diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index dd8fa409..62994409 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -149,10 +149,11 @@ In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The
 
     ✅ Think a bit about the relationship between the data and the regression target. Linear regression predicts relationships between feature X and target variable y. Can you find the [target](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) for the diabetes dataset in the documentation? What is this dataset demonstrating, given that target?
 
-2. Next, select a portion of this dataset to plot by arranging it into a new array using numpy's `newaxis` function. We are going to use linear regression to generate a line between values in this data, according to a pattern it determines.
+2. Next, select a portion of this dataset to plot by selecting the 3rd column of the dataset. You can do this by using the `:` operator to select all rows, and then selecting the 3rd column using the index (2). You can also reshape the data to be a 2D array - as required for plotting - by using `reshape(n_rows, n_columns)`. If one of the parameter is -1, the corresponding dimension is calculated automatically.
 
    ```python
-   X = X[:, np.newaxis, 2]
+   X = X[:, 2]
+   X = X.reshape((-1,1))
    ```
 
    ✅ At any time, print out the data to check its shape.
diff --git a/2-Regression/1-Tools/solution/notebook.ipynb b/2-Regression/1-Tools/solution/notebook.ipynb
index ceb81b9c..7b32a8f5 100644
--- a/2-Regression/1-Tools/solution/notebook.ipynb
+++ b/2-Regression/1-Tools/solution/notebook.ipynb
@@ -1,44 +1,18 @@
 {
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
-  },
-  "metadata": {
-   "interpreter": {
-    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
  "cells": [
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Linear Regression for Diabetes dataset - Lesson 1"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Import needed libraries"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -52,11 +26,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Load the diabetes dataset, divided into `X` data and `y` features"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -64,10 +38,12 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "(442, 10)\n[ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076\n -0.04340085 -0.00259226  0.01990842 -0.01764613]\n"
+      "(442, 10)\n",
+      "[ 0.03807591  0.05068012  0.06169621  0.02187239 -0.0442235  -0.03482076\n",
+      " -0.04340085 -0.00259226  0.01990749 -0.01764613]\n"
      ]
     }
    ],
@@ -78,31 +54,503 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Select just one feature to target for this exercise"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442,)\n"
+     ]
+    }
+   ],
    "source": [
-    "X = X[:, np.newaxis, 2]\n"
+    "# Selecting the 3rd feature\n",
+    "X = X[:, 2]\n",
+    "print(X.shape)\n"
    ]
   },
   {
-   "source": [
-    "Split the training and test data for both `X` and `y`"
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 1)\n",
+      "[[ 0.06169621]\n",
+      " [-0.05147406]\n",
+      " [ 0.04445121]\n",
+      " [-0.01159501]\n",
+      " [-0.03638469]\n",
+      " [-0.04069594]\n",
+      " [-0.04716281]\n",
+      " [-0.00189471]\n",
+      " [ 0.06169621]\n",
+      " [ 0.03906215]\n",
+      " [-0.08380842]\n",
+      " [ 0.01750591]\n",
+      " [-0.02884001]\n",
+      " [-0.00189471]\n",
+      " [-0.02560657]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [ 0.01211685]\n",
+      " [-0.0105172 ]\n",
+      " [-0.01806189]\n",
+      " [-0.05686312]\n",
+      " [-0.02237314]\n",
+      " [-0.00405033]\n",
+      " [ 0.06061839]\n",
