- They cover an area you’re personally interested in.
- Data is readily available that’s well-suited to addressing the problem (otherwise
the bulk of your time will go here).
- You can work with the data (or some relevant subset of it) comfortably on a single machine.
Don’t have a problem that comes to mind? No worries! We provide a nice onramp of
machine learning problems at Kaggle through our getting started competition series.
Start off on the
titanic competition.
2. Make a quick, dirty, hacky, end-to-end solution to your problem.
It’s really easy to get bogged down in one implementation detail or carefully tuning the
wrong machine learning algorithm. You want to avoid this.
Your goal here is to get something super basic in place as quickly as possible that covers
the end-to-end problem, from reading in the data, processing it into a form suitable for
machine learning, training a basic model, creating a result, and evaluating its performance.
3. Evolve and improve your initial solution.
Now that you have a functional baseline, it’s time to get creative. Try improving each
component of your initial solution, and measure the impact to see where it makes sense
to spend time. Many times acquiring more data or improving data cleaning and
preprocessing steps have a higher ROI than optimizing the machine learning models
themselves.
Part of this step should include being hands-on with the data - inspecting individual rows
and visualizing distributions to have a better understanding of it's structure and oddities.
4. Write up and share your solution.
The best way to get feedback on your solution is to write it up and share it. Writing about
your solution mean you’ll engage with it in a new way and understand it better. This
enables others to understand what you’ve done and provide feedback, helping you learn.
It also kickstarts your machine learning portfolio, which will help showcase your abilities
and get a job.
Kaggle Datasets and
Kaggle Kernels are an effective way to share your data and solution,
get feedback from others, and also see how others extend your problem. This starts
fleshing out your Kaggle profile as well.
5. Repeat #1-4 across a diverse set of problems.
Now that you’ve done this for a single problem that you’re interested in, do this several
more times across a different set of domains.
Did you start off with tabular data? Work on a problem that involves less structured text,
and another that solves images.
Was the machine learning problem structured for you initially? Alot of the creative and
valuable work is figuring out how to go from a loosely-defined business or research
objective to a well-defined machine learning problem in the first place. Work through this
for one problem type.
Kaggle Competitions and
Kaggle Datasets provide a good starting point for both
well-defined machine learning problems and raw data sources that’d be suitable
for machine learning.
6. Seriously compete in a Kaggle competition (if you’ve not already done so).
Giving your best shot at the same problem that thousands of others are hard at work
on is a tremendous learning opportunity: it forces you to iterate on the problem over
and over again, and then exposes you to what works effectively on the problem.
The forums for an individual competition are a rich resource on how others are
approaching it and debugging issues with your approach, kernels provide exploratory
insights about the data along with an easy way to get started on a problem, and
the winning blog posts at the end showcase what ultimately worked best.
Kaggle Competitions also provide a unique opportunity to team up with others. Our
community has a diverse set of background and skills, so everyone has something to
teach and something to learn. You never know, you may meet your future colleague
on Kaggle!
7. Apply machine learning professionally.
This enables you to spend most of your time on machine learning and really helps you
level up. Deciding on the type of role you’d like to pursue and building a personal
portfolio of projects related to this is a strong starting point. If you’re not ready to start
interviewing for machine learning positions, then taking on new projects in your current
role, seeking consulting opportunities, and getting involved with civic hackathons and
data-related community service opportunities are additional ways to get a foothold.
Professional work often requires and is greatly enhanced by strong programming
abilities - improving this with focused projects yields many downstream payoffs.
Valuable opportunities for professional machine learning work include:
- Applying machine learning in production systems.
- Focusing on machine learning research and pushing the state of the art forward.
- Leveraging machine learning in exploratory analyses to improve product and
business decisions.
