moving time series graphic

7-TimeSeries/1-Introduction/README.md

@@ -1,5 +1,9 @@
 # Introduction to Time Series Forecasting
 
+![Summary of Time series in a sketchnote](../sketchnotes/ml-timeseries.png)
+
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
 In this lesson and the following one, you will learn a bit about Time Series Forecasting, an interesting and valuable part of a ML scientist's repertoire that is a bit lesser known than other topics. Time Series Forecasting is a sort of crystal ball: based on past performance of a variable such as price, you can predict its future potential value.
 
 [![Introduction to Time Series Forecasting](https://img.youtube.com/vi/wGUV_XqchbE/0.jpg)](https://youtu.be/wGUV_XqchbE "Introduction to Time Series Forecasting")

7-TimeSeries/README.md

@@ -1,10 +1,5 @@
-
-
 # Time Series Forecasting
 
-![Summary of TIme series in a sketchnote](../sketchnotes/ml-timeseries.png)
-> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
-
 In these two lessons, you will be introduced to Time Series Forecasting, a somewhat lesser known area of Machine Learning that is nevertheless extremely valuable for industry and business applications, among other fields. While neural networks can be used to enhance the utility of these models, we will study them in the context of classical machine learning as models help predict future performance based on the past.
 
 Our regional focus is electrical usage in the world, an interesting dataset to learn about forecasting future power usage based on patterns of past load. You can see how this kind of forecasting can be extremely helpful in a business environment.