The most successful companies of the last decade all agree that data is their most valuable asset. It is common knowledge that the future belongs to organizations that will have the ability to process and extract information from data patterns that are generated every day.
It is estimated that around 2.5 quintillion bytes of data is generated every day. The science of using statistics, algorithms, and analytics to extract meaningful information from this unstructured data is called data science. This information can give organizations a much-needed insight to improve their systems and sales.
If you are a developer who is trying to pave a path in the world of IT, exploring some open-source data science projects is a great idea. In this article, we will explore a few open-source data science project ideas. Hopefully, it will offer you some encouragement to begin your first data science project today.
Open Source Machine Learning Projects
Machine learning is currently the talk of the town in the world of IT. It allows us to build programs and algorithms that improve automatically over time. It goes without saying that machine learning has huge application potential in almost every industry.
Plus, it is safe to say that this subset of artificial intelligence is here to stay and will probably transform our lives in the future. If you hope to start a career in machine learning, exploring a few open-source projects in this domain can give you a much-needed head start in understanding its intricacies. Let us now explore some interesting open-source data science projects.
1) Simplifying Machine Learning Papers – An Open-Source Project
Most people find it extremely difficult to cope with the technicalities of machine learning when they begin their careers. Studying machine learning-related research papers is especially daunting as they contain terms and annotations that are extremely hard to understand for a beginner. An interesting project that is open-sourced on Github aims to solve just that.
The project is basically a collection of machine learning related papers. It contains illustrations, annotations, and explanations of technical terminologies making it easier to understand the core concept. If you are a beginner, this is definitely a project you should check out. It will give you clarity on several key machine learning annotations that can help you in your journey ahead.
The project already has a collection of interesting and informative papers and is being updated regularly. Check out this object detection example which is one of the most interesting parts of the project.
2) Exploring NeoML
If you are someone who has an introductory knowledge of data science, this is an exciting project that you should definitely explore. Often, a great machine learning project idea fails to get executed owing to its high cost of development. NeoML tries to solve this problem.
NeoML is a machine learning framework that can help you build, train, and deploy machine learning models. In short, with NeoML, you no longer have to worry about huge investments and can instantly start building your own machine learning pipeline today. Many open-source project ideas like natural language processing, image preprocessing, data extraction from unstructured data, and computer vision can be deployed using NeoML.
Using NeoML to try out some of these interesting ideas will teach you a lot about machine learning and how it can be applied successfully.
3) Face Recognition
Face recognition is now a fully explored machine learning application found on almost every smartphone today. It is usually used as an encryption standard to unlock a user’s device. There’s a lot to learn from this open-sourced project that can benefit you if you are exploring machine learning. You can use this project to manipulate and recognize faces using simple Python programs or through the command line.
You can also try to make variations to this project idea and alter its purpose to solve some other interesting problem statements. One example could be of detecting a face mask like how it’s done here.
Open Source Computer Vision Projects
Computer vision is the field that deals with understanding how computers can intelligently extract valuable information from digital images or videos. This is one of the fastest-growing research fields and has found enormous applications over the last few years.
Organizations around the world are consistently looking for talent acquisition in this industry. Thus, exploring some of the open-source project ideas in computer vision will help you better understand how it can be applied. Let us take a look at some of the interesting projects you can try out.
4) Regenerating A Target Picture
This is one of the most interesting open-source projects which you can use to imitate a drawing process. This program needs a target image that can be replicated in great detail. You can also specify sampling masks if you need more brush-strokes at certain places in the image. This enables you to control every detail while replicating the target picture.
To work on this project you will need the following python 3 libraries:
a) opencv 3.4.1
b) numpy 1.16.2
c) matplotlib 3.0.3
d) Jupyter Notebook
If you are interested to learn about computer vision, this is one of the best open-source projects you can start exploring. It will give you a great idea of the fundamentals and prepare you to take on complex projects as well.
5) Convert Images to 3D
To build 3D models using 2D images was once a feat that could only be achieved through a deep understanding of design and hands-on experience with tools like Photoshop. However, due to the progress we have made in the field of computer vision, this can now be done using a few lines of code.
