From a231e21e913b7390493530e17dc8f498ef26e847 Mon Sep 17 00:00:00 2001 From: Vishvanathan K Date: Wed, 7 Jul 2021 17:51:40 +0530 Subject: [PATCH 1/3] Changed .pk1 to .pkl --- 3-Web-App/1-Web-App/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md index f848d8078..6150aece7 100644 --- a/3-Web-App/1-Web-App/README.md +++ b/3-Web-App/1-Web-App/README.md @@ -167,7 +167,7 @@ Now you can build a Flask app to call your model and return similar results, but css/ templates/ notebook.ipynb - ufo-model.pk1 + ufo-model.pkl ``` ✅ Refer to the solution folder for a view of the finished app From 7f0df7684f3aa3674c9a0fa6d3d7e28de2100969 Mon Sep 17 00:00:00 2001 From: Vishvanathan K Date: Wed, 7 Jul 2021 18:11:55 +0530 Subject: [PATCH 2/3] Change pip to !pip --- 4-Classification/4-Applied/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md index 0e20f6c37..773271a1e 100644 --- a/4-Classification/4-Applied/README.md +++ b/4-Classification/4-Applied/README.md @@ -31,7 +31,7 @@ First, train a classification model using the cleaned cuisines dataset we used. 1. Start by importing useful libraries: ```python - pip install skl2onnx + !pip install skl2onnx import pandas as pd ``` From 7014d4c06fa6de1f04ec226dd5ad8b2282e8c270 Mon Sep 17 00:00:00 2001 From: Vishvanathan K Date: Wed, 7 Jul 2021 18:21:43 +0530 Subject: [PATCH 3/3] change !pip to pip --- 5-Clustering/1-Visualize/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md index 12ac7c7b1..8453c4512 100644 --- a/5-Clustering/1-Visualize/README.md +++ b/5-Clustering/1-Visualize/README.md @@ -104,7 +104,7 @@ Clustering as a technique is greatly aided by proper visualization, so let's get 1. Import the `Seaborn` package for good data visualization. ```python - pip install seaborn + !pip install seaborn ``` 1. Append the song data from _nigerian-songs.csv_. Load up a dataframe with some data about the songs. Get ready to explore this data by importing the libraries and dumping out the data: