How can you improve your knowledge in AI areas like NLP, CV, Speech Processing, etc.?
What next after learning the basics of Machine Learning and Deep Learning?
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The Data Science boom started in the early or mid-2010s, and now it's increasing exponentially. This is because every field has large volumes of data that can be used to extract meaningful insights and improve business outputs. The first few steps of any Data Science enthusiast are
Learn Python programming and get hands-on experience with various data science libraries like Numpy, Pandas, Seaborn, Matplotlib, etc.
Revise college level Maths (Linear Algebra, Calculus, Probability, and Statistics) concepts useful for Data Science
Learn Machine Learning and Deep Learning
After learning all the above, the question that pops up in every data science enthusiast's mind is "How to go to the next level?" i.e., "How to further improve his/her Data Science knowledge?". Most of the online courses and books teach you only the basics. After taking a few online courses and reading books, here are the possible ways to improve your Data Science knowledge.
➙ 💡 Kaggle Competitions 💡
Once you are familiar with the basics, the best way to improve your hands-on knowledge is to participate in Kaggle competitions. The Kaggle platform is like a playground for data science enthusiasts. In cricket playground, players frequently practice to improve their batting, bowling, and fielding skills. In the same way, data science enthusiasts can use the Kaggle platform to improve their data science skills.
On the Kaggle platform, you can do the following four things.
Competitions - Kaggle frequently hosts data science competitions related to NLP, Computer Vision, and Speech Processing. You can participate in any of these competitions. Depending on the performance you have achieved, you will be given bronze, silver, and gold medals. Apart from these medals, you will also be assigned a rank.
Datasets - In any task, if you want to apply ML or DL model, the first requirement is the dataset. For every task, there may not be a dataset available readily. So, in such cases, you can scrap the instances, label them and upload the dataset to the Kaggle so that the other users can use it. similar to the competition category, here also you will be given the medals and ranking.
Notebooks - Jupyter notebook is one of the most commonly used tools by the data science community to write code. You can take a dataset, implement a recent approach on the dataset, and then make the Jupyter notebook public so that other users can use the code. Here also, you will be given medals and ranking.
Discussions - Here, you can ask questions and answer the questions posted by other users. Those Kaggle users who will give quality answers to the questions posted will be given medals and rankings.
Above all, a good Kaggle profile helps you to get good internships and job opportunities also. As a beginner, it is recommended to team up with the senior ones for the competitions. Some of the valuable resources that help you to get started with Kaggle are
➙ 💡 Shared tasks (NLP, CV, or Speech Processing) 💡
Shared tasks are very similar to Kaggle competitions. The only difference is that once the results are submitted, shared tasks involve submitting a system description paper. Participating in shared tasks helps you to experience the taste of research and improve your hands-on experience also. Every year, many shared tasks are organized in topics related to NLP, CV, and Speech Processing. As a beginner, it is always recommended to work under experienced ones for one or two shared tasks.
Some of the popular shared tasks are
SemEval (Semantic Evaluation) - Every year, Sem Eval NLP shared tasks are conducted from November to February. For example, SemEval 2023 includes 12 shared tasks related to various NLP tasks.
FIRE (Forum for Information Retrieval Evaluation) - FIRE is an NLP conference organized annually in December. Along with this conference, many shared tasks are organized. For example, FIRE 2022 includes seven shared tasks related to various NLP tasks.
Codalab - You can also find many shared tasks related to NLP, CV, and Speech processing on this platform.
➙ 💡 Open Source Contribution 💡
Open source contribution is the next level option to improve your knowledge further and to showcase your skills also. You can contribute to existing libraries by adding new features or improving existing ones. If there is no library for a specific task, you can create a new library and open-source it. You can also contribute by creating documentation for a library that doesn't have any, or you can expand the documentation by creating coding examples. Google Summer of code is a good platform offering the opportunity to make open-source contributions in various areas, including data science. To learn more about Google Summer of code, you can refer,
➙ 💡 Writing blog posts on interesting topics in NLP, CV, or Speech processing. 💡
This is another way of contributing to the community and showcasing your skills. Writing blog posts on interesting topics improves your writing skills also. A good blog post is always capable of bringing you several opportunities, like internship calls, etc. Please keep in mind that your article should not have any copied content. Some of the popular blogging websites are
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