3 Top Machine Learning Algorithms | Towards Data Science

IntroductionLSTMXGBoostCatBoostSummaryReferences
Introduction
LSTM
XGBoost
CatBoost
Summary
References
There are several machine learning algorithms that you can either study in academia or employ in the workplace, but some might be more useful than others to practice and apply. It can ultimately depend on a few factors, like the problem you are solving, the data you have, the resources your company has, and so on. However, I have seen a few algorithms that seem to be more unique, popular, or more accurate. Keep in mind, these top algorithms are formed by my opinion, and experience as a professional data scientist and interviewer. With that in mind, let’s discuss the reasons why you would want to show off these machine learning algorithms in particular.
I surprisingly have not seen many people include this algorithm, LSTM (long short-term memory), on their resume at all; it is usually one of the popular tree-based algorithms and perhaps a SARIMA model (seasonal auto-regressive integrated moving average) instead. With that being said, the main reason for including this machine learning algorithm on your resume is because it is unique and can essentially set you apart from other applicants.
Not only that, but this algorithm is also useful and can be highly accurate, of course, depending on other factors, but the opportunity does seem to be better than when compared to other similar algorithms. It can also be used for classification and time-series-based problems, as well as be more controllable or flexible in its process.
Here are some of the benefits of putting LSTM on your resume:
It is unique
It is more complex so some people are less willing to learn or master this algorithm
It can be better than RNN because of its flexibility
It has the opportunity to be more accurate or more predictively powerful
As you can see, including and knowing this algorithm can make you a unique applicant.
Perhaps the most popular of the three, this algorithm is more of a core algorithm to include in your toolkit. This algorithm is one that you should know and, of course, include on your resume. Because it is so popular already, it has the benefit of having a lot more documentation.
XGBoot is better at a lot of things, but if you were to rank the tree algorithms I would say it goes from decision trees → random forest → XGBoost, then to another, a new algorithm that I include below.
Here are some further reasons why you would put XGBoost on your resume:
High performance
Often one of the most accurate algorithms, so you can be more competitive
Tons of documentation, examples, so interviewers are familiar with discussing this algorithm more so than the other two
There are countless reasons to use the algorithm itself, but for the purpose of including it on your resume, it will show that you are a steady applicant that has the base knowledge that can be applied to many more algorithms.
Perhaps the most superior, and newest, is CatBoost. Apply everything you know about XGBoost, but add that it is even faster, easier to use, more accurate, and can deal with categorical features exceptionally well. It is so new that some interviewers might not even know about this algorithm yet, so you can really show off on your resume when you include this algorithm, but it is not so far off that they cannot understand it, especially if they are familiar with most tree algorithms and especially XGBoost.
To add to the previously mentioned note, now we can include the final rank to include CatBoost:
Decision trees → random forest → XGBoost → CatBoost
Here are some more reasons you should include CatBoost on your data science resume:
Very new, and will make you more of a competitive applicant
Can show the interviews that you can work with an algorithm that is highly accurate and efficient
Can show that you can work with an algorithm that deals with categorical future well, nearly all algorithms perform poorly in someway with categorical features, like using one-hot-encoding that creates a large, sparse dataframe
... Where you could instead retain the categorical feature column as it is originally, making it easier to collaborate with stakeholders like software engineers, ML engineers, or product matters
As you can see, I included the best for last. CatBoost is a great algorithm for a variety of reasons and is something you should include on your resume.
You cannot go wrong with any of these machine learning algorithms, they are highly useful for different reasons. Whether you are predicting a continuous target, or trying to classify a category, or even predicting a time-series target, these algorithms can all help to produce great results, and that is valuable for you in an interview.
To summarize, here at the top three machine learning algorithms that you should include on your data science resume:
* LSTM* XGBoost* CatBoost
I hope you found my article both interesting and useful. Please feel free to comment down below if you agree or disagree with these top machine learning algorithms. Why or why not? What other algorithms do you think you include on your data science resume? These can certainly be clarified even further, but I hope I was able to shed some light on data science resumes. Thank you for reading!
I am not affiliated with any of these companies.
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[1] Photo by Kote Puerto on Unsplash, (2018)
[2] Photo by Fakurian Design on Unsplash, (2021)
[3] Photo by Johann Siemens on Unsplash, (2014)
[4] Photo by Ludemeula Fernandes on Unsplash, (2017)