, Chainer’s flexibility is useful for executing recurrent neural networks.

Another benefit of this AI project with source code is the simplicity of debugging. Usually, it is challenging to determine the fault an error occurs in the training calculation when using the define-and-run approach. This is because the code written to define the actual position of the error and the network are separated.

If you are searching for Python AI source code for various projects, Chainer should be on your list.

6. Caffe

Caffe is a product of Berkeley AI Research and is a deep learning framework that focuses on modularity, speed, and expression. It is among the most popular open-source AI projects in Python. 

It has excellent architecture and speed as it can process more than 60 million images in a day. Moreover, it has a thriving community of developers who are using it for industrial applications, academic research, multimedia, and many other domains. 

7. Gensim

Gensim is an open-source Python library that can analyse plain-text files for understanding their semantic structure, retrieve files that are semantically similar to that one, and perform many other tasks. 

It is scalable and platform-independent, like many of the Python libraries and frameworks we have discussed in this article. If you plan on using your knowledge of artificial intelligence to work on NLP (Natural Language Processing) projects, then you should study this library for sure. 

Gensim stands for Generate Similar. It is a python-based open-source framework for natural language processing and unsupervised topic modeling. It extracts semantic concepts from documents. It can also manage extensive text collections. So, it differentiates itself from other ML software packages that use memory processing.

It is one of the best AI projects for beginners with source code that improves processing speed. This is because it offers efficient multicore implementations for different algorithms. It features more text processing features than many other packages like R, Scikit-learn, etc.

It uses the best models and modern statistical machine learning (like Creating word or document vectors) to perform various complex tasks. It also detects semantic structure in plain-text materials.

Gensim is one of the popular AI projects for beginners with source code because it has been used in many applications including Word2vec, fastText, Latent Semantic Analysis (LSA),  Latent Dirichlet Allocation (LDA), and Term Frequency-Inverse Document Frequency (TF-IDF).

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8. PyTorch

PyTorch helps in facilitating research prototyping so you can deploy products faster. It allows you to transition between graph modes through TorchScript and provides distributed training you can scale. PyTorch is available on multiple cloud platforms as well and has numerous libraries and tools in its ecosystem that support NLP, computer vision, and many other solutions. To perform advanced AI implementations, you’ll have to become familiar with PyTorch.

PyTorch started its journey as a Python-based substitute for the Lua Torch framework. Initially, it focused only on research applications. Presently, the PyTorch ecosystem includes tools, projects, libraries, and models developed by a community of industrial and academic researchers, deep learning experts, and application developers.

PyTorch is better than other AI python projects with source code. This is because it uses dynamic computing that offers excellent flexibility in developing complex networks. Moreover, PyTorch features a readable syntax, so users can easily grasp it.

PyTorch improves the AI models’ optimization with the help of Python’s intrinsic potential for asynchronous implementation. Its Distributed Data Parallelism feature facilitates project development by running models across various computers. PyTorch’s capability to construct ML/DL solutions is vast because the community behind it expands.

If you are looking for artificial intelligence projects with source code in Python, you can check out Pytorch.

Read more: Tensorflow vs Pytorch – Comparison, Features & Applications

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9. Shogun

Shogun is a machine learning library (open-source) and provides many unified as well as efficient ML methods. It is not based on Python exclusively so you can use it with several other languages too such as Lua, C#, Java, R, and Ruby. It allows combining of multiple algorithm classes, data representations, and tools so you can prototype data pipelines quickly. 

It has a fantastic infrastructure for testing that you can use on various OS setups. It has several exclusive algorithms as well, including Krylov methods and Multiple Kernel Learning, so learning about Shogun will surely help you in mastering AI and machine learning. 

Shogun provides various unified and proficient machine learning algorithms. This project’s core is kernel machines like support vector machines for solving classification and regression problems.

It is one of the versatile AI projects with source code because its scope is not limited to any single language. You can use its toolkit across a unified interface (SWIG) from Python, C++, C#, Octave, Java, R, Ruby, Lua, etc.

Its ML toolkit inspires the development journey with its key characteristics like open source and accessibility.

Shogun is popular for being one of the oldest and biggest open-source ML platforms.  Anyone can effortlessly learn it by simply connecting to a Jupyter Notebook. It is possible to run it in Cloud.  All the standard ML algorithms’ implementation is competitive according to the MLPack benchmarking framework benchmark. Shogun is one of the AI projects with source code that offers extensive testing infrastructure. So, it is compatible with various operating systems.

