How to Run 30 Machine Learning Models with a Few Lines of Code by Ismael Araujo via @TDataScience

Learn how to run multiple machine learning models using lazy predict.

This is really neat and so you need to bookmark or add it to something like Evernote so you can use this in your Python code.

WEBINAR: Harness the Power of Big Data Analytics - 14 June 2018

Harness the Power of Big Data Analytics Join us for the latest DSC Webinar on June 14th, 2018

From BI to AI, the need for Big Data and analytics is pervasive and transformational. However, Big Data technologies such as Hadoop or Spark are still quite complicated and not leveraged to their full capacity by business practitioners. New technologies are available to leverage the power of big data platforms for self-service data preparation and automated machine learning to help organizations get the most out of their analytics initiatives and unlock the full potential of their Big Data investments. In this latest Data Science Central webinar you will learn the essentials you need for a modern data and analytics strategy, ways to expand your strategy development repertoire, and emerging approaches, as well as: Why big data solutions like Hadoop and Spark are ideal for machine learning and advanced analytics initiatives What Automated Machine Learning for Big Data is and how it can change your approach to ML How Self-Service Data Preparation reduces the work required to deliver clean data at scale for predictive modeling How to leverage Big Data platforms to rapidly deliver more accurate predictions for ML initiatives Speakers: Raju Penmatcha, PhD, Customer Facing Data Scientis -- DataRobot Connor Carreras, Manager for Customer Success, Americas -- Trifacta Hosted by: Bill Vorhies, Editorial Director -- Data Science Central Title:  Harness the Power of Big Data Analytics Date:  Thursday, June 14th, 2018 Time:  9:00 AM - 10:00 AM PDT

Register here

Using Evolutionary AutoML to Discover Neural Network Architectures by/via @googleresearch

Updates from Google Research: they’ve successfully used an evolutionary algorithm to beat out reinforcement learning-based approaches to autoML. The most fascinating result is that the evolutionary approach is not simply higher performance—it requires far less computation to arrive there

This is really interesting.

WEBINAR: Model Risk Management with Automated Machine Learning - 29 March 2018

Webinar Details Register today Thursday, March 29, 2018      Presenter:  1:00 pm Eastern / 10:00 am Pacific      Seph Mard 45 minutes with Q&A      Head of Model Validation, DataRobot

Model Risk Management has recently become a very hot topic in regulatory and compliance-rich industries. But traditional model development methods are time-consuming, tedious, and subject to user error and bias.  Automated Machine Learning offers a much more robust framework for managing and minimizing model risk than traditional manual modeling.  It not only automates the building of highly-accurate predictive models, but also automates the documentation required for Model Risk Management.  On this webinar, you'll discover:  - The tools that Automated Machine Learning (AML) provides to optimize and accelerate your model risk management framework. - How AML increases the efficiency of your model development and validation processes, while also further aligning modeling and validation processes to regulatory expectations for model risk management. - How AML reduces model risk, while developing cutting-edge machine learning models.

Presenter: Seph Mard Head of Model Validation  DataRobot