Follow these Strategies to Win a Machine Learning Hackathon – Analytics Insight

Machine Learning Hackathon

At the end of every machine learning hackathon, participants will have amazing learning

The tech sphere brings exciting things for tech-savvy people literally every day. Whether you are a beginner or an expert, you can still participate in many tech-related competitions, conferences, events, and seminars to shape your skills. One such effective event to test your talent is the machine learning hackathon. Today, machine learning is emerging as a powerful technology behind every digital mechanism. Many people are aspiring to become well-versed in machine learning. However, they can enhance their practical skills by participating in machine learning hackathons. Machine learning hackathons are conducted specifically for programmers, coders, and others involved in the development of software. Other professionals like interface designers, project managers, domain experts, graphic designers, and others who work closely on software development will also try their hand at the competition. During a hackathon, participants will be asked to create a working software with the datasets and models provided within a limited time. Fortunately, at the end of every machine learning hackathon, participants will have amazing learning. However, winning a hackathon is completely different from participating in one for the experience. If you are planning to be the star of the event, then you should follow certain strategies to win a machine learning hackathon.

Strategies to Win a Machine Learning Hackathon

Start from Short Hackathons

If you are new to machine learning and want to give it a try in a hackathon, then short or online hackathons should be your first choice. Remember victory comes with experience. Therefore, directly jumping into long hackathons won’t secure you the winner title. Start from small which are below 24-hours and then move on to long hackathons. But make sure you are well-organized and prepared when you shift from short to long competitions.

Join Hands with an Experienced Person

As a beginner, you should try to enter a hackathon with someone who is experienced and has great knowledge about machine learning. This will help you both learn throughout the process and win the prize if possible. Besides, make sure you join hands with somebody who can contradict your basic knowledge. For example, if you are a developer, join a league with a person who has business knowledge. With the combination of business and developing skills, you can surely bank the first place.

Formulate a Diverse Team

Diverse here doesn’t mean ideology but talent. If all the members of the team are developers and have zero knowledge about other perspectives, then the result will completely be a slider on one side. Therefore, you should ensure that everybody has something new and different from others to offer in your team. Also, try to go with clients who can support you throughout the challenge. Ask them to constantly check your development and give directions if necessary.

Sort Out Your Goal

Success in a hackathon comes in two different ways. One is winning the competition and the other is impressing the client. Therefore, before beginning the work, sort out which one you are going to concentrate on. If you are planning to impress the judges and win the hackathon, then you should go with shiny software and an outstanding presentation. But it is quite opposite to impress the client. You should come up with useful software that can be utilized even after the competition is over if you planning to impress your sponsor.

Prepare the Perfect Dataset

Data is the core of software development along with the coding. Therefore, always make sure you are preparing the data well before starting core operations. But scraping data is time-consuming and can be tricky when it comes to dynamically generated content. Instead, try to go with publicly available data provided by IMDb or maybe the Kaggle dataset. One thing to keep in mind is that many winning teams usually save their time by seeking readily available data.

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