* Translate 1.1 (Intro to ML) to Korean
* Translate 1.2 (History of ML) to Korean
* Translate 1.3 (Fairness) to Korean
* Translate 1.4 (Techniques of ML) to Korean
* Translate 2.1 (Tools) to Korean
* Translate 2.2 (Data) to Korean
* Translate 2.3 (Linear) to Korean
* Translate 2.4 (Logistic) to Korean
* Translate 3.1 (Web App) to Korean
* Translate 4.1 (Intro to Classification) to Korean
* Translate 4.2 (Classifiers 1) to Korean
* Translate 4.3 (Classifiers 2) to Korean
* Translate assignment 4.4 (Applied) to Korean
* Translate assignment 5.1 (Visualize) to Korean
* Translate assignment 5.2 (K-Means) to Korean
* Translate assignment 6.1 (Intro to NLP) to Korean
* Translate assignment 6.2 (Tasks) to Korean
* Fix minor typo in assignment 6.2 (Tasks) Korean translation
* Translate assignment 6.3 (Translation Sentiment) to Korean
* Translate assignment 6.4 (Hotel Reviews 1) to Korean
* Translate assignment 6.5 (Hotel Reviews 2) to Korean
* Translate assignment 7.1 (Intro to Time Series) to Korean
* Remove English title from assignment 7.1 (Intro to Time Series) in Korean
* Translate assignment 7.2 (ARIMA) to Korean
* Translate 8.1 (Q-Learning) to Korean
* Translate assignment 8.2 (Gym) to Korean
* Translate assignment 9.1 (Applications) to Korean
In this section of the curriculum, you will be introduced to an applied ML topic: how to save your Scikit-learn model as a file that can be used to make predictions within a web application. Once the model is saved, you'll learn how to use it in a web app built in Flask. You'll first create a model using some data that's all about UFO sightings! Then, you'll build a web app that will allow you to input a number of seconds with a latitude and a longitude value to predict which country reported seeing a UFO.
Ian Park
d516b3c97a