+      " [ 0.03582872]\n",
+      " [-0.01267283]\n",
+      " [-0.07734155]\n",
+      " [ 0.05954058]\n",
+      " [-0.02129532]\n",
+      " [-0.00620595]\n",
+      " [ 0.04445121]\n",
+      " [-0.06548562]\n",
+      " [ 0.12528712]\n",
+      " [-0.05039625]\n",
+      " [-0.06332999]\n",
+      " [-0.03099563]\n",
+      " [ 0.02289497]\n",
+      " [ 0.01103904]\n",
+      " [ 0.07139652]\n",
+      " [ 0.01427248]\n",
+      " [-0.00836158]\n",
+      " [-0.06764124]\n",
+      " [-0.0105172 ]\n",
+      " [-0.02345095]\n",
+      " [ 0.06816308]\n",
+      " [-0.03530688]\n",
+      " [-0.01159501]\n",
+      " [-0.0730303 ]\n",
+      " [-0.04177375]\n",
+      " [ 0.01427248]\n",
+      " [-0.00728377]\n",
+      " [ 0.0164281 ]\n",
+      " [-0.00943939]\n",
+      " [-0.01590626]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.04931844]\n",
+      " [ 0.04121778]\n",
+      " [-0.06332999]\n",
+      " [-0.06440781]\n",
+      " [-0.02560657]\n",
+      " [-0.00405033]\n",
+      " [ 0.00457217]\n",
+      " [-0.00728377]\n",
+      " [-0.0374625 ]\n",
+      " [-0.02560657]\n",
+      " [-0.02452876]\n",
+      " [-0.01806189]\n",
+      " [-0.01482845]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [-0.06979687]\n",
+      " [ 0.03367309]\n",
+      " [-0.00405033]\n",
+      " [-0.02021751]\n",
+      " [ 0.00241654]\n",
+      " [-0.03099563]\n",
+      " [ 0.02828403]\n",
+      " [-0.03638469]\n",
+      " [-0.05794093]\n",
+      " [-0.0374625 ]\n",
+      " [ 0.01211685]\n",
+      " [-0.02237314]\n",
+      " [-0.03530688]\n",
+      " [ 0.00996123]\n",
+      " [-0.03961813]\n",
+      " [ 0.07139652]\n",
+      " [-0.07518593]\n",
+      " [-0.00620595]\n",
+      " [-0.04069594]\n",
+      " [-0.04824063]\n",
+      " [-0.02560657]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.00457217]\n",
+      " [-0.06440781]\n",
+      " [-0.01698407]\n",
+      " [-0.05794093]\n",
+      " [ 0.00996123]\n",
+      " [ 0.08864151]\n",
+      " [-0.00512814]\n",
+      " [-0.06440781]\n",
+      " [ 0.01750591]\n",
+      " [-0.04500719]\n",
+      " [ 0.02828403]\n",
+      " [ 0.04121778]\n",
+      " [ 0.06492964]\n",
+      " [-0.03207344]\n",
+      " [-0.07626374]\n",
+      " [ 0.04984027]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03207344]\n",
+      " [ 0.00457217]\n",
+      " [ 0.02073935]\n",
+      " [ 0.01427248]\n",
+      " [ 0.11019775]\n",
+      " [ 0.00133873]\n",
+      " [ 0.05846277]\n",
+      " [-0.02129532]\n",
+      " [-0.0105172 ]\n",
+      " [-0.04716281]\n",
+      " [ 0.00457217]\n",
+      " [ 0.01750591]\n",
+      " [ 0.08109682]\n",
+      " [ 0.0347509 ]\n",
+      " [ 0.02397278]\n",
+      " [-0.00836158]\n",
+      " [-0.06117437]\n",
+      " [-0.00189471]\n",
+      " [-0.06225218]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.09618619]\n",
+      " [-0.06979687]\n",
+      " [-0.02129532]\n",
+      " [-0.05362969]\n",
+      " [ 0.0433734 ]\n",
+      " [ 0.05630715]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04984027]\n",
+      " [ 0.11127556]\n",
+      " [ 0.06169621]\n",
+      " [ 0.01427248]\n",
+      " [ 0.04768465]\n",
+      " [ 0.01211685]\n",
+      " [ 0.00564998]\n",
+      " [ 0.04660684]\n",
+      " [ 0.12852056]\n",
+      " [ 0.05954058]\n",
+      " [ 0.09295276]\n",
+      " [ 0.01535029]\n",
+      " [-0.00512814]\n",
+      " [ 0.0703187 ]\n",
+      " [-0.00405033]\n",
+      " [-0.00081689]\n",
+      " [-0.04392938]\n",
+      " [ 0.02073935]\n",
+      " [ 0.06061839]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03315126]\n",
+      " [-0.06548562]\n",
+      " [ 0.0433734 ]\n",
+      " [-0.06225218]\n",
+      " [ 0.06385183]\n",
