In any profession, it is important to create an image of oneself, which is reflected by materials included in a portfolio. It does not matter if an individual is a veteran or a fresher because a good portfolio always works in the direction of betterment. Through a well-organised machine learning portfolio, one can increase his or her value in the industry as a portfolio is similar to a brochure that a buyer goes through before making a purchase.
In this article, we will discuss how to create a machine learning portfolio, which will help an individual grab a better opportunity.
Projects and Courses
The number of projects done by the candidate will speak for the experience that has been gained firsthand. It is wise to work on ones, which have an impact on the day-to-day life of ordinary people and is highly appealable. A minimum number of five projects must be under the hood to create a good portfolio. While focusing on doing different types of individual projects is essential, one should also collaborate on others’ projects to demonstrate their capabilities. Some of the most popular open-source ML projects you can work on are TensorFlow or Neon.
The second important element to show off is the certified courses in machine learning. In today’s era, courses offer a wide range of experience and exposure into the field of machine learning and also pinpoints on the fact that a candidate is professionally trained to handle certain aspects. “At Tata Elxsi we look for certification completion (paid ones) because it means that the applicant is serious about the course and the subject being taught,” said Biswajit Biswas, Chief Data Scientist at Tata Elxsi.
Research Paper and Teaching
A research paper could be a vital addition in creating a portfolio. Any invention or new development in the world of technology begins its journey from a research paper. A candidate can try to put something new in front of the world by doing research, which could benefit the industry and society. In the scenario where a research paper becomes successful, identity in the data science will be created automatically. One can also indulge in teaching others by creating blogs and vlogs to establish a name for herself or himself in the community, which could act as a massive boost.
Hackathons and Conferences
Hackathons are an ideal place to make a name for themselves by participating against thousands of data scientists. A brilliant place to start off is MachineHack, which allows a user to test and practice machine learning skills. Once instilled with confidence, a user can take on the real-world by participating in various hackathons.
Moving on, conference and summits are a good place to take part off. The spell will work only when one can manage to be a speaker or win an award in these gatherings, which could be done by winning hackathons or writing extensive research papers that are good enough to gain a candidate some limelight.
Dataset Sources
It is of utmost importance to practice with a variety of data sets. Finding resources for datasets could be a tough job. However, there are several free resources from where datasets can be retrieved such as Kaggle, Machine Learning Repository and ImageNet. Kaggle covers a wide range of datasets along with several examples of projects. Also, one can take part in competitions after some practice to understand the depth. Machine Learning Repository is considered one of the finest as it provides various academic datasets. A number of other datasets to look at are Enroll email dataset, Parkinson Dataset and Google Trends Data Portal. One can further click on the link to know more about a variety of datasets.
Marketing
Machine learning is not just a subset of artificial intelligence, but it has turned into a community of its own. It is imperative for anyone, be a veteran or fresher to have a good image in the community due to which marketing is very important. The best place to start is GitHub, where one can showcase their projects along with having deep discussions about machine learning with millions of developers and experts. Also, recruiters often go through GitHub repository of a user, so it is best to keep the projects open for the public eye. LinkedIn is another place to showcase one’s project.
Not to mention, one can compete in Kaggle as well to become a rank holder in the community and contribute by providing different datasets, which could be beneficial to other users. In this manner, followers can be gained, which will help the portfolio weigh heavy in the eyes of a recruiter.
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