Data Science: Supervised Machine Learning in Python

Editorial course preview

What the public course preview actually shows

These 4 complementary views highlight concrete, legible examples from the course presentation.

A presentation slide titled 'The Models' displaying four diagrams: a Naive Bayes graphical model, a decision tree troubleshooting flowchart, a K-Nearest Neighbors scatter plot with — Preview 1 of 4
This slide illustrates four fundamental machine learning models: a Naive Bayes graphical structure, a decision tree for troubleshooting, a K-Nearest Neighbors classification diagram, and a perceptron neural network.

A presentation slide titled 'Focusing on the Fundamentals' featuring text about building a web-service API for image recognition and a diagram showing mobile devices connecting toa — Preview 2 of 4
This slide outlines a project to build a web-service API for image recognition, illustrated by a system architecture diagram connecting client devices to a server.

A presentation slide titled 'Focusing on the Fundamentals' with text about theory and implementation, featuring a cartoon teacher at a chalkboard with math equations and a student. — Preview 3 of 4
This slide outlines a course approach focusing on fundamentals by deriving algorithms from scratch using basic math and building real computer programs.

A presentation slide titled 'What is this course about?' featuring text on foundational supervised machine learning algorithms and two scatter plots illustrating Classification and — Preview 4 of 4
This slide introduces the course scope by defining foundational supervised machine learning algorithms and visually distinguishing between classification and regression tasks.