+      " [ 0.03043966]\n",
+      " [ 0.07247433]\n",
+      " [-0.0191397 ]\n",
+      " [-0.06656343]\n",
+      " [-0.06009656]\n",
+      " [ 0.06924089]\n",
+      " [ 0.05954058]\n",
+      " [-0.02668438]\n",
+      " [-0.02021751]\n",
+      " [-0.046085  ]\n",
+      " [ 0.07139652]\n",
+      " [-0.07949718]\n",
+      " [ 0.00996123]\n",
+      " [-0.03854032]\n",
+      " [ 0.01966154]\n",
+      " [ 0.02720622]\n",
+      " [-0.00836158]\n",
+      " [-0.01590626]\n",
+      " [ 0.00457217]\n",
+      " [-0.04285156]\n",
+      " [ 0.00564998]\n",
+      " [-0.03530688]\n",
+      " [ 0.02397278]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00297252]\n",
+      " [-0.06656343]\n",
+      " [-0.01267283]\n",
+      " [-0.04177375]\n",
+      " [-0.03099563]\n",
+      " [-0.00512814]\n",
+      " [-0.05901875]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00349435]\n",
+      " [ 0.05415152]\n",
+      " [-0.04500719]\n",
+      " [-0.05794093]\n",
+      " [-0.05578531]\n",
+      " [ 0.00133873]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00672779]\n",
+      " [ 0.04660684]\n",
+      " [ 0.02612841]\n",
+      " [ 0.04552903]\n",
+      " [ 0.04013997]\n",
+      " [-0.01806189]\n",
+      " [ 0.01427248]\n",
+      " [ 0.03690653]\n",
+      " [ 0.00349435]\n",
+      " [-0.07087468]\n",
+      " [-0.03315126]\n",
+      " [ 0.09403057]\n",
+      " [ 0.03582872]\n",
+      " [ 0.03151747]\n",
+      " [-0.06548562]\n",
+      " [-0.04177375]\n",
+      " [-0.03961813]\n",
+      " [-0.03854032]\n",
+      " [-0.02560657]\n",
+      " [-0.02345095]\n",
+      " [-0.06656343]\n",
+      " [ 0.03259528]\n",
+      " [-0.046085  ]\n",
+      " [-0.02991782]\n",
+      " [-0.01267283]\n",
+      " [-0.01590626]\n",
+      " [ 0.07139652]\n",
+      " [-0.03099563]\n",
+      " [ 0.00026092]\n",
+      " [ 0.03690653]\n",
+      " [ 0.03906215]\n",
+      " [-0.01482845]\n",
+      " [ 0.00672779]\n",
+      " [-0.06871905]\n",
+      " [-0.00943939]\n",
+      " [ 0.01966154]\n",
+      " [ 0.07462995]\n",
+      " [-0.00836158]\n",
+      " [-0.02345095]\n",
+      " [-0.046085  ]\n",
+      " [ 0.05415152]\n",
+      " [-0.03530688]\n",
+      " [-0.03207344]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04768465]\n",
+      " [ 0.06061839]\n",
+      " [ 0.05630715]\n",
+      " [ 0.09834182]\n",
+      " [ 0.05954058]\n",
+      " [ 0.03367309]\n",
+      " [ 0.05630715]\n",
+      " [-0.06548562]\n",
+      " [ 0.16085492]\n",
+      " [-0.05578531]\n",
+      " [-0.02452876]\n",
+      " [-0.03638469]\n",
+      " [-0.00836158]\n",
+      " [-0.04177375]\n",
+      " [ 0.12744274]\n",
+      " [-0.07734155]\n",
+      " [ 0.02828403]\n",
+      " [-0.02560657]\n",
+      " [-0.06225218]\n",
+      " [-0.00081689]\n",
+      " [ 0.08864151]\n",
+      " [-0.03207344]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00888341]\n",
+      " [ 0.00672779]\n",
+      " [-0.02021751]\n",
+      " [-0.02452876]\n",
+      " [-0.01159501]\n",
+      " [ 0.02612841]\n",
+      " [-0.05901875]\n",
+      " [-0.03638469]\n",
+      " [-0.02452876]\n",
+      " [ 0.01858372]\n",
+      " [-0.0902753 ]\n",
+      " [-0.00512814]\n",
+      " [-0.05255187]\n",
+      " [-0.02237314]\n",
+      " [-0.02021751]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00620595]\n",
+      " [-0.01698407]\n",
+      " [ 0.05522933]\n",
+      " [ 0.07678558]\n",
+      " [ 0.01858372]\n",
+      " [-0.02237314]\n",
+      " [ 0.09295276]\n",
+      " [-0.03099563]\n",
+      " [ 0.03906215]\n",
+      " [-0.06117437]\n",
+      " [-0.00836158]\n",
+      " [-0.0374625 ]\n",
+      " [-0.01375064]\n",
+      " [ 0.07355214]\n",
