10 Must-Try Open Source Tools for Machine Learning

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@ michaelgarbade Michael Co-founder and CEO of Education Ecosystem.

Machine learning is the future. But will machines ever extinct humans?

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That’s a far-fetched idea for sure!

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As a machine learning developer, you surely want to succeed in your goals. That’s where open-source tools for machine learning comes in.

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The machine learning open-source community is active. If you are into open-source, you will notice that there are plenty of machine learning resources. This also includes tools that you can use in your projects.

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In this article, we will go through the ten must-know open source tools for machine learning. These tools will help you improve your workflow.

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10 Must-Know Open Source Tools for Machine Learning

For non-programmers

If you are new to programming or machine learning, you need to use tools that match your experience. For beginners, we suggest using these two tools.

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Knime: Knime is an excellent tool that lets you do end-to-end data science workflows without the need to write a single line of code.

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To help newbies, it comes with a drag-and-drop interface. The interface is clear, and you can create and productionize using it.

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The first step is to do data gathering and transforming using the tool.

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Once done, you can then create a model and visualize it. When it comes to production, you can deploy and manage your data science project.

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Lastly, you can leverage your implementation by producing insights using Knime.

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Uber Ludwig: Uber Ludwig is another excellent tool for beginners. With it, you can quickly test and train deep learning models. Writing code is optional, and you can use the drag and drop interface to your advantage.

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To get started, you need to first train your data by loading your CSV file. By using the pre-trained model, you can then predict the output targets. Lastly, you can visualize your data using the available visualization options.

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If you are a beginner in programming, you can also use their extensive API and train models in Python.

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Below is the code for training models using API.

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Fascinated? Check out the user guide here.

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For Model-Deployment

Model deployment is one of the critical aspects of machine learning. To help you in the process, we have listed the following tools.

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TensorFlow.js- TensorFlow.js lets you build and deploy machine learning models directly from the web. It uses JavaScript for running on the web.

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You can also utilize Node.js for it. With it, you can not only run existing models but also retrain existing models.

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It offers intuitive APIs that let you build and train models using JavaScript, and that too on a web browser.

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You can also check out TensorFlow Lite if you want to develop on the mobile.

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MLFlow: MLFlow lets you tackle end-to-end machine learning lifecycle. It comes with three major components.

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MLflow Tracking - takes care of experiments by recording and comparing results and parameters

MLflow Projects - let you package projects into reusable form for other members

MLflow Models - helps you to deploy and manage ML libraries in diverse platforms

Another amazing feature of MLFlow is that it is library-agnostic. This means that you can use it with other machine learning library without any compatibility issues. To achieve library-agonistic behavior, it utilizes REST API and CLI.

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For NLP, Computer Vision and Audio

There are other, handy tools that you can utilize for doing different things in machine learning.

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Detectron: If you are looking for state-of-the-art object detection algorithm, then you can not go wrong with Detectron.

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It is developed by Facebook and is part of their AI Research software system. It utilizes Caffe2 deep learning framework and Python.

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SimpleCV: SimpleCV, an open-source framework, lets you build a computer vision application. It is similar to OpenCV and enables you to get access to high-level computer vision libraries. This means that you do not have to worry about intricate concepts.

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With it, you can make computer vision projects without investing too much time on basics. After all, it is named as SimpleCV for a reason.

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Tesseract OCR: Tesseract OCR is a powerful Optical Character Recognition software that lets you recognize languages.

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It supports more than 100+ languages and can also be programmed to recognize new languages.

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For Reinforcement learning

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If you want to train smart agents, then you need to take help of reinforcement learning.

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Open AI Gym: Open AI Gym lets you train your agents to do almost anything, including walking, playing games, and so on. It does it with the help of easy-to-use reinforcement learning tasks suite.

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Unity ML Agents: Unity offers their open source unity plugin, the Unity ML agents that let you develop smart agents that can be used within games.

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For Data Mining

If you wish to gather data for your data science project, you can use the following tool.

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Weka: Weka is used for data mining tasks. It does it with the help of machine learning algorithm designed for data mining. With it, you can find a lot of things, including classification, preparation, regression, clustering, visualization, and association rules mining.

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The project is open-source, and GNU licensed.

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Conclusion

Machine learning is transforming how we interact with the world. It is making our lives easier and ensuring that we build a futuristic world.

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Python Sklearn and Keras are also great tools that you can use to do machine learning models.

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So, which machine learning open source tools do you like?

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Comment below and let us know.

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