IT Professional Curricula     Enterprise Database Systems Solution Area     Data Science     Data Science Essentials

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Machine learning is a particular area of data science that uses techniques to create models from data without being explicitly programmed. In this course, you'll explore the conceptual elements of various machine learning techniques.

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Objectives

Machine Learning Introduction start the course identify problems in which supervised learning techniques apply identify problems in which unsupervised learning techniques apply apply linear regression to machine learning problems identify predictors in machine learning Regression and Classification apply logistic regression to machine learning problems describe the use of dummy variables use naive bayes classification techniques work with decision trees Clustering describe K-means clustering define cluster validation define principal component analysis Errors and Validation describe machine learning errors describe underfitting describe overfitting apply k-folds cross validation describe fall-forward and back-propagation in neural networks describe SVMs and their use Practice: Choosing a Method choose the appropriate machine learning method for the given example problems