added readme ipynb

fawazsiddiqi · fawazsiddiqi · commit 82ccf8e809bd · 2020-09-15T17:45:23.000+04:00
diff --git a/notebooks/00-clustering_with_scikit-learn-readme.ipynb b/notebooks/00-clustering_with_scikit-learn-readme.ipynb
@@ -2,8 +2,6 @@
  "cells": [
   {
    "source": [
-    "## UNDER DEVELOPMENT BY FAWAZ SIDDIQI (github.com/fawazsiddiqi)\n",
-    "\n",
     "## Introduction\n",
     "\n",
     "In this tutorial, you use unsupervised learning to discover groupings and anomalies in data. Unsupervised learning is when there is no *ground truth* or labeled data set that shows you the expected result. Instead, you take the raw data and use various algorithms to uncover clusters of data. If you want to learn about the theory and ideas behind unsupervised learning, read [Unsupervised learning for data classification](https://developer.ibm.com/articles/cc-unsupervised-learning-data-classification/).\n",

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`@@ -2,8 +2,6 @@`
`2`	`2`	`"cells": [`
`3`	`3`	`{`
`4`	`4`	`"source": [`
`5`		`- "## UNDER DEVELOPMENT BY FAWAZ SIDDIQI (github.com/fawazsiddiqi)\n",`
`6`		`- "\n",`
`7`	`5`	`"## Introduction\n",`
`8`	`6`	`"\n",`
`9`	`7`	"In this tutorial, you use unsupervised learning to discover groupings and anomalies in data. Unsupervised learning is when there is no ground truth or labeled data set that shows you the expected result. Instead, you take the raw data and use various algorithms to uncover clusters of data. If you want to learn about the theory and ideas behind unsupervised learning, read [Unsupervised learning for data classification](https://developer.ibm.com/articles/cc-unsupervised-learning-data-classification/).\n",