Although we find ourselves in unprecedented times of uncertainty, current events have shown just how valuable the fields of Data Science and Computer Science truly are. Technologies — like the Johns Hopkins dashboard, contact tracing, and data analytics — compose the “virtual front lines” of our attack on the pandemic and continuously prove to be driving sources of change. However one question still remains: Exactly how valuable are these fields?
In this article we will take a snapshot of where Data Science is in 2020 and take a deep dive into where salaries and other forms of compensation stand.
In this article, we will learn how to code linear regression, the “hello world” of machine learning, in less than 50 lines of python! Data scientists and programmers frequently incorporate machine learning algorithms with the use of third party libraries like scikit-learn but fail to understand how they work under the hood.
Learning how to code the algorithm from scratch will not only increase our programming proficiency, but also provide a much deeper understanding of the topic at hand.
When I wanted to learn Machine Learning and began to sift through the internet in search of explanations and implementations of introductory algorithms, I was taken aback. Every site I landed on explained the algorithms like I was reading some sort of research paper and was not beginner-friendly at all! All kinds of jargon and equations were thrown around assuming I was just supposed to know them — which I had absolutely no clue about.
With this article, and the others in the series, I’ll try to explain the algorithm and the intuition behind it with a down-to-earth, layman’s approach…
Machine Learning is arguably the flashiest and most discussed topic currently in the field of Computer Science, but how are you supposed to keep track of the endless cascade of different algorithms that seemingly pop up from nowhere?
From personal experience, it can feel extremely overwhelming when algorithm names are thrown around and you are expected to just know what they are and how they work. If you’re in the same boat, you’re in the right place!
In this article, we will take a high-level tour of the most popular machine learning algorithms in order to understand the true scope…
When developers begin to look into python environments and how to clean their workflow, they are bombarded with all kinds of different options. Such a large menu naturally leads developers to unnecessarily sift through articles and documentation to find the “best” one to use. In this article, we will go over the differences and benefits amongst each of the major virtual environment options in order to consolidate all those references into one single article. By the end, hopefully, you will have found the environment that best suits your needs!
Imagine yourself wanting a specific book and walking into a library in search of it. However, to your shock, there is absolutely no organization within the entire building — no way to distinguish fiction from nonfiction; no way to tell what author wrote what; simply no way to find your book. You go to the librarian and ask her where the specific book is, but all she tells you is “search for it.” The only option you have to find the book you so desperately want is to search every. single. book. in. the. building.
This library is your computer…
A wannabe writer playing at the crossroads between technology, programming and intelligence | ML Masters’ @ Georgia Tech