Dive deeper into machine learning with our interactive machine learning intermediate course. You’ll learn additional algorithms such as logistic regression and k-means clustering. You’ll also learn about things like how to detect overfitting and the bias-variance tradeoff.
Then you’ll dig into understanding model performance using sensitivity and specificity as it relates to classification models. You’ll get an introduction to clustering, an unsupervised learning technique designed to find patterns in data and group data into clusters that are closely related. And you’ll discover the difference between supervised and unsupervised learning, as well as when it makes sense to use each type of machine learning.
- Logistic regression.
- Introduction to evaluating binary classifiers.
- Multiclass classification.
- Clustering basics.
- K-means clustering.
- Guided Project: Predicting the stock market.
What will you learn
- How to learn intermediate linear regression and logistic regression concepts.
- How to learn how to prevent overfitting, a common problem in machine learning.