We have seen so many popular technological innovations in recent years that have made our lives a lot simpler than what it used to be. Machine learning is one of those innovations that have taken the world by a storm. Its applications go far beyond what we see today.
Machine learning, if properly used, has the potential of transforming more than a few aspects or areas of our daily lives. So, how does machine learning technology do all of this? With the help of algorithms that model systems without requiring them to be explicitly programmed. It is great for data analysis as well as automating the processes for creating analytical models.
What doe ML has to do with GitHub? Machine learning involves data-based predictions and algorithm study, and now it has found newer possibilities with GitHub. In this blog, we will list some of the most popular machine learning projects on GitHub. These will be only a few of the more than 100 million projects hosted on GitHub.
What is machine learning?
Machine learning adheres to a well-defined process that includes data preparation, algorithm training, machine learning model generation, and finally, making and improving predictions. Machine learning is based on a very general notion that some basic algorithms have the power of finding out something very interesting within data sets. And the best part is that you don’t have to write any code to get this done. Instead, you will be required to provide the algorithm with data, on which it will base its logic.
Their are different types of machine learning, let us take an example to understand this in a better way. We have a type of algorithm that is known as the classification algorithm. It divides data into separate groups. This algorithm can be used to separate spam from your emails and identify handwritten numbers without having you change the code even slightly. The algorithm remains the same but the difference in its classification logic comes from the different training data it is given.
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What is GitHub?
GitHub is an open-source application that is used to store code on the web. It can be used in several different ways. You can use it to store your projects on the cloud for free or as your online portfolio that let’s potential employers see how good you are at coding. Still, it won’t be wrong to say that GitHub is a lot more than what meets the eye.
It’s not just your code storage; it is rather a tool that is used by developers worldwide to collaborate on projects. It helps developers and teams to improve their codes by having a pool of other developers located in different locations making their valuable contributions.
GitHub is based on Git, which is the version control software that can be easily downloaded on your local machine for further use. Git and GitHub are different from each other; however, we won’t be discussing those differences in this blog. Our focus here is to help you understand how machine learning and GitHub are related, and then list a few machine learning projects that are hosted on GitHub. Also know more about interesting machine learning project ideas for beginners.
GitHub comes with several unique features that have contributed immensely to making it so popular. In addition to being your simple storage, it is your coding hub with very significant social networking connections. It allows individual developers to spread across the length and breadth of this world to make their contributions to multiple projects and teams. Once you get used to how it works, you will come to know all those things that you can do with it. Confused about difference between Git and Github? We have listed the difference between Git and Github in this article.
Top 7 machine learning projects on GitHub
1. Neural Classifier (NLP)
One of the biggest challenges that you may come across in daily life is using text data to perform multi-label classification. When working on NLP problems that are still in their early stages, we use single-label classification. But when it comes to data from the real world, the classification level goes a few notches higher.
When it comes to graded multi-label classification, Neural Classifier can be used to implement neural models much more quickly. One of the best things about Neural Classifiers is that it comes with text encoders that we are used to seeing – Transformer encoder, FastText, and RCNN amongst others. We can use it to perform several classification tasks, including binary-class text classification, multi-label text classification, multi-class text classification, and hierarchical or graded text classification.
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Most people think transfer learning is just about NLP. They are so engrossed in the developments that they forget about other applications of transfer learning. MedicalNet is one of those projects that you will be thrilled to see.
This project combines medical datasets with several different things, such as target organs, pathologies, and diverse modalities to come with larger datasets. And if you know how deep learning models work, you will realize where these large data sets can be used. This is a great open source project that you should definitely work on.
This is a Big Data platform that is built for the Internet of Things or IOT, IT infrastructure, Connected Cars, and Industrial IoT amongst other things. It provides an entire set of data engineering chores. It was rated amongst the best new projects hosted on GitHub.
Bidirectional Encoder Representations from Transformers or BERT is again a very popular machine learning project on GitHub. BERT is a new addition to the projects that are related to the representations of language. It is a bidirectional system and the very first unsupervised one for NLP pre-training.
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5. Video object removal
The way modern machines deal with and manipulate images has reached a very advanced stage. If you want to become a computer vision specialist, you need to be on the top of your game when it comes to the detection of objects in images.
It is not at all easy when you are asked to work on videos and build bounding boxes around different objects in them. This is a complex task because objects are dynamic in nature. Machine learning training helps you accomplish these tasks with relative ease.
