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.

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.

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.

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