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
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.
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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.
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.
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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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.
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.
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.
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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.
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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.
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.
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.
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.
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.
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.
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.