completing lesson for intro - techniques

1-Introduction/4-techniques-of-ML/README.md

@@ -80,5 +80,16 @@ This is the moment where you can use completely new  data to test your model's a
 
 In these lessons, you will discover how to use these steps to prepare, build, test, evaluate, and predict - all the gestures of a data scientist and more, as you progress in your journey to become a 'full stack' ML engineer.
 
+---
+## 🚀Challenge
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+Draw a flow chart reflecting the steps of a ML practitioner. Where do you see yourself right now in the process? Where do you predict you will find difficulty? What seems easy to you?
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+
+## Review & Self Study
+
+
+
+## Assignment 
+
+[Interview a data scientist](assignment.md)

1-Introduction/4-techniques-of-ML/assignment.md

@@ -1,11 +1,11 @@
-# Assignment
+# Interview a data scientist
 
 ## Instructions
 
-
+In your company, in a user group, or among your friends or fellow students, talk to someone who works professionally as a data scientist. Write a short paper (500 words) about their daily occupations. Are they specialists, or do they work 'full stack'?
 
 ## Rubric
 
-| Criteria | Exemplary | Adequate | Needs Improvement |
-| -------- | --------- | -------- | ----------------- |
-|          |  1        |   2       |  3              |
+| Criteria | Exemplary                                                                            | Adequate                                                           | Needs Improvement     |
+| -------- | ------------------------------------------------------------------------------------ | ------------------------------------------------------------------ | --------------------- |
+|          | An essay of the correct length, with attributed sources, is presented as a .doc file | The essay is poorly attributed or shorter than the required length | No essay is presented |

2-Regression/1-Tools/README.md

@@ -8,14 +8,13 @@
 ## Introduction
 
 
+In these four lessons, you will discover how to build Regression models. We will discuss what these are for shortly. But before you do anything, make sure you have the right tools in place to start the process!
 
-But before you do anything, make sure you have the right tools in place!
-
-In this lesson, you will learn:
+In this lesson, you will learn how to:
 
 - Configure your computer for local machine learning tasks.
-- Getting used to working with Jupyter notebooks.
-- Using Scikit-Learn, including installation.
+- Work with Jupyter notebooks.
+- Use Scikit-Learn, including installation.
 - Explore Linear Regression with a hands-on exercise.
 
 ## Installations and Configurations
@@ -44,7 +43,7 @@ Notebooks are an interactive environment that allow the developer to both code a
 
 ### Exercise - work with A Notebook
 
-In this folder, you will find the file `notebook.ipynb`. 
+In this folder, you will find the file _notebook.ipynb_. 
 
 1. Open _notebook.ipynb_ in Visual Studio Code.