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@@ -1,14 +1,10 @@
 # W2D01  Piscine AI - Data Science
 
-The goal of this day is to understand practical Linear regression and supervised learning.
-
-Author:
+![Alt Text](w2_day01_linear_regression_video.gif)
 
-# Table of Contents
+## Linear regression with Scikit Learn
 
-Historical part:
-
-# Introduction
+The goal of this day is to understand practical Linear regression and supervised learning.
 
 The word "regression" was introduced by Sir Francis Galton (a cousin of C. Darwin) when he
 studied the size of individuals within a progeny. He was trying to understand why
@@ -19,9 +15,25 @@ Today we will learn a basic algorithm used in **supervised learning** : **The Li
 
 We will also learn progressively the Machine Learning methodology for supervised learning - today we will focus on evaluating a machine learning model by splitting the data set in a train set and a test set.
 
-'0.22.1'
+## Exercises of the day
+
+- Exercise 1 Scikit-learn estimator
+- Exercise 2 Linear regression in 1D
+- Exercise 3 Train test split
+- Exercise 4 Forecast diabetes progression
+- Exercise 5 Forecast diabetes progression
+- Bonus: Exercise 6 Gradient Descent - **Optional**
+
+
+## Virtual Environment 
+- Python 3.x
+- NumPy
+- Pandas
+- Matplotlib
+- Scikit Learn
+- Jupyter or JupyterLab
 
-## Rules
+*Version of Scikit Learn I used to do the exercises: 0.22*. I suggest to use the most recent one. Scikit Learn 1.0 is finally available after ... 14 years. 
 
 ## Ressources
 
@@ -51,4 +63,4 @@ We will also learn progressively the Machine Learning methodology for supervised
 
 - https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/
 
-- https://developers.google.com/machine-learning/crash-course/training-and-test-sets/video-lecture?hl=en
+- https://developers.google.com/machine-learning/crash-course/training-and-test-sets/video-lecture?hl=en
\ No newline at end of file
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