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Utilizing your Weekends with the FREE courses in Machine Learning from Top Universities
- Supervised learning techniques for regression and classification
- Unsupervised learning techniques for data modeling and analysis
- Probabilistic versus nonprobabilistic viewpoints
- Optimization and inference algorithms for model learning
- Overview
- After completing this course, you will be familiar with the following concepts and techniques:
- Data exploration, preparation, and cleaning
- Supervised machine learning techniques
- Unsupervised machine learning techniques
- Model performance improvement
- By the end of this course, students will be able to:
- Distinguish between quantum computing paradigms relevant for machine learning
- Assess expectations for quantum devices on various time scales
- Identify opportunities in machine learning for using quantum resources
- Implement learning algorithms on quantum computers in Python
- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets