The global AI software market cap is predicted to reach around 126 billion US dollars by 2025, according to Statista. The exponential technologies are shaping the modern job market and creating new jobs in the process. Of late, 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 in a matter of 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 large tech companies: Such hackathons have high visibility and credibility.
We put together a list of key skills required to crack AI hackathons:
- Strong basics: 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, a data scientist at TheMathCompany and a MachineHack grandmaster, said: “I guess the prerequisites were pretty simple for me. But, of course, it is always Python at the start. But then, for any ML hackathon, it comes down to good domain understanding. Then, dive deep into the sklearn package for error metrics, model algorithms, cross-validation etc. Most importantly, know how to understand data, train and validate.”
- Get hands-on experience: Bookish knowledge will only take one so far. Working on projects where one can apply the concepts taught in a book or a class is more effective than reading a book. As per Mobassir Hossen, the first Kaggle grandmaster from Bangladesh, one should not focus heavily on MOOCs or books, but rather spend more time on hands-on work and stay up to date with the latest research.
- Hyperparameters vs ideas: In a time-based challenge, it’s often easy to lose track of time focussing on the tuning of hyperparameters of an ML model. Instead, the participant should spend more time implementing new ideas based on the EDA and latest data to improve their models.
- Designing 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 holdout folds. One must always run tests on the test set variables distribution and construction against the leaderboard to ensure the correct local validation strategy is used.
- Time is of the essence: It is important to plan the model by taking the timeline into account. It is very easy to lose track of time when focusing on tuning hyperparameters or running cross-validation tests, etc. Adhering to a strict schedule will make sure that you finish your project on time.
- Explore-collaborate: Hackathons provide an overview of the talent pool present in the community. One must explore new possibilities, learn more about what’s trending and collaborate with fellow participants to come up with out-of-the-box ideas.
- The importance of feature engineering: Feature engineering is the process of extracting new data from existing data. It is one of the most important aspects of an AI hackathon as the performance of your model depends on the quality of the dataset used to train the model.
- Perseverance is key: Although not impossible, you are less likely to win a hackathon in the first go. You must be patient and learn from the competitions, accrue practical knowledge and develop a portfolio to reach a competitive level.
- Follow the grandmasters and engage in forums: Engaging regularly in the hackathon forums will bring you up to speed on the cutting-edge techs, tools and approaches. Following grandmasters and picking their brains will give insights into their game plans; what worked for them and what did not.
- Keep evolving: Adaptability is key to ace hackathons. The participants have to roll with the punches and be anti-fragile to overcome minor setbacks. Make sure you have a time-critical approach and a solid plan that account for untoward events. Learn from the mistakes, and develop a robust approach to tackle challenges.
