From 91fe6db5f6b8b36ea19a4637c5198b8d00a5a00e Mon Sep 17 00:00:00 2001 From: Marion Walton Date: Wed, 31 Jan 2024 18:48:42 +0200 Subject: [PATCH] Link to scikit home page --- _episodes/04-what-is-ml-good-at.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_episodes/04-what-is-ml-good-at.md b/_episodes/04-what-is-ml-good-at.md index 6950db4..71c37ec 100644 --- a/_episodes/04-what-is-ml-good-at.md +++ b/_episodes/04-what-is-ml-good-at.md @@ -23,7 +23,7 @@ slideOptions: ## Classical Machine Learning and Deep Learning -In the previous episoode, most of our machine learning examples dealt with tabular data. There are numerous different algorithms that work with tabular data, and they all have strengths and weaknesses for different tasks and types and sizes of data. (See the scikit learn flowchart for a good overview of which algorithms you should choose for a given task). +In the previous episoode, most of our machine learning examples dealt with tabular data. There are numerous different algorithms that work with tabular data, and they all have strengths and weaknesses for different tasks and types and sizes of data. (See the ![Scikit learn home page](https://scikit-learn.org/stable/index.html) for a good overview of which algorithms you can use for a given task). ![Wikipedia CNN Diagram](../fig/ep-03-cnn-diagram.png)