
Kathrin Melcher
Data Scientist
KNIME
location_on Germany
Member since 2 years
Kathrin Melcher
Specialises In
Kathrin Melcher is a Data Scientist at KNIME. She holds a Master's Degree in Mathematics from the University of Konstanz, Germany. She joined the evangelism team at KNIME in 2017 and has a strong interest in data science and machine learning algorithms. Kathrin enjoys teaching and sharing her data science knowledge with the community - for example in the book "From Excel to KNIME" - as well as on various blog posts and at training courses, workshops, and conference presentations.
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Deep Dive into Data Science with KNIME Analytics Platform
Kathrin MelcherData ScientistKNIMEPaolo TamagniniData ScientistKNIMEschedule 1 year ago
Sold Out!480 Mins
Workshop
Beginner
In this course we will cover the major steps in a data science project. From data access, data pre-processing, and data visualization, to machine learning, model optimization, and deployment using KNIME Analytics Platform.
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The Magic of Many-To-Many LSTMs: Codeless Product Name Generation and Neural Machine Translation
Kathrin MelcherData ScientistKNIMEPaolo TamagniniData ScientistKNIMEschedule 1 year ago
Sold Out!45 Mins
Case Study
Intermediate
What do product name generation and neural machine translation have in common?
Both involve sequence analysis which can be implemented via recurrent neural networks (RNN) with LSTM layers.
LSTM Neural Networks are the state of the art technique for sequence analysis. In this presentation, we find out what LSTM layers are, learn about the difference between many-to-one, many-to-many, and one-to many-structures, and train many-to-many LSTM networks for both use cases.
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Guided Analytics - Building Applications for Automated Machine Learning
Paolo TamagniniData ScientistKNIMEKathrin MelcherData ScientistKNIMEschedule 1 year ago
Sold Out!90 Mins
Tutorial
Beginner
In recent years, a wealth of tools has appeared that automate the machine learning cycle inside a black box. We take a different stance. Automation should not result in black boxes, hiding the interesting pieces from everyone. Modern data science should allow automation and interaction to be combined flexibly into a more transparent solution.
In some specific cases, if the analysis scenario is well defined, then full automation might make sense. However, more often than not, these scenarios are not that well defined and not that easy to control. In these cases, a certain amount of interaction with the user is highly desirable.
By mixing and matching interaction with automation, we can use Guided Analytics to develop predictive models on the fly. More interestingly, by leveraging automated machine learning and interactive dashboard components, custom Guided Analytics Applications, tailored to your business needs, can be created in a few minutes.
We'll build an application for automated machine learning using KNIME Software. It will have an input user interface to control the settings for data preparation, model training (e.g. using deep learning, random forest, etc.), hyperparameter optimization, and feature engineering. We'll also create an interactive dashboard to visualize the results with model interpretability techniques. At the conclusion of the workshop, the application will be deployed and run from a web browser.
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Deep Dive into Data Science with KNIME Analytics Platform
Kathrin MelcherData ScientistKNIMEVincenzo TursiData ScientistKNIMEschedule 2 years ago
Sold Out!480 Mins
Workshop
Beginner
In this course we cover the major steps in a data science project, from data access, data pre-processing, and data visualization to machine learning, model optimization, and deployment using the KNIME Analytics Platform.
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Sentiment Analysis with Deep Learning, Machine Learning or Lexicon Based
Kathrin MelcherData ScientistKNIMEVincenzo TursiData ScientistKNIMEschedule 2 years ago
Sold Out!90 Mins
Workshop
Beginner
Do you want to know what your customers, users, contacts, or relatives really think? Find out by building your own sentiment analysis application.
In this workshop you will build a sentiment analysis application, step by step, using KNIME Analytics Platform. After an introduction to the most common techniques used for sentiment analysis and text mining, we will work in three groups, each one focusing on a different technique.
- Deep Learning: This group will work with the visual Keras deep learning integration available in KNIME (completely code free)
- Machine Learning: This group will use other machine learning techniques, based on native KNIME nodes
- Lexicon Based: This group will focus on a lexicon based approach for sentiment analysis
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