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Top 5 Machine Learning Competitive Platforms in 2021

Last Updated on August 13, 2021 by Editorial Team

Author(s): Abid Ali Awan

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Discovering the most popular Machine learning competitive platform in 2021 to showcase your recently acquired skills.

Image by Author | Elements by Starline, Macrovector

Introduction

Reading books on machine learning, taking a 98-hour course,s and developing coding experience is good for a solid foundation but how do you prepare for real-world scenarios? Companies who are looking to hire new graduates want to know how much experience you have with machine learning and data science, they will ask for projects, research, and certificates. I know what you are thinking right now that how I can just start working on a project or get certified when I have no idea where to start my journey Towards Data Science. This is where hackathons and data science competitions come in to save the day. Participating in these competitions will prepare you for real-world scenarios and helps you create the dream portfolio for your resume.

The key advantages of participating in Data Science competitions

  • Learn innovative approaches to solve the problems: You learn new model architecture, processing data, and a new machine learning approach.
  • Dealing with a unique type of data set: You will be dealing with medical data, aerospace, video Imagining, music, etc.
  • Community discussions: Grow together by discussing new ideas, participate in the competition as a team.
  • Portfolio: display your findings on GitHub or publish the research paper, add it to your CV.
  • Leaderboard: you will get an idea of where you stand among top data practitioners.
  • The prize money: You will be eligible to win thousands of dollars prize money and sometimes swags to show off. You might even get Job offer based on your performance.
  • Bragging rights: Show off your rank, show off your project, tell the world how great you are among the bests.

1. Kaggle

Kaggle Competitions

Kaggle provides a complete ecosystem for data scientists to learn and grow together. The Kaggle Competitions vary from data analysis to machine learning. It has the largest data science community which consists of both beginners and experts. Why Kaggle is unique and at the top of the leader board? Because it provides free cloud computing including 30 hours of GPU and TPU every week so that no competitor is left behind due to machine constraints. The Kaggle also has the largest dataset available for machine learning and data science projects. In short, if you are participating in the Kaggle competition you don’t have to worry about downloading a huge data set. With just one click you will have GPU and dataset loading into your kernel. The only thing that is stopping you to win the competition is your knowledge to tackle the problem.

2. DataDriven

Competitions (drivendata.org)

DrivenData is the second platform that got me hooked to competitive learning. During the last part of the DataCamp course, I was given the task to participate in the DrivenData competition called Reboot: Box-Plots for Education. For a beginner, it was a quite challenging task, and it expanded my knowledge about tabular data and classification problems in general. At this moment I realized that the best way to learn any subfield of machine learning is to participate in competitions and try to achieve at least the top 50. DrivenData hosts competition related to social good which includes health, education, and development of the public sector. With simple steps, you can either join the competition or host your competitions just like Kaggle. DrivenData doesn’t provide you with kernel or GPU, you must download the data on your machine and produce either model with code or submit the predictions. Participating in this competition will give you chance to win prize money, but it also provides you with stratification of contribution in a nonprofit project which eventually improves the lives of people living in underdeveloped countries. In short, you are contributing to a worthy cause.

3. AIcrowd

AIcrowd

AIcrowd is a crowdsourcing platform that helps organizations develop, manage, and promote the machine learning challenges AIcrowd. It is quite a unique approach to solving problems related to Artificial Intelligence. Just like Kaggle, people are sharing their code/notebook and the whole community is quite friendly. When you enter a particular competition, the starter kit consists, of a starter code, dataset, complete explanation of how to deal with the problem, and additional resources. You can either run shared code on Google Colab or run it locally. They provide complete support for a beginner and experts. The fun part is that they host all kinds of Data science competitions which range from Reinforcement Learning to Alzheimer’s Detection. If you are looking to add a flashy project to your profile, I will suggest you start with AICrowd.

4. Zindi

Competitions — Zindi

Zindi was a happy accident for me as I was going through beginner-level competitions on ML Contests. The Social Media Sentiment Analysis for Tunisian Arabizi got my attention as I got quite good at text classification by participating in Kaggle’s Disaster Tweets challenge. It took me one month to do research and produce a model based on a transformer which opened a whole new world for me, and I became smarter at solving Natural language problems. Zindi is the first competitive platform based in Africa. It hosts all kinds of Datathon, hackathons, and machine learning competitions. They focus on helping companies based in Africa. The cool thing about Zindi is that anyone can participate in this competition and win the prize. The community does share the solution and problems, but they are not open just like Kaggle.

5. Analytics Vidhya

Contests | Analytics Vidhya

Analytics Vidhya is a community-based knowledge-sharing platform for analytics and data science professionals. They provide great content for beginners in form of blogs and video courses based. The platform also provides users to solve real-world challenges by participating in ongoing hackathons. This hackathon does not just provide hands-on experience of dealing with data but also provides prize money and sometimes job opportunities. Analytics Vidhya is based in India, so it attracts both local and international populations. Most companies offer job opportunities to the top scoring participants so if you ask me, it’s the fastest and easiest way to get hired in India.

So far, I am actively participating in a blogathon where you have submitted articles based on machine learning or new research on data science. Your article is more likely to be published if you have added snippet of code to your article. I have quite pleasant experience with the editorial team, and they have improved me in producing top-tier technical articles.

Conclusion

I am a big fan of Jermy Howard, and he always says that if you want to learn a new skill always learn by practice. You don’t need to learn all the math or statistics; you just need few basics and then get better by working on real-world problems. I stand by his words, and I am sure if you want to excel in data science or machine learning fields then start working on projects. I know sometimes it’s difficult to find a beginner-friendly project and that is where these competitive platforms come in to help you prepare for real-world problems. At first, it will be difficult to understand or even write a single line of code, but with the help of the data science community and by practicing, things will get easier. Participating in these challenges also contributes to your portfolio which will make you a solid candidate in the data science job market.

In the end, I will suggest you just sign up for these platforms and just observe how other participants are doing. You don’t have to immediately participate; you just need to learn and then try to duplicate the work. Learn how they are dealing with problems, and I am sure within no time you will start to achieve the top 10 in these competitions.


Top 5 Machine Learning Competitive Platforms in 2021 was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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