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In this course, you will :
- Learn the distinction between regression and classification, train a Linear Regression model to predict values, and use Logistic Regression to predict states.
- Discover the definition of a perceptron as a neural network building block and the perceptron algorithm for classification.
- Learn the Bayes rule and use it to predict spam messages using the Naive Bayes algorithm. Train models with Bayesian Learning and complete a natural language processing exercise with Bayesian Learning.
- Learn how to train a Support Vector Machine to separate data in a linear fashion. To train SVMs on data that is not linearly separable, use Kernel Methods.
Syllabus :
- Regression
- Perceptron Algorithms
- Decision Trees
- Naive Bayes
- Support Vector Machines
- Ensemble of Learners
- Evaluation Metrics
- Training and Tuning Models