+      " [-0.02452876]\n",
+      " [ 0.03367309]\n",
+      " [ 0.0347509 ]\n",
+      " [-0.03854032]\n",
+      " [-0.03961813]\n",
+      " [-0.00189471]\n",
+      " [-0.03099563]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06492964]\n",
+      " [ 0.04013997]\n",
+      " [-0.02345095]\n",
+      " [ 0.05307371]\n",
+      " [ 0.04013997]\n",
+      " [-0.02021751]\n",
+      " [ 0.01427248]\n",
+      " [-0.03422907]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00457217]\n",
+      " [ 0.03043966]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.06169621]\n",
+      " [-0.00728377]\n",
+      " [ 0.00564998]\n",
+      " [ 0.05415152]\n",
+      " [-0.00836158]\n",
+      " [ 0.114509  ]\n",
+      " [ 0.06708527]\n",
+      " [-0.05578531]\n",
+      " [ 0.03043966]\n",
+      " [-0.02560657]\n",
+      " [ 0.10480869]\n",
+      " [-0.00620595]\n",
+      " [-0.04716281]\n",
+      " [-0.04824063]\n",
+      " [ 0.08540807]\n",
+      " [-0.01267283]\n",
+      " [-0.03315126]\n",
+      " [-0.00728377]\n",
+      " [-0.01375064]\n",
+      " [ 0.05954058]\n",
+      " [ 0.02181716]\n",
+      " [ 0.01858372]\n",
+      " [-0.01159501]\n",
+      " [-0.00297252]\n",
+      " [ 0.01750591]\n",
+      " [-0.02991782]\n",
+      " [-0.02021751]\n",
+      " [-0.05794093]\n",
+      " [ 0.06061839]\n",
+      " [-0.04069594]\n",
+      " [-0.07195249]\n",
+      " [-0.05578531]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03315126]\n",
+      " [ 0.04984027]\n",
+      " [-0.08488624]\n",
+      " [ 0.00564998]\n",
+      " [ 0.02073935]\n",
+      " [-0.00728377]\n",
+      " [ 0.10480869]\n",
+      " [-0.02452876]\n",
+      " [-0.00620595]\n",
+      " [-0.03854032]\n",
+      " [ 0.13714305]\n",
+      " [ 0.17055523]\n",
+      " [ 0.00241654]\n",
+      " [ 0.03798434]\n",
+      " [-0.05794093]\n",
+      " [-0.00943939]\n",
+      " [-0.02345095]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03422907]\n",
+      " [-0.00297252]\n",
+      " [ 0.06816308]\n",
+      " [ 0.00996123]\n",
+      " [ 0.00241654]\n",
+      " [-0.03854032]\n",
+      " [ 0.02612841]\n",
+      " [-0.08919748]\n",
+      " [ 0.06061839]\n",
+      " [-0.02884001]\n",
+      " [-0.02991782]\n",
+      " [-0.0191397 ]\n",
+      " [-0.04069594]\n",
+      " [ 0.01535029]\n",
+      " [-0.02452876]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06924089]\n",
+      " [-0.06979687]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [ 0.01858372]\n",
+      " [ 0.00133873]\n",
+      " [-0.03099563]\n",
+      " [-0.00405033]\n",
+      " [ 0.01535029]\n",
+      " [ 0.02289497]\n",
+      " [ 0.04552903]\n",
+      " [-0.04500719]\n",
+      " [-0.03315126]\n",
+      " [ 0.097264  ]\n",
+      " [ 0.05415152]\n",
+      " [ 0.12313149]\n",
+      " [-0.08057499]\n",
+      " [ 0.09295276]\n",
+      " [-0.05039625]\n",
+      " [-0.01159501]\n",
+      " [-0.0277622 ]\n",
+      " [ 0.05846277]\n",
+      " [ 0.08540807]\n",
+      " [-0.00081689]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00888341]\n",
+      " [ 0.08001901]\n",
+      " [ 0.07139652]\n",
+      " [-0.02452876]\n",
+      " [-0.0547075 ]\n",
+      " [-0.03638469]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.07786339]\n",
+      " [-0.03961813]\n",
+      " [ 0.01103904]\n",
+      " [-0.04069594]\n",
+      " [-0.03422907]\n",
+      " [ 0.00564998]\n",
+      " [ 0.08864151]\n",
+      " [-0.03315126]\n",
+      " [-0.05686312]\n",
+      " [-0.03099563]\n",
+      " [ 0.05522933]\n",
+      " [-0.06009656]\n",
+      " [ 0.00133873]\n",
+      " [-0.02345095]\n",
+      " [-0.07410811]\n",
+      " [ 0.01966154]\n",
+      " [-0.01590626]\n",
