Essential guide to various clustering evaluation metrics — Clustering is an unsupervised machine-learning approach to divide the population into groups or clusters such that the samples assigned to the same cluster share similar properties, and samples assigned to different clusters are different from each other. There are various popular clustering techniques including k-Means clustering which is a centroid-based…

Essential guide to transfer learning — The rapid developments in Computer Vision — image classification use cases have been further accelerated by the advent of transfer learning. It takes a lot of computational resources and time to train a computer vision neural network model on a large dataset of images. Luckily, this time and resources can…

Essential guide to changepoint detection algorithms for time series analysis — Time-series analytics is one of the topics a data scientist must have exposure to. Time-series analysis comprises the process and mathematical set of tools used for looking into time-series data to learn what happened, when and why it happened, and what is most likely to occur in the future. Change…

Essential guide to scale-up your Pandas workflow — Pandas is one of the most popular Python libraries for data exploration and visualization. Pandas offer tons of APIs to perform data-wrangling tasks, but when working with large datasets, it fails or has slow computations. In this article, we will discuss 14 techniques to speed up the Pandas workflow using…

Essential guide to the diagnosis prediction in healthcare via attention-based Bi-LSTM network — Predicting future health information or disease using the Electronic Health Record (EHR) is a key use case for research in the healthcare domain. EHR data consists of diagnosis codes, pharmacy codes, and procedure codes. …

Essential guide to time series feature engineering and forecasting — Time series forecasting involves model building on historical time-stamped data values and external factors to make scientific predictions that drive future strategic decision-making. Training a robust time-series forecasting model for accurate and reliable predictions is one of the most challenging tasks, given its direct impact on related decisions. …

Essential guide to an open-source Python framework for automated feature engineering — Feature engineering is an important and time-consuming component of the data science model development pipeline. The feature engineering pipeline decides the robustness and performance of the model. There are various automated feature engineering packages that process and create features for a single dataset. But these packages fail for the use-cases…

An open-source package for time series preprocessing and forecasting with unified and user-friendly APIs — Time series forecasting involves model building on historical time-stamped data to make scientific predictions and drive future strategic decision-making. Time series forecasting has many uses in various domains including: Predict consumer demand for every product Forecasting pandemic spread, diagnosis, medication, and planning in healthcare

Extract hundreds of relevant features for your time series use-case — Time Series data capture the variable's value repeatedly over time resulting in a series of data points indexed in time order. In time series data has natural temporal ordering i.e. the value of a variable at a particular time is dependent on past values. Traditional machine learning algorithms are not…