Course Outline

Introduction

Describing the Structure of Unlabled Data

  • Unsupervised Machine Learning

Recognizing, Clustering and Generating Images, Video Sequences and Motion-capture Data

  • Deep Belief Networks (DBNs)

Reconstructing the Original Input Data from a Corrupted (Noisy) Version

  • Feature Selection and Extraction
  • Stacked Denoising Auto-encoders

Analyzing Visual Images

  • Convolutional Neural Networks

Gaining a Better Understanding of the Structure of Data

  • Semi-Supervised Learning

Understanding Text Data

  • Text Feature Extraction

Building Highly Accurate Predictive Models

  • Improving Machine Learning Results
  • Ensemble Methods

Summary and Conclusion

Requirements

  • Python programming experience
  • An understanding of basic principles of machine learning

Audience

  • Developers
  • Analysts
  • Data scientists
  21 Hours
 

Number of participants


Starts

Ends


Dates are subject to availability and take place between 09:30 and 16:30.
Open Training Courses require 5+ participants.

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