If you are looking for Python machine learning projects that can help you train ML models more effectively, you should check Shogun out.

10. Pylearn2

Based on Theano, Pylearn2 is among the most prevalent machine learning libraries among Python developers. You can use mathematical expressions to write its plugins while Theano takes care of their stabilization and optimization. On Github, Pylearn2 has more than 7k commits, and they are still growing, which shows its popularity among ML developers. Pylearn2 focuses on flexibility and provides a wide variety of features, including an interface for media (images, vectors, etc.) and cross-platform implementations. 

11. Nilearn

Nilearn helps in Neuroimaging data and is a popular Python module. It uses scikit-learn (which we’ve discussed earlier) to perform various statistical actions such as decoding, modeling, connectivity analysis, and classification. Neuro-imaging is a prominent area in the medical sector and can help in solving multiple issues such as better diagnosis with higher accuracy. If you’re interested in using AI in the medical field, then this is the place to start. 

Read: Scikit-learn in Python: Features, Prerequisites, Pros & Cons

12. Numenta

Numenta is based on a neocortex theory called HTM (Hierarchical Temporal Memory). Many people have developed solutions based on HTM and the software. However, there’s a lot of work going on in this project. HTM is a machine intelligence framework that’s based on neuroscience. 

13. PyMC

PyMC uses Bayesian statistical models with algorithms such as the Markov chain. It is a Python module and because of its flexibility, finds applications in many areas. It uses NumPy for numeric problems and has a dedicated module for Gaussian processes. 

It can create summaries, perform diagnostics, and embed MCMC loops in big programs; you can save traces as plain text, MySQL databases, as well as Python pickles. It is undoubtedly a great tool for any artificial intelligence professional. 

14. DEAP

DEAP is an evolutionary computation framework for testing ideas and prototyping. You can work on genetic algorithms with any kind of representation as well as perform genetic programming through prefix trees. 

DEAP has evolution strategies, checkpoints that take snapshots, and a benchmarks module for storing standard test functions. It works amazingly well with SCOOP, multiprocessing, and other parallelization solutions. 

15. Annoy

Annoy stands for Approximate Nearest Neighbors Oh Yeah, yes, that’s the exact name of this C++ library, which also has Python bindings. It helps you perform nearest neighbor searches while using static files as indexes. WIth Annoy, you can share an index across different processes so you wouldn’t have to build multiple indexes for each method. 

Its creator is Erik Bernhaardsson, and it finds applications in many prominent areas, for example, Spotify uses Annoy for making better recommendations to its users. 

Also Read: Python Projects for Beginners

16. XGBoost (eXtreme Gradient Boosting)

XGBoost stands out as a prominent open-source machine learning library for Python, gaining widespread popularity in the AI community. Originally developed by Tianqi Chen, this library specializes in gradient boosting, a technique that builds multiple decision trees sequentially to improve model performance. XGBoost excels in solving classification and regression problems efficiently.

One of the key strengths of XGBoost lies in its optimization for performance and speed. It implements parallel computing and tree-pruning techniques, making it highly scalable for large datasets. The library also supports various objective functions and evaluation criteria, allowing users to tailor their models to specific tasks.

XGBoost has found applications in diverse domains, such as finance, healthcare, and natural language processing. Its adaptability, feature importance visualization, and ability to handle missing data make it an indispensable tool for machine learning practitioners. As an open-source project, XGBoost fosters collaboration and continuous improvement within the machine learning community. Developers and data scientists appreciate its versatility and efficiency in boosting model accuracy across a wide array of tasks.

17. LightGBM

LightGBM, a paradigm of excellence in the open-source machine learning landscape for Python, stands as a robust gradient-boosting framework that has garnered accolades for its efficiency and scalability. Conceived by Microsoft, LightGBM is meticulously crafted for distributed and efficient training of machine learning models.

Its exceptional speed in model training, coupled with minimal memory usage, positions it as a go-to solution for handling large datasets and achieving state-of-the-art performance in classification, regression, and ranking tasks. LightGBM’s parallel computing capabilities and advanced tree pruning techniques make it a versatile tool embraced by data scientists and researchers across diverse domains.