This is another interesting open-source project you can try out to understand more about computer vision. It takes a single RGB-D image as an input and converts each of its components to build a 3D photo. You can also try to read about a framework called PyTorch which has been extensively used in this example.
6) PULSE – Building High-Resolution Images
PULSE, which stands for Photo Upsampling via Latent Space Exploration aims to generate high-resolution images from low-resolution image inputs. It can also be used as a face de-pixelizer.
PULSE is thus a classic project in understanding computer vision. It is capable of producing extremely high-resolution images in a completely self-supervised fashion. Before you try out this project idea, explore how the fundamental concept of PULSE works. This will help you in better understanding its code.
7) Transform An Image To A Cartoon
This is a fun project that you can try out and share with your friends. It aims at transforming an image into a cartoon model version. The concept of GAN (Generative Adversarial Networks) is a fundamental part of this project.
GAN is a class of machine learning frameworks originally designed by Ian Goodfellow in 2014. It attempts to regenerate data based on a training set. You can learn more about GAN in this research paper.
While this project is a fun project that does not need a lot of time to implement, it can definitely offer you some key insights on machine learning, computer vision, and GAN. It is currently open-sourced and definitely worth a try.
Other Open Source Data Science Projects
8) Slime Volleyball
This is probably one of the best open-source projects for every beginner to learn from. Slime is a simple game that involves two players who go head to head with each other. The aim is to try and make the ball hit the floor in your opponent’s half. It is a great example of reinforcement learning.
You can directly install this game from pip:
pip install slimevolleygym
9) OpenAI Jukebox
OpenAI is one of the leading AI research and deployment labs in the world and has constantly tried to push the limits of deep-tech and machine learning. Jukebox as the name suggests is their attempt to apply predictive analysis to music. In its essence, this project is a neural network model that has the ability to generate raw music samples.
You can provide the music genre, artist, and lyrics as a sample input, and the neural model can generate a music sample from scratch based on this input. This is a very interesting project that you should definitely try out and explore. You can check it out as it is open-sourced on OpenAI’s official site.
Data Science is a vast field that has huge implications for how we live our lives today and how our relationship with technology is going to evolve in the future. While its potential application in our world is truly fascinating, it can be intimidating when you first try to learn about it.
One of the best ways to get introduced to this domain is by trying out some open-source data science project ideas. Studying them can help you gain some clarity of its fundamentals and an edge to move towards complex problems.
If you are a beginner, you can start by trying out simple image processing projects like PULSE or transforming an image into a cartoon. If you are interested in machine learning, you can try exploring NeoML or face recognition. All of the open-source data science project ideas in this article can help you in moving towards a great career in this booming industry.
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What is an open-source data science project?
An open-source project means that anybody may use, study, edit, and distribute it for any reason. Similarly, an open-source data science project implies that users can utilize already available data science projects to redefine how the projects worked. Most open-source data science projects are practical because they lower the obstacles of starting from scratch and are easy to get into, allowing individuals to propagate and develop projects swiftly. Also, in comparison to closed sources, these projects will enable people to govern their computers. By doing open source data science projects, data science professionals increase their chances of getting hired, as these projects showcase their ability to read, handle and debug.
What are the elements of a data science project?
There are four elements of a Data Science project, which are as follows:
1. The essential step of doing a data science project is to create a strategy about what your project aims to deliver. Open Sourced Projects are aimed at a particular output that needs to be recreated by the end-user. Data needs to be collected according to the strategy.
2. The second step is Engineering. Moulding the project according to your requirement is a task that needs data engineering.
3. Mathematical Models and Data Analysis are the heart of a data science project, and this step involves joining mathematical algorithms and analyzed data.
4.Data Visualization and Operations deals with the presentation of the project in an understandable form.
What are the benefits of doing open source projects?
Contributing to open-source projects adds value to your CV and portfolio. A person or group may desire to open source a project for a variety of reasons.
1.Collaboration: Changes to open source projects can come from anywhere globally, which can help increase exposure.
2. Adoption and remixing: Anyone can utilize open-source programs for almost any purpose. People can even use it to construct other things.
3.Transparency: An open-source project may be inspected by anybody for faults or inconsistencies. Transparency is essential to regulated businesses such as banking, healthcare, and security software.
Doing open source data science projects indicates that you are capable, involved in the community, and passionate.