• Throwing the dice with a Quantum Computer

    Online event

    "Quantum computing is the future!", so sayeth the soothsayers of technology's future. But, what good is that to us today if these magnificent devices are still decades away? Well, that's where we have some good news for you - Quantum computers are real, and you can use them TODAY. In this workshop, we'll go through the very basics of Quantum computing, we'll create our first quantum circuit the IBM Q Experience (no coding knowledge required), and then we'll make it do something useful with Qiskit - the Python framework for creating Quantum algorithms. Long story short, if you're interested in random numbers, by the end of this workshop you'll have used the quantum nature of the IBM Q to generate the most random number you've ever known. Please join this event on Crowdcast: https://www.crowdcast.io/e/throwing-the-dice-with-a-quantum-computer

  • Data Science Lunch and Learn: Mapping COVID projections

    This is part 4 of a 4 part series on dealing with COVID case data. Don't worry, you should be able to follow this session by itself and you can always find the replays here. Once we understand what is actually going on in a region, we can try to extrapolate by applying our findings to future dates. Prediction is tricky, and we will see if and how we can do that. Perhaps even more important, how do you present and communicate your projections? Mapping is always a striking example, and using Folium we will see what is needed to create an interactive, animated map of what we found and how we think it will develop: a coronaradar. We will guide you through the data preparation needed to create such a map and pay attention to the setup of your analysis for deployment in a pipeline. We will run a Jupyter notebook in Watson Studio. If you want to try this out for yourself, please go here http://ibm.biz/data-science-monday to sign up for a free account Speaker: Damiaan Zwietering Follow these instructions to get up and running: https://github.com/IBMDeveloperUK/data-science-lunch-and-learn/blob/master/watson-studio-instructions.md JOINING INSTRUCTIONS This event will be live streamed and available on demand via our channel on Crowdcast: https://www.crowdcast.io/e/data-science-lunch-and-5/register Instructions on how to setup your device for Crowdcast can be found here: https://www.crowdcast.io/setup

  • Automate your machine learning workflow tasks using Elyra and Kubeflow Pipelines

    Whether you are just getting started in Data Science or are seasoned data scientist, the JupyterLab IDE is likely a tool you are using frequently to get work done. In this session we will introduce Elyra - a set of of AI-centric extensions to JupyterLab - that provide support for ML workflow pipelines, Git versioning, code snippets and much more. We'll also demonstrate how to create Machine Learning pipeline from Jupyter notebooks or Python scripts using Elyra's Visual Pipeline Editor, and how to run pipelines locally in JupyterLab or remotely on Kubeflow Pipelines. Speaker: Patrick Titzler JOINING INSTRUCTIONS This event will be live streamed and available on demand via our channel on Crowdcast: https://www.crowdcast.io/e/data-science-lunch-and-6/register Instructions on how to setup your device for Crowdcast can be found here: https://www.crowdcast.io/setup

  • Data Science Lunch and Learn: AMA with Damiaan

    Online event

    This is part 5 (!) of the 4 part series on dealing with COVID case data. We asked Damiaan one more time to answer any questions you might have about his work. If you have missed the series, don't worry, you can always watch the replays and find all resources here: https://ibm.biz/data-science-monday In the finale of this series Damiaan will present the latest results and there will be room for discussion on related topics such as the methods used or lessons learned. Follow these instructions to get up and running with Watson Studio : https://github.com/IBMDeveloperUK/data-science-lunch-and-learn/blob/master/watson-studio-instructions.md Speaker: Damiaan Zwietering Host: Margriet Groenendijk JOINING INSTRUCTIONS This event will be live streamed and available on demand via our channel on Crowdcast: https://www.crowdcast.io/e/data-science-lunch-and-7/register Instructions on how to setup your device for Crowdcast can be found here: https://www.crowdcast.io/setup

  • Data Science Lunch and Learn:Clustering algorithms using Python and scikit-learn

    Interested in Machine Learning? Join us at this workshop where we compare different clustering algorithms with a detailed walk-through of each of them with a hands-on experience. Clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. It is a method of unsupervised learning and a common technique for statistical data analysis used in many fields. Categories of clustering algorithms we will be discussing: - Centroid-based clustering - Density-based clustering - Hierarchical clustering In this workshop, scikit-learn provides data sets that help to illustrate the clustering algorithm differences We’ll use these where needed, but we also use our customer data set to help you visualise clustering with realistic data instead of obvious shapes. Speaker: Fawaz Siddiqi We will run a Jupyter notebook in Watson Studio. If you want to try this out for yourself, please go here http://ibm.biz/data-science-monday to sign up for a free account Follow these instructions to get up and running: https://github.com/IBMDeveloperUK/data-science-lunch-and-learn/blob/master/watson-studio-instructions.md JOINING INSTRUCTIONS This event will be live streamed and available on demand via our channel on Crowdcast: https://www.crowdcast.io/e/data-science-lunch-and-8/register Instructions on how to setup your device for Crowdcast can be found here: https://www.crowdcast.io/setup

  • Predict Loan Eligibility using Machine Learning Models

    Online event

    Loans are the core business of banks. The main profit comes directly from the loan’s interest. The loan companies grant a loan after an intensive process of verification and validation. However, they still don’t have assurance if the applicant is able to repay the loan with no difficulties. In this session, we’ll build a predictive model to predict if an applicant is able to repay the lending company or not. We will prepare the data using Jupyter Notebook and use various models to predict the target variable. Presenter: Mridul Bhandari We will run a Jupyter notebook in Watson Studio. If you want to try this out for yourself, please go here https://ibm.biz/Bdqkzd to sign up for a free account Then follow these instructions to get up and running: https://github.com/IBMDeveloperUK/data-science-lunch-and-learn/blob/master/watson-studio-instructions.md JOINING INSTRUCTIONS This event will be live streamed and available on demand via our channel on Crowdcast: https://www.crowdcast.io/e/data-science-lunch-and-9/register Instructions on how to setup your device for Crowdcast can be found here: https://www.crowdcast.io/setup