Updated April 15, 2023
Introduction to GitHub Machine Learning Projects
GitHub hosts millions and millions of Machine Learning Projects. Apart from being a code versioning system and storage system, GitHub offers many more things like connecting people socially with their peers, students with their Teachers / Future Employers, and Developers with Technical experts in their field. Therefore, GitHub provides a perfect platform for software professionals, budding developers, and students to use tons of information available on Machine Learning concepts in its repository and develop projects in this field and share it with the community through GitHub. By this way, students get visibility and find a job; Developer jumps to a higher position, Professionals scale a new level.
What is Machine Learning?
- It is a part of Artificial Intelligence in which the system or model or algorithm developed by developers learns from the data supplied to it.
- The data train the model without any extra coding effort.
- It identifies patterns from its data and develops its own intelligence to make decisions without any human intervention.
Some of the GitHub Machine Learning Projects
GitHub hosts several projects for several use cases listed below:
1. Face Recognition System
This system identifies the employees as they walk past the security gate, and it can be used as attendance, obviating the need for any signature or any card punching, etc.
Various steps in this project:
- The application is built with several Python libraries like dlib and face_recognition.
- Django framework is used in front-end development.
- Develop the data model.
- Capture the photos of all the existing employees at all possible angles.
- Train the model with all the photos stored in the previous step.
- Have the web cam installed at the gate and scan the photo of an employee.
- Compare the scanned photo with stored photos and display the result.
- If any new employee joins, add his photo and train the model with this.
- It has an accuracy of 99.38%
2. Sentiment Analysis
It is an industry-relevant topic and deployed for analyzing reviews of products or services. The reviews can happen through physical or social media, and the analysis rates the reviews whether they are positive or negative. Thus, the organization can get true feedback and make a course correction in the product or service wherever it is needed.
Steps involved:
- Construct the dataset by randomly pulling out samples of reviews in various grades from a superset downloaded from social media sites
- Classifier models such as Random Forest, Multinomial Naive Bayes, Linear Support Vector Classifier (SVC), and Logistic Regression are used to construct data models and validate the review data for results.
- The review data from the user is taken as input, and they are validated with the data model using the relevant classifier.
- The result (positive or negative) is published for all the reviews within a short time.
3. Predictive Analytics
It helps organizations control costs and maximize the revenue from existing assets by reducing downtime and improving their performance. Predictive analytics can predict the maintenance required upfront and avoid breakdowns and predict any asset’s loading pattern or usage methods to increase the operational efficiency of any asset.
Methods:
- Data models with the data on the assets and the related entities will have to be built.
- Models will have to be further augmented with historical data on the performance of the asset is also built.
- Predictive techniques like linear regression, ridge regression, time series, ANOVA, logistic regression neural networks, and decision trees are used to predict the failures and improve the loading pattern.
4. Chatbot
Chatbots replace mundane and routine activities with an automated process, and they can be put into use in customer-facing applications for 24×7 use. In addition, it can use natural language processing to understand the language of the caller.
Steps:
- Develop the bot to take the queries from users and infer them.
- Build the database for a bot to refer to and answer the queries.
- As per the program logic, derive the answer either directly or coin the result with the data extracted from the database.
- Work out a mechanism to respond to the queries of users.
- Train the bot on a continual basis with different types of voices and syllables using tokenization, stemming, and lemmatization NLP techniques.
5. Classification
This method is widely adopted by data scientists in the stratification of data into groups for further analysis. However, a clear understanding of the data and its features is mandatory to classify the data.
Techniques used:
- Data of the target application should be built with the live and historical data.
- Predict the class for the data built.
- Logistic regression, regularization, stochastic gradient descent optimization, and linear classifier are used to classify the data.
- Run through the data, apply the techniques and classify the data.
GitHub Machine Learning Repositories
The following GitHub repositories offer tremendous scope for several projects on Machine learning. Using Libraries, packages, and algorithms provided by GitHub, several projects have been developed.
|
Repository |
Description |
| TensorFlow | An open-source library with data flow graphs that facilitates number crunching. Each node in the graph indicates a mathematical operation, and the edges indicate the tensors (data arrays) flowing. This can be deployed flexibly in any computing environment without any code changes. |
| NLTK | Natural language Tool kit using natural language processing. |
| Scikit-Learn | A machine learning module developed in Python deployed over SciPy. |
| Keras | Python API in neural networks and it is supported in Theno / TensorFlow. They are used more in R & D areas to achieve faster results. |
| Swift AI | ML library developed in Swift. |
| Pattern | Data Mining, NLP, ML tool developed in Python. |
| PredictionIO | REST APIs are used in this open-source ML framework for the deployment of Algorithms and predictive results querying. |
| MXNet | An efficient and flexible deep learning tool. |
Conclusion
GitHub provides an opportunity for developers to create ML projects by giving them a platform to develop, reference/ knowledge to sharpen it, and host it to get better visibility.
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This is a guide to GitHub Machine Learning Projects. Here we discuss the introduction, some of the GitHub machine learning projects, and repositories. You may also have a look at the following articles to learn more –
