Top 10 tips for successful ML hackathons

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The global AI software market capitalization is expected to reach around US$126 billion by 2025, according to Statista. Exponential technologies are shaping the modern labor market and creating new jobs in the process. Lately, hackathons have become a big part of tech companies’ hiring strategies. And for good reasons.

Source: Statista

TAIKAI claimed that 40% of their hackathon participants were hired by companies within months. AI/ML hackathons like MachineHack, Kaggle, NeurIPS, etc. are the best platforms to network with industry experts, collaborate with peers and get recruited by big tech companies: these hackathons have high visibility and credibility.

We’ve put together a list of the key skills required to crack AI hackathons:

  1. Solid foundations: A good fundamental knowledge in subjects such as programming language, mathematical concepts, machine learning methods, deep learning, etc. is a necessity for such competitions. Rajat Rajan, data scientist at TheMathCompany and grandmaster of MachineHack, said, “I guess the prerequisites were pretty straightforward for me. But, of course, it’s always Python at the start. But then, for any ML hackathon, it comes down to a good understanding of the domain. Next, dive deep into the sklearn package for error metrics, model algorithms, cross-validation, and more. Most importantly, know how to understand data, train and validate.
  1. Get hands-on experience: Book knowledge will only take one so far. Working on projects where one can apply the concepts taught in a book or course is more effective than reading a book. According to Mobassir Hossen, Bangladesh’s first Kaggle Grandmaster, one should not focus on MOOCs or books, but rather spend more time on practical work and keep up to date with the latest research.
  1. Hyperparameters vs. Ideas: In a time-based challenge, it’s often easy to lose track of time by focusing on tuning the hyperparameters of an ML model. Instead, the participant should spend more time implementing new ideas based on EDA and the latest data to improve their models.
  1. Design a strong validation strategy: A proper validation strategy can be the difference between winning and losing. Defining it is more complicated than cross validation or exclusion folds. Always run tests on the distribution and construction of the test set variables against the collation to ensure that the correct local validation strategy is used.
  1. Time is running out: it is important to plan the model taking into account the calendar. It’s very easy to lose track of time when you’re focused on tuning hyperparameters or running cross-validation tests, etc. Following a strict schedule will allow you to complete your project on time.
  1. Explore-collaborate: Hackathons provide insight into the talent pool in the community. You have to explore new possibilities, learn more about trends and collaborate with other participants to come up with original ideas.
  1. The Importance of Feature Engineering: Feature engineering is the process of extracting new data from existing data. This is one of the most important aspects of an AI hackathon because the performance of your model depends on the quality of the dataset used to train the model.
  1. Persistence is key: while it’s not impossible, you’re less likely to win a hackathon the first time. You must be patient and learn from competitions, acquire practical knowledge and develop a portfolio to reach a competitive level.
  1. Follow the grandmasters and participate in forums: Regularly participating in the hackathon forums will allow you to keep up to date with cutting-edge technologies, tools and approaches. Following the Grandmasters and picking their masterminds will provide insight into their game plans; what worked for them and what didn’t.
  2. Keep Evolving: Adaptability is key to great hackathons. Participants must roll with the punches and be anti-fragile to overcome small setbacks. Make sure you have an urgent approach and a solid plan that takes untoward events into account. Learn from your mistakes and develop a robust approach to meeting challenges.
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