Machine learning isn't Kaggle competitions
1–10 of 24 posts
Re: Machine learning isn't Kaggle competitions
#2except building models is the essence of machine learning
Re: Machine learning isn't Kaggle competitions
#3I'm selecting my final year (undergrad) project in the next couple of weeks. One of my professors posted a project that will simply be entering a competition on Kaggle. I don't know much about machine learning yet and I thought that this would be a great way to pick up something I've always been interested in.
What does HN think of doing this?
Re: Machine learning isn't Kaggle competitions
#4I'm selecting my final year (undergrad) project in the next couple of weeks. One of my professors posted a project that will simply be entering a competition on Kaggle. I don't know much about machine learning yet and I thought that this would be a great way to pick up something I've always been interested in. What does HN think of doing this?
It sounds like a great idea. Even though the article is entirely correct in that Kaggle competitions are only a small part of machine learning, they are still a good way to learn some aspects.
Even the data-cleaning that the author claims is missing from Kaggle competitions, is not really missing, it's just Kaggle have made your data cleaning much simpler.
The main advice I would give is to find a problem where you can get an understanding of the data, i.e. not just treat it like a black box.
Re: Machine learning isn't Kaggle competitions
#5It sounds like someone justifying their own shortcomings. "Since kaggle proves I am not that good at the 'math' part of machine learning, I will dismiss it by pointing out that it does not include all of the steps necessary to make a machine learning approach work for a business"
Re: Machine learning isn't Kaggle competitions
#6This is exactly my experience and that of most people in the field I've worked with. In fact, many people have said the quality/processing of data is much more important than the machine learning model you use.
The exception is those fields that have physical data, like computer vision or speech recognition. In those fields, the actual model matters a lot more.
I think every job has this tendency, where the public focus on the most exciting and interesting part, and ignore the mundane but also extremely important parts.
And on the matter of skill/ability, in spite of not being a Kaggle winner, the author couldn't do their job without a good understanding of machine learning models. To do machine learning in practice, you must know both the software development/systems side and the maths/stats/ML side.
Re: Machine learning isn't Kaggle competitions
#7Machine Learning is just a part of many steps required to solve a practical problem. In my opinion although data cleansing/pre processing is important , but so are other things like feature engineering, model selection, understanding of machine learning models. And kaggle competitions are great way to practically learn these other things.
Re: Machine learning isn't Kaggle competitions
#8I think it is more accurate to say that data science isn't Kaggle. The process of taking a data set and fitting it to the most robust model is certainly machine learning.
Re: Machine learning isn't Kaggle competitions
#9It's a shame there is a huge disconnect between what data scientists ACTUALLY do in their day to day vs what's published in the media. Many people see machine learning as synonymous with data science when a data scientist's real job is to leverage data to achieve business objectives.
This has a wide range of implications from producing reports, to visualizations for understanding different kinds of trends.
Model making is, imo, the fun part of the process, but by far from what actually needs to be done.
That being said, kaggle is a great platform to learn on.
There is a lot of value in understanding how to train a model, but it's only as valuable as understanding how to take data from disparate sources, cleaning and normalizing it, doing some EDA, and then understanding AFTER all that if you need a model to achieve your goals.
Re: Machine learning isn't Kaggle competitions
#10"Once I played against an NBA player and I couldn't do anything against him. I felt sad and demoralized. I still don't think I could win against an NBA player, but everyday I do (among other things) play basketball with my friends! And, you know, there're so many problems in basketball aside of dribbling and stuff: you have to find space to play with your friends, you have to convince the guy that doesn't want to play this evening. Once I didn't have time to play that day, and if it ever happened to you: it doesn't matter how good you are at dribbling if you don't have time to play. So I decided to write an article for you guys to know: that stuff they do at NBA has nothing to do with basketball!"