Published on July 19, 2021 in Towards Data Science Read data directly from the clipboard without saving it first When I write about a library or a new concept, I typically like to showcase its working via examples. The source of datasets that I use in my articles varies widely. Sometimes I create simple toy datasets, while on other … Continue reading From clipboard to DataFrame with Pandas
Some tips and tricks to make the most of your Github experience Originally published here This article compiles some useful tips and hacks that I have discovered over time while using Github. These have been gathered from various sources over time. I have filtered out the ones that were too familiar to avoid repetition. … Continue reading A Tour of 10 Useful Github Features
Speed up Inference of your scikit-learn models This article was originally published here. Deep learning frameworks consist of tensors as their basic computational unit. As a result, they can utilize the hardware accelerators (e.g., GPUs), thereby speeding up the model training and inference. However, the traditional machine learning libraries like scikit-learn are developed to run on CPUs … Continue reading Tensors are all you need
Originally published here Github contribution graph shows your repository contributions over the past year. A filled-up contribution graph is not only pleasing to the eye but points towards your hard work, too(unless if you have hacked it). The graph, though pretty, also displays considerable information regarding your performance. However, if you look closely, it is … Continue reading Create GitHub’s style contributions plot for your Time Series data
Originally published here Massive Open Online Courses (MOOCs) are an indispensable part of the life of a self-taught data scientist. If you are in a room full of wanna-be data scientists, the chances are that fifty percent of them have taken the famous Machine Learning course by Andrew Ng. However, here is the twist. Even … Continue reading Reviewing the recently released HuggingFace 🤗 Course
Important caveats to be kept in mind when encoding data with pandas.get_dummies() Handling categorical variables forms an essential component of a machine learning pipeline. While machine learning algorithms can naturally handle the numerical variables, the same is not valid for their categorical counterparts. Although there are algorithms like LightGBM and Catboost that can inherently handle the categorical variables, it is … Continue reading Beware of the Dummy variable trap in pandas
