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5 Online Platforms To Practice Machine Learning Problems

5 Online Platforms To Practice Machine Learning Problems

Ambika Choudhury
  • CloudXLab
  • Google Colab
  • Kaggle
  • MachineHack
  • OpenML

The best way to learn anything is by practising it. A number of theories and tutorials are available online as well as offline to learn machine learning. But one cannot truly learn until and unless one truly gets some hands-on training to learn how to actually solve the problems.  

In this article, we list down five online platforms where a machine learning enthusiast can practice computational applications.

(The list is in alphabetical order)

1| CloudXLab

CloudXLab is an online cloud platform which provides online video courses, auto-assessment tests, BootML which is the UI-based machine learning model code generator as well as 24 hours of lab access with Jupyter environment. In this platform, you can learn paid online courses like Big data with Hadoop and Spark, Machine Learning Specialisation, Python for Data Science, Deep learning and much more. 

There are also free tutorials available on Linux basics, introduction to Python, NumPy for machine learning and much more. You can either enroll for course and lab or you can enroll only for practicing in the lab. There are packages for lab enrollment which includes the duration of one to six months.


Click here for more.

2| Google Colab

Google Colaboratory is a platform built on top of the Jupyter Notebook environment which runs entirely on Google Cloud Platform (GCP). This platform provides GPU which is free of cost and supports Python 2 and 3 versions. With the help of Colab, one can not only improve machine learning coding skills but also learn to develop deep learning applications. You can also learn to work with popular deep learning libraries such as Keras, TensorFlow, OpenCV and others. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.

Click here to know more.   

3| Kaggle

Kaggle is a no-setup, customisable, Jupyter Notebooks environment by Google. This platform is very much similar to Google Colab in the aspect that both the platforms provide free GPUs along with a large community of published data and code. With over 19,000 public datasets and 200,000 public notebooks, this platform provides one of the best places to practice data science computational problems. This cloud computational environment supports Python 3 and R and enables reproducible and collaborative analysis where one can explore and run machine learning codes seamlessly.

Click here to know more. 

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4| MachineHack

MachineHack is an online platform by Analytics India Magazine for Machine Learning Hackathons where one can test and practice their machine learning skills. In this platform, a beginner can learn and practice how to deploy popular machine learning algorithms such as Linear Regression, Multiple Linear Regression, Support Vector Regression, Extreme Gradient Boosting Classification, Naive Bayes, K-Nearest Neighbours, and other such algorithms on datasets provided by the site. The coolest thing about this platform is one can practice as many times as he wants and there is no limit to the practice sessions. 

Click here to know more.

5| OpenML

OpenML is an open, collaborative, and automated machine learning environment which includes several specific features such as find or add data to analyse, download or create computational tasks, find or add data analysis flows and much more. This open science platform for machine learning is a cross-platform programming environment for sharing and organising data, machine learning algorithms, and experiments. 

It is designed to create a frictionless networked ecosystem where one can easily integrate into an existing code or environment. It provides a useful learning and working environment for students, citizen scientists and practitioners. OpenML is one of the perfect environments to explore and reuse the best solutions for specific analysis problems as well as interact with the scientific community.

Click here to know more.

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