There are tons of machine learning models that you can use to visualize your data. In this video, learn how to determine which method is best used for which desired outcome.
- [Instructor] With so many machine learning … algorithms available from scikit-learn, … which algorithm should you choose? … Selecting a good enough model from among a large number … of possible machine learning models is one … of the hardest parts of machine learning. … Some algorithms are better suited … to different types of data and problems. … Luckily, a quick answer to model selection … with scikit-learn is, use the algorithm cheat sheet. … It's meant to give you a rough guide … in how to choose an algorithm. … From the start point, … you first ask, do you have more than 50 samples? … From there, you keep on answering questions … until you get an idea of what you should try. … If you don't use the cheat sheet, … here are a few things to consider when choosing a model. … The first thing is a problem you're trying to solve. … For example, if you have a supervised learning problem, … figuring out if you're trying to predict a continuous … or categorical value can be an important first step. …
This course was created by Madecraft. We are pleased to host this content in our library.
- Why use scikit-learn?
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Principal component analysis (PCA)