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.
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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.
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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.
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.
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.
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.
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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.
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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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