• BookClub: Feature Engineering for Machine Learning - Chapter 6 and 7

    Our next book club will be about Chapter 6: "Dimensionality Reduction" and Chapter 7: "NonlinearFeaturization via K-Means Model Stacking" of "Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari. See http://shop.oreilly.com/product/0636920049081.do for more details. A Book Club Meetup does not include a talk, but is an exchange about the respective chapters. We discuss things that we haven't understood yet, videos and tutorials that deal with a topic in more detail or code examples and libraries we have worked with. Beverage and food will be at your own expense.

  • BookClub: Feature Engineering for Machine Learning - Chapter 4 and 5

    Our next book club will be about Chapter 4: The Effects of Feature Scaline and Chapter 5: Categorical Variables of "Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari. See http://shop.oreilly.com/product/0636920049081.do for more details. A Book Club Meetup does not include a talk, but is an exchange about the respective chapters. We discuss things that we haven't understood yet, videos and tutorials that deal with a topic in more detail or code examples and libraries we have worked with. Beverage and food will be at your own expense.

  • Machine Learning Workshop for Beginners

    innoQ Deutschland GmbH

    In this hands-on-workshop we want to dive into the field of machine learning. We start with giving a short overview over concepts and definitions of ML. What is machine learning, what kind of algorithms are used? We talk about the basic terms, like supervised and unsupervised learning, training and testing, before we will examine a dataset together (probably some kind of health data) and work with different **classification** algorithms. You need your laptops and it would be perfect to install the required stuff beforehand (we will send you an email what to do) so that we can start directly with the hands-on part. The workshop will last around 3 hours.

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  • BookClub: Feature Engineering for Machine Learning - Chapter 3: Text Data

    Our next book club will be about Chapter 3 of "Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari. See http://shop.oreilly.com/product/0636920049081.do for more details. A Book Club Meetup does not include a talk, but is an exchange about the respective chapters. We discuss things that we haven't understood yet, videos and tutorials that deal with a topic in more detail or code examples and libraries we have worked with. Beverage and food will be at your own expense.

  • BookClub: Feature Engineering for Machine Learning - Chapter 1 & 2

    Our next book club will be about Chapter 1 & 2 of "Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari. See http://shop.oreilly.com/product/0636920049081.do for more details. A Book Club Meetup does not include a talk, but is an exchange about the respective chapters. We discuss things that we haven't understood yet, videos and tutorials that deal with a topic in more detail or code examples and libraries we have worked with. Beverage and food will be at your own expense.

  • Kafka on Kubernetes & Pipelines with Hadoop & Probabilistic Programming

    NorCom Information Technology AG

    19:00 Doors open & Get-Together 19:30-20:00 Emiliano Tomaselli, Olgierd Grodzki: Streaming Data Platform with Kafka and Kubernetes 20:00-20:25 Andreas Pawlik: Data Pipelining and Deep Learning Pipelines with Hadoop 20:25-20:35 Break 20:35-21:20 Alexey Kuntsevich: Data-Driven Decision Making with Probabilistic Programming Emiliano Tomaselli, Olgierd Grodzki: Streaming Data Platform with Kafka and Kubernetes ======================== During this session Emiliano Tomaselli and Olgierd Grodzki from Data Reply give a presentation on Kafka and Kubernetes. Kafka has become the defacto standard for building a streaming architecture. A lot of organizations want to run Kafka "as a service" - on premise or in the cloud - and use it to enable its developers to create Apps, Data Pipelines and more. In order to make the platform deployment Scalable, Fault Tolerant and Cloud Native we decided to take advantage of one of the most popular open-source systems for orchestrating containerized applications: Kubernetes. We will show you our use-cases developed at the customer side using those platforms, some challenges that we faced (eg. Security, Acls and more ) and how we tried to solve them by developing custom tools and applications. To conclude we will also present one of the solutions we adopted to bring automation into the Kubernetes Ecosystem with the CI/CD Pipelines. Andreas Pawlik: Data Pipelining and Deep Learning Pipelines with Hadoop ============================ Deep Learning profits from training and validation on large amounts of data. I will outline the challenges with running Deep Learning workflows on large data sets, discuss how Hadoop/Mesos can help address these challenges and explore the benefits and limitations of the approach. Alexey Kuntsevich: Data-Driven Decision Making with Probabilistic Programming ======================== No modern company can avoid shifting towards a data-driven decision making, in order to stay successful and competitive. Still, all the experience and knowledge gathered inside the company has to be incorporated into data-driven processes and automation. Probabilistic programming is able to combine automation, uncertainty which is a big part of any business, and existing knowledge from domain experts. The number of potential applications for probabilistic programming is limitless, from image reconstruction and spam filtering to economical modelling and anomaly detection. Moreover, probabilistic programming frameworks exist for nearly all popular development stacks. With several simple examples I’d like to show how to translate business reasoning into code and how to use a human-in-the-loop model to control and manage the uncertainty of the environment.

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  • Hadoop - Taming the Elephant & Dark Sides of AI & From DWH to data lake

    19:00 Doors open 19:30 - 20:15 Hadoop - Taming the Elephant (With a Whale) by Lisa Maria Moritz 20:15 - 21:00 Dark Sides of AI by Alexander Pospiech 21:15 - 21:45 From DWH to data lake: a story of 2 data engineers (Migrating from Amazon Redshift to Amazon Athena) by Dineshkarthik & Sergii

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  • BookClub: Practical Statistics - Chapter 7: Unsupervised Learning

    Café Über den Tellerrand

    During this meetup we will discuss "Chapter 7: Unsupervised Learning" of "Practical Statistics for Data Scientists"[1]. The book examples are in R but at least two people will use Python while working through the book. Feel free to use your own language of choice. Please bring your laptop, if you have code to show. [1] http://shop.oreilly.com/product/0636920048992.do https://www.ebooks.com/95787269/practical-statistics-for-data-scientists/bruce-peter-bruce-andrew

  • BookClub: Practical Statistics - 6: Statistical Machine Learning

    Café Über den Tellerrand

    During this meetup we will discuss "Chapter 6: Statistical Machine Learning" of "Practical Statistics for Data Scientists"[1]. The book examples are in R but at least two people will use Python while working through the book. Feel free to use your own language of choice. Please bring your laptop, if you have code to show. [1] http://shop.oreilly.com/product/0636920048992.do https://www.ebooks.com/95787269/practical-statistics-for-data-scientists/bruce-peter-bruce-andrew

  • BookClub: Practical Statistics for Data Scientists - 5: Classification

    During this meetup we will discuss "Chapter 5: Classification" of "Practical Statistics for Data Scientists"[1]. The book examples are in R but at least two people will use Python while working through the book. Feel free to use your own language of choice. Please bring your laptop, if you have code to show. [1] http://shop.oreilly.com/product/0636920048992.do https://www.ebooks.com/95787269/practical-statistics-for-data-scientists/bruce-peter-bruce-andrew

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