Introduction to Machine Learning

Course Details:







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:

  1. Implement and run a Naive Bayes classifier

  2. Understand the intuition behind support vector machines (SVM), and implement them

  3. Explain how decision tree classifiers work, including the concepts of entropy and information gain, and implement them

  4. Model continuous data using linear regression

  5. Analyze a dataset to detect outliers and remove them

  6. Use text data in any machine learning algorithm

  7. Understand the difference between supervised learning and unsupervised learning

  8. Explain k-means clustering and implement it

  9. Understand which algorithms require feature rescaling before use

  10. Implement feature selection

  11. Explain dimensionality reduction using principal component analysis (PCA) and implement it

  12. Use different evaluation metrics to evaluate the performance of ML algorithms