You Must-Try These Open Source Frameworks For Machine Learning

Open Source Frameworks For Machine Learning

Artificial Intelligence has become a core part of technology and Machine Learning is a synonym of it. Numerous top companies or enterprises around the world are constantly working with AI and ML, while many others are turning to it in order to increase their productivity or business activities. 

Whether you are a student, an engineer, an entrepreneur, a data scientist, or just a machine learning enthusiast, Machine Learning is very useful for all in today’s time. As per the report of Gartner, the duo ML and AI are going to create 2.3 million Jobs by 2020 and this growth will increase the popularity of various Machine Learning Frameworks. 

Frameworks For Machine Learning

Let’s Understand What Is Machine Learning- 

Machine Learning is something that acts naturally like humans. It allows software applications to learn from the data, text, image or sound and improve it without being explicitly programmed. Thus, an algorithm of ML teaches the machine to look for a pattern and use that experience to make better decisions without human intervention.

In this article, we will discuss Machine Learning frameworks that are easy to learn and understand. 

Most Popular Open Source Tools for Machine Learning- 

  1. ML for Non-programmers


Knime, a GUI-based workflow platform, has proved to be an excellent tool for beginners that lets them draw end-to-end data science workflows without writing a single line of code. You can create data models with ease with its drag-and-drop feature. 

It provides an intuitive environment where one can create a data model and visualize it. This free and open-source data analytics, reporting and integration platform can make data processing, data modeling, data analysis, and data visualization easier through its various useful components. 

Uber Ludwig- 

Uber Ludwig, built on top of TensorFlow, is another popular tool that the one can use (without writing any code) to test and train deep learning models. The drag-and-drop interface of this tool is an advantage that the one can leverage to quickly test and train deep learning models. You just need to first train your data by providing your CSV file. With the pre-trained model, next, you can then predict the outputs. Now visualization options are available to visualize your data. Using this tool, you can build complex models with ease in no time. 

Read Also: Future Of Artificial Intelligence For 2020

  1. ML for Model-deployment

Model development is one of the best frameworks which provides an excellent environment to make data models. Here we have listed some tools as follow- 


TensorFlow.js is an open-source library for machine learning in JavaScript that the developers use to define, train, and run ML models directly from the web browser.

TensorFlow.js is best for those who are a JavaScript developer who is new to Machine Learning. 

It also provides developers intuitive APIs that allow them to build and train models using JavaScript. 


MLFlow, an open-source platform for the machine learning lifecycle, allows you to track end-to-end ML Lifecycle. It has three major components that help you manage the ML lifecycle. 

  • Tracking

It is used to track, record and compare results and parameters. 

  • Projects

One can use package projects into a reusable form for other members. 

  • Models

It helps you deploy and manage ML libraries in diverse platforms. 

This platform also provides a set of lightweight APIs that you can use with any existing ML application or library (TensorFlow, PyTorch, XGBoost, etc). 

  1. ML for NLP, Computer Vision, and Audio

This is another most useful platform for ML that helps you perform multiple tasks based on Machine Learning. 


It is a software system of Facebook AI Research that provides state-of-the-art object detection algorithms, including Mask R-CNN. Powered by the Caffe2 deep learning framework, this software is built on Python. 


SimpleCV, written in Python, and compatible with Mac, Windows, and Ubuntu Linux, allows you to build a computer vision application by using a collection of software and computer vision libraries. SimpleCV is very similar to OpenCV that allows you to work with images or video streams that come from webcams, IP cameras, FireWire, etc.  

Tesseract OCR 

Tesseract OCR is a software developed by Google which is used to recognize languages or text in images. It supports 100+ languages and can also be programmed to recognize new languages. 

  1. ML for Reinforcement Learning 

Reinforcement Learning is very useful for those who want to train smart agents. Employed by various software and machines, it makes easy to find the best possible behavior or path that it must take in any situation. 

Open AI Gym 

As the name suggests, it helps you train agents to do everything from playing games to walking. This toolkit is best as it uses gym library for developing and comparing reinforcement learning algorithms. 

Unity ML-Agents 

Unity ML-Agents provides the best way to build, test and run new algorithms easily across a new generation of robotics and games. 

  1. ML for Data Miming

It refers to learn from a large amount of data. It involves the study of programs or algorithms that can extract information automatically. If you want to gather data for your data science project, then this tool is best for you. 


Weka is one of the best open-source ML software that one can access through a Graphical User Interface (GUI), standard applications, or a Java API. This software is widely being used for data mining tasks. It also gives access to popular software such as scikit-learn, R, and Deeplearning4j. 


Tools that we discussed above are very useful and even in trending in today’s time. It is very easy to learn them and use them for mobile application development. All these machine learning open source tools are reliable and free to use that you can choose as per project environment that you are looking to take on and work your own way through.

About author

Ravikant Chouhan, soft by nature and creative by mind. Being an Engineer specialized in Computer Science, he carries similar curiosity for both; technical world and automobile industry. He keeps himself indulged in discovering new things and with this passion and the vast knowledge attained by him has helped the company to grow at a faster pace.
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