Skip to content
    Latest

    Getting Started with OpenCV

    In this article we talk about the foundations of Computer vision, the history and capabilities of the OpenCV framework, and how to make your first steps in accessing and...

    Reinforcement Learning: The K-armed bandit problem

    In a previous blog post we talked about the foundations of reinforcement learning. We covered classical and operant conditioning, rewards, states,...

    KNN with Examples in Python

    In this article, we will introduce and implement k-nearest neighbours (KNN) as one of the supervised machine learning algorithms. KNN is utilised to...

    Getting Started with Ray

    In this blog post we give a quick introduction to Ray. We talk about the architecture and execution model, and present some of Ray's core paradigms...

    Getting Data with Beautiful Soup

    Data is all around us, from the spreadsheets we analyse on a daily basis, to the weather forecast we rely on every morning or the webpages we read....

    Data Exploration with Pandas Profiler and D-Tale

    We all have heard how data is the new oil. I always say that if that is the case, we need to go through some refinement process before that raw oil...

    Explaining black-box models using attribute importance, PDPs, and LIME

    In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights...

    Building a Named Entity Recognition model using a BiLSTM-CRF network

    In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated...

    Accelerating model velocity through Snowflake Java UDF integration

    Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data...

    Fitting Support Vector Machines via Quadratic Programming

    In this blog post we take a deep dive into the internals of Support Vector Machines. We derive a Linear SVM classifier, explain its advantages, and...

    ML internals: Synthetic Minority Oversampling (SMOTE) Technique

    In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. We present the...

    Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

    In this article, we'll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot...

    On-Demand Spark clusters with GPU acceleration

    Apache Spark has become the de facto standard for processing large amounts of stationary and streaming data in a distributed fashion. The addition...

    How to Supercharge Data Exploration with Pandas Profiling

    Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles, whether consisting of summary...

    Subscribe to the Data Science Blog

    Receive data science tips and tutorials from leading Data Scientists right to your inbox.