How to get Started with Kaggle and become a Master in it?
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Kaggle is one of the best communities for hosting Machine Learning Competitions. Machine Learning Competitions can be a great way to learn new things and prove your capabilities.
Why Kaggle?
There are many ways to learn and practice Machine Learning. But Kaggle has specific benefits for which you should seriously consider it. Below are mentioned some of the points
- You can build your portfolio of projects which will help you to showcase your skills in any job interview.
- The problems are well defined and the data is provided readily.
- The great community has very good discussion around competition topics and you can always learn and contribute to it.
- It is difficult to fool yourself to be the best as there are private and public leaderboards.
- Kaggle is a platform which judges on the basis of your knowledge, not on paper degrees or certificates. The better you perform the better exposure you get.
How to start your journey on kaggle?
We recommend to follow four step process to excel on Kaggle
- Pick a Platform / Technology
- Practice on Standard Data Sets
- Practice Old Kaggle Problems
- Compete on Kaggle
The process looks too easy to read but is difficult to implement. It will take a lot of time and effort to complete this process. But if you are disciplined and continuous you will soon become a world class machine learning practitioner. For people who are not beginners and are somewhere in the middle of learning machine learning, directly jumping to step 3 or step 4 shouldn’t be any problem. Anyone who follows these steps will get an above average outcome.
1. Pick a Platform / Technology
Many times it is essential to use multiple technologies but beginners should generally try to stick to one platform or technology. Most of the senior machine learning practitioners recommend to start with Python the simple reason being
- Demand for Python skills is growing.
- Python ecosystem is sufficiently mature (statsmodel, xgboost, pandas, sklearn etc.)
- Python has one of the few best deep learning tools (keras, tensorflow)
- Python is a full featured programming language.
2. Practice on Standard datasets
Once you decide on a platform, it is really important to try different data sets and excel in that technology. Learn how to get the best out of any tool, algorithm or dataset. Treating each data set as a mini competition would really take you ahead.
3. Practice old Kaggle problems
Once you are good in using tools and bringing the best out of any dataset, you can practice old competitions on kaggle, post your solutions and have them evaluated on public and private leaderboards.
4. Compete on Kaggle
Once you are done with all above steps you can confidently take part in live competitions. This is the time when you should aim to get into the top 10% or top 25% of any competition. You should consider working on a single problem until you top or are completely stuck. This is the phase where you spend less time on reading about things and rather spend time implementing them. Below we have also mentioned few additional tips to excel kaggle competitions
- Practice a lot.
- Understand various Evaluation Metrics.
- Study the domain: Business cases, domain knowledge etc.
- Team up: It is essential if you want to finish in the Top 10%.
- Contribute to Forums: Understand different angles to chase a particular problem set.
- Experiment: This is a phase to implement things rather than to plan them.
- Creative: Try to approach problems from different angles.
- Tools: Find and use the best possible tools in the best possible way.
- Tuning: You should tune all model parameters.
Competitive Machine learning is really fun. One must find it fun. Some perseverance is needed to go through any knowledge hump in the initial days of learning any new tool or technology