With the technology at our disposal today, we are trusting computers to do more and more everyday things for us. While some may be skeptical, there isn’t anything as such wrong with this as the more data we generate, the more precise the algorithms powering these computers will become. However,
Pinterest began as a small startup website in 2010 with just a few thousand users. Since then, the visual bookmarking application has grown into a tech powerhouse with 400+ million monthly users, serving over 200 billion pins scattered across 4 billion boards and counting. No small feat for a company
Duolingo came on the scene in 2011 operating as a teach and translate language learning app. It has since transformed into a $700+ million business, and become the first EdTech app to top $140 Million in annual revenue. Pretty good numbers, for a language learning app that still doesn’t charge
As machine learning developers, we always need to deal with ETL processing (Extract, Transform, Load) to get data ready for our model. Airflow can help us build ETL pipelines, and visualize the results for each of the tasks in a centralized way. In this blog post, we look at some
If you want to understand machine learning algorithms, it is very important to understand basic statistics and what is behind them. Understanding how the algorithm operates gives you the option of configuring the model according to what you need, as well as explaining with more confidence the results obtained from
In statistics and ML, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
I recently participated in a Machine Learning workshop at Rootstrap, where my coworkers and I learned about the basics of data science, did some research, and created interesting experiments. We had the opportunity to choose among the studied Machine Learning algorithms and work with them. So, I decided to do
Whenever you want to analyze the performance of machine learning algorithms, you would need to study the root cause of the error. Concepts like bias and variance would help you understand this cause and give you insights on how to improve your model. What is Bias error? Bias error corresponds
Machine learning is one of the hottest topics nowadays. People talk about machine learning as if it is magic. Organizations are racing to integrate machine learning into their functions. Everyone talks about it, but not too many people know what it really is. It is just math and statistics plus
We are currently experiencing the negative impacts of the novel Coronavirus. This virus has quickly changed our lives and has left many of us feeling confused and fearful of what’s to come. As engineers and data scientists – we want to help make sense of the overwhelming amount of data
