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Duration 21 hours
Course Outline
Introduction
Characterizing 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 Deeper Insight into Data Structure
- Semi-Supervised Learning
Interpreting Text Data
- Text Feature Extraction
Developing Highly Accurate Predictive Models
- Enhancing Machine Learning Outcomes
- Ensemble Methods
Summary and Conclusion
Requirements
- Proficiency in Python programming
- Foundational knowledge of machine learning principles
Target Audience
- Developers
- Analysts
- Data scientists
Testimonials (1)
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
