diff --git a/7-TimeSeries/3-SVR/README.md b/7-TimeSeries/3-SVR/README.md
index f73fb3c9..da50702f 100644
--- a/7-TimeSeries/3-SVR/README.md
+++ b/7-TimeSeries/3-SVR/README.md
@@ -173,10 +173,10 @@ print(x_test.shape, y_test.shape)
 
 ### Implement SVR [^1]
 
-Now, It's time to implement SVR:
+Now, it's time to implement SVR. To read more about this implementation, you can refer to [this documentation](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html). For our implementation, we follow these steps:
 
   1. Define the model by calling `SVR()` and passing in the model hyperparameters: kernel, gamma, c and epsilon
-  2. Prepare the model for the training data by calling the `fit()` function.
+  2. Prepare the model for the training data by calling the `fit()` function
   3. Make predictions calling the `predict()` function
 
 Now we create an SVR model. Here we use the [RBF kernel](https://scikit-learn.org/stable/modules/svm.html#parameters-of-the-rbf-kernel), and set the hyperparameters gamma, C and epsilon as 0.5, 10 and 0.05 respectively.
@@ -361,6 +361,10 @@ MAPE:  2.0572089029888656 %
 - Try to use different kernel functions for the model and analyze their performances on the dataset. A helpful document can be found [here](https://scikit-learn.org/stable/modules/svm.html#kernel-functions).
 - Try using different values for `timesteps` for the model to look back to make prediction.
 
+## Review & Self Study
+
+This lesson was to introduce the application of SVR for Time Series Forecasting. To read more about SVR, you can refer to [this blog](https://www.analyticsvidhya.com/blog/2020/03/support-vector-regression-tutorial-for-machine-learning/). This [documentation on scikit-learn](https://scikit-learn.org/stable/modules/svm.html) provides a more comprehensive explanation about SVMs in general, [SVRs](https://scikit-learn.org/stable/modules/svm.html#regression) and also other implementation details such as the different [kernel functions](https://scikit-learn.org/stable/modules/svm.html#kernel-functions) that can be used, and their parameters.
+
 ## Assignment
 
 [A new SVR model](assignment.md)