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1. Giving Computers the Ability to Learn from Data
Giving Computers the Ability to Learn from Data
Building intelligent machines to transform data into knowledge
The three different types of machine learning
Introduction to the basic terminology and notations
2. Training Simple Machine Learning Algorithms for Classification
Training Simple Machine Learning Algorithms for Classification
Artificial neurons – a brief glimpse into the early history of machine learning
Implementing a perceptron learning algorithm in Python
Adaptive linear neurons and the convergence of learning
5. Compressing Data via Dimensionality Reduction
Compressing Data via Dimensionality Reduction
7. Combining Different Models for Ensemble Learning
Combining Different Models for Ensemble Learning
Learning with ensembles
Combining classifiers via majority vote
Bagging – building an ensemble of classifiers from bootstrap samples
Leveraging weak learners via adaptive boosting