ML-For-Beginners/4-Classification/1-Introduction

Introduction to Classification

In these four lessons, you will discover the 'meat and potatoes' of classic machine learning - Classification. No pun intended - we will walk through using various classification algorithms with a dataset all about the brilliant cuisines of Asia. Hope you're hungry!

Classification is a form of supervised learning that bears a lot in common with Regression techniques. If machine learning is all about assigning names to things via datasets, then classification generally falls into two groups: binary classification and multiclass classfication.

Remember, Linear Regression helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict what price a pumpkin would be in September vs. December, for example. Logistic Regression helped you discover binary categories: at this price point, is this pumpkin orange or not-orange?

Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this recipe data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.

🎥 Click the image above for a video: MIT's John Guttag introduces Classification

Pre-lecture quiz

Introduction

Before working to clean the data and prepare it for analysis, it's useful to understand several of the algorithms that you will use.

Support-vector machines Naive Bayes Decision trees K-nearest neighbor algorithm

Prerequisite

What steps should have been covered before this lesson?

Preparation

Preparatory steps to start this lesson

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[Step through content in blocks]

[Topic 1]

Task:

Work together to progressively enhance your codebase to build the project with shared code:

code blocks

✅ Knowledge Check - use this moment to stretch students' knowledge with open questions

[Topic 2]

[Topic 3]

🚀Challenge

Add a challenge for students to work on collaboratively in class to enhance the project

Optional: add a screenshot of the completed lesson's UI if appropriate

Post-lecture quiz

Review & Self Study

Assignment

Assignment Name