18. CatBoost

CatBoost is the epitome of innovation in open-source machine learning and it distinguishes itself as a high-performance library designed to tackle challenges related to categorical feature support. Born out of the ingenuity of Yandex, CatBoost alleviates the complexities associated with handling categorical features in machine learning.

Its unique algorithms empower developers to construct accurate models with minimal preprocessing, making it a valuable asset in the Python ecosystem. CatBoost’s commitment to simplicity, efficiency, and user-friendly design positions it as an indispensable tool for both seasoned practitioners and those entering the realm of machine learning.

If you were searching for an artificial intelligence project GitHub, Catboost might be the right one for you.

19. FastAI

FastAI is a beacon of accessibility in open-source deep learning and it emerges as a high-level library built on the robust PyTorch framework. Developed by the FastAI team, this library aims to democratize AI, making cutting-edge techniques accessible to both novices and experienced practitioners. FastAI stands out with its user-friendly interface, comprehensive documentation, and a commitment to providing best practices and abstractions. It not only simplifies the complex landscape of deep learning but also empowers users to achieve state-of-the-art results with minimal coding effort. FastAI’s active community and focus on education further contribute to its prominence in the Python AI ecosystem.

20. Hugging Face Transformers

Hugging Face Transformers has a towering presence in open-source Python libraries and takes center stage with its transformative impact on natural language processing (NLP). Developed by Hugging Face, this library serves as a treasure trove, offering a rich collection of pre-trained models and tools for a plethora of NLP tasks, including text classification, language translation, and sentiment analysis.

The Transformers library not only simplifies the implementation of state-of-the-art NLP models but also fosters a collaborative spirit with an expansive model repository. It has become the cornerstone of the NLP community, guiding researchers and developers through the intricacies of language-related AI projects.

21. scikit-image

Scikit-image is undoubtedly an architectural marvel in the realm of open-source image processing for Python and extends the capabilities of scikit-learn into the captivating domain of image analysis. Nurtured by a vibrant and active community, scikit-image exemplifies comprehensive algorithmic prowess, addressing tasks such as image segmentation, feature extraction, and filtering.

Its modular design and seamless integration with other scientific computing libraries make it a versatile and indispensable tool for researchers and practitioners immersed in the world of computer vision. scikit-image’s unwavering commitment to simplicity, interoperability, and continuous improvement solidifies its position as a cornerstone in the arsenal of tools for image-related applications within the Python ecosystem.

Scikit-learn is one of the most useful machine learning examples Python out there. If you are looking for AI mini projects with source code, scikit-image can help you make the most out of Scikit-learn.

22. Prophet

Prophet, an open-source forecasting tool developed by Facebook, has carved a niche for itself in the Python ecosystem. It specializes in time series forecasting, making it an invaluable asset for analysts and data scientists. Prophet is designed for simplicity and ease of use, allowing users to produce accurate forecasts with minimal effort. Its intuitive interface, automatic handling of missing data, and the ability to incorporate holidays and special events make it a go-to solution for predicting trends and patterns in time series data.

This is a great example of machine learning mini projects with source code.

23. AllenNLP

AllenNLP is a cutting-edge open-source library for natural language processing (NLP) that is engineered to facilitate research and experimentation in the field. Developed by the Allen Institute for Artificial Intelligence, AllenNLP empowers researchers to design and implement state-of-the-art NLP models. Its modular architecture and extensive pre-built components facilitate the development of custom models, making it a playground for innovation in tasks such as text classification, named entity recognition, and language modeling.

If you are interested in a Python AI projects with source code, AllenNLP can be right one for you.

24. Optuna

Optuna, a hyperparameter optimization framework, emerges as a crucial tool for tuning machine learning model parameters efficiently. Developed by the team at Preferred Networks, Optuna automates the process of finding the optimal set of hyperparameters for a given machine learning model. Its flexibility, support for various optimization algorithms, and seamless integration with popular machine learning frameworks make it a sought-after choice for practitioners striving to enhance model performance through systematic hyperparameter tuning.

If you are looking for optimization machine learning projects using Python that is easy to use, Optuna is the right one for you.

25. SpaCy

SpaCy is a lightning-fast open-source library for natural language processing and has become a staple in the toolkit of NLP enthusiasts. Developed with efficiency in mind, SpaCy excels in tasks such as tokenization, part-of-speech tagging, and named entity recognition. Its focus on performance and user-friendly design makes it a preferred choice for building robust and scalable NLP pipelines. SpaCy’s pre-trained models, continuous updates, and support for multiple languages contribute to its widespread adoption in both research and industry applications.