+      " [-0.01590626]\n",
+      " [ 0.03906215]\n",
+      " [-0.0730303 ]]\n"
+     ]
+    }
    ],
+   "source": [
+    "#Reshaping to get a 2D array\n",
+    "X = X.reshape(-1, 1)\n",
+    "print(X.shape)\n",
+    "print(X)"
+   ]
+  },
+  {
    "cell_type": "markdown",
-   "metadata": {}
+   "metadata": {},
+   "source": [
+    "Split the training and test data for both `X` and `y`"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,26 +558,29 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Select the model and fit it with the training data"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+       "LinearRegression()"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "execution_count": 5
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -138,15 +589,15 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Use test data to predict a line"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,27 +605,26 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Display the results in a plot"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
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\n"
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
-     "metadata": {
-      "needs_background": "light"
-     }
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -183,5 +633,32 @@
     "plt.show()"
    ]
   }
- ]
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index 8427313e..298f552e 100644
--- a/2-Regression/2-Data/README.md
+++ b/2-Regression/2-Data/README.md
@@ -73,11 +73,11 @@ Open the _notebook.ipynb_ file in Visual Studio Code and import the spreadsheet
 
     There is missing data, but maybe it won't matter for the task at hand.
 
-1. To make your dataframe easier to work with, drop several of its columns, using `drop()`, keeping only the columns you need:
+1. To make your dataframe easier to work with, select only the columns you need, using the `loc` function which extracts from the original dataframe a group of rows (passed as first parameter) and columns (passed as second parameter). The expression `:` in the case below means "all rows".
 
     ```python
-    new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
-    pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+    columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']
+    pumpkins = pumpkins.loc[:, columns_to_select]
     ```
 
 ### Second, determine average price of pumpkin
diff --git a/2-Regression/2-Data/solution/notebook.ipynb b/2-Regression/2-Data/solution/notebook.ipynb
index 7c8ec0b6..3595485d 100644
--- a/2-Regression/2-Data/solution/notebook.ipynb
+++ b/2-Regression/2-Data/solution/notebook.ipynb
@@ -304,8 +304,8 @@
    "source": [
     "\n",
     "# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
-    "new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']\n",
-    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
     "\n",
     "# Get an average between low and high price for the base pumpkin price\n",
     "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
@@ -412,7 +412,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.11.1"
   },
   "metadata": {
    "interpreter": {
diff --git a/2-Regression/3-Linear/notebook.ipynb b/2-Regression/3-Linear/notebook.ipynb
index 2da56e5b..70052064 100644
--- a/2-Regression/3-Linear/notebook.ipynb
+++ b/2-Regression/3-Linear/notebook.ipynb
@@ -344,8 +344,8 @@
    "source": [
     "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
     "\n",
-    "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
-    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+    "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
     "\n",
     "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
     "\n",