Module Overview
This course teaches the end-to-end process of investigating data with a machine learning lens. It will teach how to extract and identify useful features that best represent the data, a few of the most important machine learning algorithms, and how to evaluate the performance of the same.
Learning Outcomes
Upon successfully completing the course, you will be able to:
Implement and run a Naive Bayes classifier
Understand the intuition behind support vector machines (SVM), and implement them
Explain how decision tree classifiers work, including the concepts of entropy and information gain, and implement them
Model continuous data using linear regression
Analyze a dataset to detect outliers and remove them
Use text data in any machine learning algorithm
Understand the difference between supervised learning and unsupervised learning
Explain k-means clustering and implement it
Understand which algorithms require feature rescaling before use
Implement feature selection
Explain dimensionality reduction using principal component analysis (PCA) and implement it
Use different evaluation metrics to evaluate the performance of ML algorithms