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Course Outline
Introduction
Describing the Structure of Unlabeled Data
- Unsupervised Machine Learning
Recognizing, Clustering, and Generating Images, Video Sequences, and Motion-Capture Data
- Deep Belief Networks (DBNs)
Reconstructing Original Input Data from Corrupted (Noisy) Versions
- Feature Selection and Extraction
- Stacked Denoising Auto-encoders
Analyzing Visual Images
- Convolutional Neural Networks
Gaining a Better Understanding of Data Structure
- Semi-Supervised Learning
Understanding Text Data
- Text Feature Extraction
Building Highly Accurate Predictive Models
- Improving Machine Learning Results
- Ensemble Methods
Summary and Conclusion
Requirements
- Experience with Python programming.
- A solid understanding of basic machine learning principles.
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.