This machine learning project on GitHub has resources that make understanding and using TensorFlow very easy. It has a collection of TensorFlow projects, experiments, and libraries. TensorFlow open-source machine learning program that has different community resources, tools, and libraries for helping you create the most advanced projects using machine learning. Developers can use TensorFlow to build and deploy machine learning applications at a much faster pace.
7. FacebookResearch’s fastText
This is a FacebookResearch’s free open-source library that provides a cost-effective way of learning word representations. fasText is lightweight and provides you a deep understanding of sentence classifiers as well as text representations. This is a great library for people interested in NLP.
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Why should use Git for your Machine Learning Project
When you think about creating a project for your resume to land a great job, you want to make sure that this project is completed and decent enough to show top companies. For this, you must work on some bigger projects rather than small projects because if you are hired, it may work in your favour. You should demonstrate that you can design, test, and launch different features while still independently creating other sections of the code. You should also demonstrate that you can code in an effective fashion for your GitHub Machine Learning projects.
Git is a platform that enables you to keep track –
- Remote repositories
For creating Github Machine Learning Projects, you must understand the basics of the platform.
Git views the files as a collection of snapshots. Git effectively takes a snapshot of how all of your files now appear and keeps a reference to this snapshot if you want the version control system, i.e. Git, to save your most recent modifications. Git is very effective since it saves a link to the prior identical version of any files that haven’t changed and is best to create some great Machine Learning projects GitHub.
A file can be in one of three states in Git:
What is the basic workflow of Github
- You modify the files
- Add the files to the staging area with the final changes
- Now, you commit. You can save the files as if they were in the staging area. This helps you store the file in GitHub permanently. These files are called committed files.
Now that you have a basic understanding of the platform, you must know how to practice it before creating the final machine learning projects GitHub –
- Create ML projects Github
Create a file on your device or where you want that project to be, either manually or via cd, to start a new project.
$ cd /Users/user/my_project (MacOs or Linux)
$ cd C:/Users/user/my_project (Windows)
Post that, you must type in
$ git init
This will create a subdirectory for you, making the process easier.
- With this platform, we have the option to create files and enter data into them. They are in the “modified” condition. We may use the command ‘add’ to put them in the “staged” state.
$ git add HelloWorld.py
To commit the file to a local repository, we can write the following code –
$ git commit -m ‘This message describes what changes are to be made’
Most tasks simply require local files. However, you will need to master a few additional tools if you plan to collaborate on your project with other people or if you want to showcase your work on websites like Github.com.
A server hosts remote repositories. GitHub is one pretty well-liked location. A name and url are attached to a remote repository. Once you have a local GitHub repository, creating a remote repository is fairly easy. Another step toward ML projects GitHub.
Using branches, you may securely experiment with new ideas, create functionality, or address errors in a specific part of your repository.
A branch is always made from an existing branch. Normally, you could build a new branch off of the repository’s default branch.
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This blog discusses machine learning, GitHub, and how they are linked to one another. We listed a few machine learning projects that are hosted on GitHub and provided a brief understanding of how these projects work and who they can be useful to.
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What are the limitations of machine learning?
Machine learning is a very powerful tool for solving a wide range of problems in all industries. However, there are also some limitations in using machine learning: 1. Machine Learning is costly, you need to spend lots of money to buy the software and train data-sets. 2. Machine Learning is not easy to get started with, the open source machine learning libraries are very difficult to use. 3. Machine Learning is not an instant solution, you should spend time and effort to understand the data. 4. Machine Learning is not for everyone, you need to know more about data science, statistics and math. 5. Machine learning can only be used for prediction and estimation, so you still need to do some human work.
How to start learning machine learning?
Machine learning is a hot topic and the smartest way to enter this industry is to learn it from the basics and understand how it works. Machine learning is essentially a set of algorithms that are used to analyze and make decisions using historical data. Machine learning is a very broad term and there is a lot to learn and it might seem overwhelming. So, we recommend you to start with a simple algorithm like Linear Regression and then move to more advanced approaches like Gradient Boosting and Deep Learning.
What are some cool things that you can do with machine learning?
You can develop a model to predict your players behavior (or your users behavior), for example, based on their location, time of the day, device, etc. You can use this model to automatically trigger an action. For example, send push notifications with special offer to users when they are near your store. This is the easiest way to make money from data science. If you want to become a machine learning engineer, you will be in high demand. Most companies, from small startups to Google, Amazon, IBM, Facebook and more, heavily invest in machine learning.