diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..cb2fdb3
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="PROJECT_PROFILE" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
index fea06a7..982e2fd 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,15 +1,5 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectLevelVcsManager" settingsEditedManually="false">
-    <OptionsSetting value="true" id="Add" />
-    <OptionsSetting value="true" id="Remove" />
-    <OptionsSetting value="true" id="Checkout" />
-    <OptionsSetting value="true" id="Update" />
-    <OptionsSetting value="true" id="Status" />
-    <OptionsSetting value="true" id="Edit" />
-    <ConfirmationsSetting value="0" id="Add" />
-    <ConfirmationsSetting value="0" id="Remove" />
-  </component>
   <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.5.2 (C:\Users\zanlo\Anaconda3\python.exe)" project-jdk-type="Python SDK" />
   <component name="masterDetails">
     <states>
diff --git a/README.md b/README.md
index 240a9d6..9b3e11c 100644
--- a/README.md
+++ b/README.md
@@ -19,10 +19,50 @@ Show you simple machine learning can actually be, where the real hard part is ac
 
 ## Part 3 - Our Method and where we will be getting our Data
 Cover how to acquire, label and organize data, as well as figure out which machine learning algorithm to use.  
-[Video](https://youtu.be/AleGZ9dkfPs?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3)
+[Video](https://youtu.be/AleGZ9dkfPs?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3) |
 [Text](https://pythonprogramming.net/data-acquisition-machine-learning/)
 
 ## Part 4 - Parsing data
 How to handle our data set for machine learning. Cover basic code regarding how to pull specific data points out of the file.  
-[Video](https://youtu.be/rAdAVcS4aL0?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3)
+[Video](https://youtu.be/rAdAVcS4aL0?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3) |
 [Text](https://pythonprogramming.net/getting-data-machine-learning/?completed=/data-acquisition-machine-learning/)
+
+## Part 5 - More Parsing
+Pulling out the specific data point that we're interested in as using as a feature.
+[Video](https://youtu.be/2vQfMAEu670?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3) |
+[Text](https://pythonprogramming.net/parsing-data-website-machine-learning/)
+
+## Part 6 - More Parsing
+Use the Pandas module to help structure and modify our data.  
+[Video](https://youtu.be/cdaMWZIy5vA?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3)) |
+[Text](https://pythonprogramming.net/using-pandas-structure-process-data/)
+
+## Part 7 - Getting more data and meshing data sets
+Grab S&P 500 index data to use as a benchmark. Label stocks that outperform market or not.  
+[Video](https://youtu.be/PMAwBh0nrds?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3) |
+[Text](https://pythonprogramming.net/getting-data-sp-500-index-value-comparison/)
+
+## Part 8 - Labeling of data part 1
+Label data using the stock price's performance compared to the S&P 500 index's performance.  
+[Video](https://youtu.be/QJKJBVUywDM?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3) |
+[Text](https://pythonprogramming.net/labeling-data-machine-learning/)
+
+## Part 9 - Labeling of data part 2
+Calculate the difference in percent change performance between the individual stocks and the overall S&P 500 index.  
+[Video](https://youtu.be/THOyYh-Bfno?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3) |
+[Text](https://pythonprogramming.net/labeling-data-machine-learning-part-2/)
+
+## Part 10 - More Parsing
+Labeling data as out or under-performing the S&P500.  
+[Video](https://youtu.be/Tk2JfUr6IT4?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3) |
+[Text](https://pythonprogramming.net/label-data-machine-learning/)
+
+## Part 11 - Linear SVC Machine learning SVM example with Python
+Basic linear SVC example with scikit-learn.  
+[Video](https://youtu.be/81ZGOib7DTk?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3) |
+[Text](https://pythonprogramming.net/linear-svc-example-scikit-learn-svm-python/)
+
+## Part 12 - Getting more features from our data
+How to grab more data features from our data set for us to do learning on.
+[Video](https://youtu.be/4WM6hB7l4Lc?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3) |
+[Text](https://pythonprogramming.net/collecting-features-machine-learning/)