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Course Outline
Introduction
Characterizing the Structure of Unlabeled Data
- Unsupervised Machine Learning
Recognition, Clustering, and Generation of Images, Video Sequences, and Motion-Capture Data
- Deep Belief Networks (DBNs)
Recovering Original Input Data from Corrupted (Noisy) Versions
- Feature Selection and Extraction
- Stacked Denoising Auto-encoders
Analyzing Visual Images
- Convolutional Neural Networks
Gaining a Deeper Understanding of Data Structure
- Semi-Supervised Learning
Interpreting Text Data
- Text Feature Extraction
Constructing Highly Accurate Predictive Models
- Enhancing Machine Learning Outcomes
- Ensemble Methods
Summary and Conclusion
Requirements
- Familiarity with Python programming
- Foundational knowledge of machine learning principles
Target Audience
- Developers
- Analysts
- Data scientists
21 Hours
Testimonials (1)
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