If you are searching for AI projects with source code, you can check out this NLP project.

26. Bokeh

Bokeh can be described as an interactive visualization library, stands out in the Python ecosystem for creating captivating and interactive visualizations for web applications. Developed by Anaconda, Inc., Bokeh supports a variety of plot types, ranging from simple line charts to complex, interactive dashboards. Its seamless integration with Jupyter notebooks and ability to handle large datasets make it an excellent choice for data scientists and engineers seeking to communicate insights effectively through visually appealing and interactive plots. Bokeh’s commitment to open-source principles and active community support solidify its position as a go-to library for creating engaging data visualizations in Python.

27. AllenSDK

AllenSDK, an initiative by the Allen Institute for Brain Science, provides a comprehensive set of tools and resources for neuroscientists and researchers. This open-source Python library enables the analysis and exploration of large-scale neuroscientific datasets. AllenSDK facilitates tasks such as accessing brain atlases, retrieving electrophysiological data, and performing complex analyses, making it an essential resource for advancing our understanding of the brain’s intricacies.

28. Surprise

Surprise is an open-source Python library designed for building and analyzing recommender systems. It offers a collection of collaborative filtering algorithms, making it a valuable asset for developers and data scientists working on recommendation engines. Surprise’s user-friendly API simplifies the process of experimenting with different recommendation algorithms and evaluating their performance, making it an ideal choice for those delving into the realm of personalized content recommendations.

If you are looking for machine learning projects Python, you can check out Surprise.

29. BioPython

BioPython stands as a testament to open-source innovation in the realm of computational biology and bioinformatics. This Python library provides tools for working with biological data, including DNA sequences, protein structures, and phylogenetic trees. BioPython’s rich functionality and modular design make it a versatile toolkit for researchers and scientists engaged in biological data analysis and computational biology projects.

30. Plotly

Plotly, a dynamic open-source graphing library has become synonymous with creating interactive and visually appealing plots in Python. Whether it’s static charts, 3D visualizations, or interactive dashboards, Plotly excels in conveying complex data insights with elegance. Developed by Plotly Technologies, this library supports integration with popular data science tools and frameworks, making it a preferred choice for professionals seeking to elevate their data visualization game. Plotly’s versatility and active community contribute to its prominence in the ever-evolving landscape of interactive data visualization in Python.

Learn More about Python in AI and ML

We hope you found this list of AI projects in Python helpful. Learning about these projects will help you in becoming a seasoned AI professional. Whether you begin with TensorFlow or DEAP, it’d be a significant step in this journey.

If you’re interested in learning more about artificial intelligence, then we recommend heading to our blog. There, you’ll find plenty of detailed and valuable resources. Moreover, you can get an AI course and get a more individualized learning experience.

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.

Why is it recommended to use Python in data science and machine learning and AI?

One of the key reasons Python is by far the most popular AI programming language is the large number of libraries available. A library is a pre-written computer program that allows users to access certain functionality or conduct certain activities. Python libraries provide basic stuff so that coders don't have to start from scratch every time. Because of the low entry barrier, more data scientists can quickly learn Python and start utilizing it for AI research without putting in a lot of work. Python is not only simple to use and understand, but it is also quite versatile. Python is incredibly easy to read, thus any Python developer can comprehend and alter, copy, or share the code of their peers.

What problems can machine learning AI solve?

One of the most basic uses of machine learning is spam detection. Our email providers automatically filter undesired spam emails into an unwanted, bulk, or spam inbox in most of our inboxes. Recommender systems are among the most common and well-known applications of machine learning in everyday life. Search engines, e-commerce sites, entertainment platforms, and a variety of web and mobile apps all leverage these systems. The major issues that any marketer faces are client segmentation, churn prediction, etc. Over the last few years, advances in deep learning have sped up progress in image and video identification systems.

How many types are available in machine learning?

One of the most common categories of machine learning is supervised learning. The machine learning model is trained on labelled data in this case. The ability to deal with unlabeled data is a benefit of unsupervised machine learning. Reinforcement learning is directly inspired by how people learn on data in their daily lives. It includes a trial-and-error algorithm that builds upon itself and learns from different scenarios.

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