typo

Author: jmportilla

.ipynb_checkpoints/Supervised Learning - Naive Bayes-checkpoint.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Naive Bayes Classifiers\n",
     "\n",
-    "In this lecture we will learn how to use Naive Bayes Classifier to perform a Multi Class CLassification on a data set we are already familiar with: the Iris Data Set. This Lecture will consist of 7 main parts:\n",
+    "In this lecture we will learn how to use Naive Bayes Classifier to perform a Multi Class Classification on a data set we are already familiar with: the Iris Data Set. This Lecture will consist of 7 main parts:\n",
     "\n",
     "    Part 1: Note on Notation and Math Terms\n",
     "    Part 2: Bayes' Theorem\n",
@@ -25,7 +25,7 @@
    "source": [
     "#### Part 1: Note on Notation and Math Terms\n",
     "\n",
-    "There are a few more advanced notations and amthematical terms used during the exaplanation of naive Bayes Classification.\n",
+    "There are a few more advanced notations and amthematical terms used during the explanation of naive Bayes Classification.\n",
     "You should be familiar with the following:\n",
     "\n",
     "[Product of Sequence](http://en.wikipedia.org/wiki/Product_%28mathematics%29#Product_of_sequences)\n",
@@ -389,7 +389,12 @@
     "2.) [Naive Bayes with NLTK](http://slendermeans.org/ml4h-ch3.html)\n",
     "\n",
     "3.) [Wikipedia on Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier)\n",
-    "\n"
+    "\n",
+    "4.) [Andrew Ng's Class Notes](http://cs229.stanford.edu/notes/cs229-notes2.pdf)\n",
+    "\n",
+    "5.) [Andrew Ng's Video Lecture on Naive Bayes](https://www.youtube.com/watch?v=z5UQyCESW64)\n",
+    "\n",
+    "6.) [UC Berkeley Lecture by Pieter Abbeel](https://www.youtube.com/watch?v=DNvwfNEiKvw)"
    ]
   },
   {

Supervised Learning - Naive Bayes.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Naive Bayes Classifiers\n",
     "\n",
-    "In this lecture we will learn how to use Naive Bayes Classifier to perform a Multi Class CLassification on a data set we are already familiar with: the Iris Data Set. This Lecture will consist of 7 main parts:\n",
+    "In this lecture we will learn how to use Naive Bayes Classifier to perform a Multi Class Classification on a data set we are already familiar with: the Iris Data Set. This Lecture will consist of 7 main parts:\n",
     "\n",
     "    Part 1: Note on Notation and Math Terms\n",
     "    Part 2: Bayes' Theorem\n",
@@ -25,7 +25,7 @@
    "source": [
     "#### Part 1: Note on Notation and Math Terms\n",
     "\n",
-    "There are a few more advanced notations and amthematical terms used during the exaplanation of naive Bayes Classification.\n",
+    "There are a few more advanced notations and amthematical terms used during the explanation of naive Bayes Classification.\n",
     "You should be familiar with the following:\n",
     "\n",
     "[Product of Sequence](http://en.wikipedia.org/wiki/Product_%28mathematics%29#Product_of_sequences)\n",
@@ -389,7 +389,12 @@
     "2.) [Naive Bayes with NLTK](http://slendermeans.org/ml4h-ch3.html)\n",
     "\n",
     "3.) [Wikipedia on Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier)\n",
-    "\n"
+    "\n",
+    "4.) [Andrew Ng's Class Notes](http://cs229.stanford.edu/notes/cs229-notes2.pdf)\n",
+    "\n",
+    "5.) [Andrew Ng's Video Lecture on Naive Bayes](https://www.youtube.com/watch?v=z5UQyCESW64)\n",
+    "\n",
+    "6.) [UC Berkeley Lecture by Pieter Abbeel](https://www.youtube.com/watch?v=DNvwfNEiKvw)"
    ]
   },
   {