diff --git a/m04_machine_learning/m04_c02_linear_regression_and_cross_validation/data/brain_and_body_weight.txt b/m04_machine_learning/m04_c02_linear_regression/data/brain_and_body_weight.txt
similarity index 100%
rename from m04_machine_learning/m04_c02_linear_regression_and_cross_validation/data/brain_and_body_weight.txt
rename to m04_machine_learning/m04_c02_linear_regression/data/brain_and_body_weight.txt
diff --git a/m04_machine_learning/m04_c02_linear_regression/m04_c02_lab.ipynb b/m04_machine_learning/m04_c02_linear_regression/m04_c02_lab.ipynb
new file mode 100644
index 0000000..4f1fed2
--- /dev/null
+++ b/m04_machine_learning/m04_c02_linear_regression/m04_c02_lab.ipynb
@@ -0,0 +1,398 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/4/47/Logo_UTFSM.png\" width=\"200\" alt=\"utfsm-logo\" align=\"left\"/>\n",
+    "\n",
+    "# MAT281\n",
+    "### Aplicaciones de la Matemática en la Ingeniería"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Módulo 04\n",
+    "## Laboratorio Clase 02: Regresión Lineal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "### Instrucciones\n",
+    "\n",
+    "\n",
+    "* Completa tus datos personales (nombre y rol USM) en siguiente celda.\n",
+    "* La escala es de 0 a 4 considerando solo valores enteros.\n",
+    "* Debes _pushear_ tus cambios a tu repositorio personal del curso.\n",
+    "* Como respaldo, debes enviar un archivo .zip con el siguiente formato `mXX_cYY_lab_apellido_nombre.zip` a alonso.ogueda@gmail.com, debe contener todo lo necesario para que se ejecute correctamente cada celda, ya sea datos, imágenes, scripts, etc.\n",
+    "* Se evaluará:\n",
+    "    - Soluciones\n",
+    "    - Código\n",
+    "    - Que Binder esté bien configurado.\n",
+    "    - Al presionar  `Kernel -> Restart Kernel and Run All Cells` deben ejecutarse todas las celdas sin error."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "__Nombre__:\n",
+    "\n",
+    "__Rol__:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import altair as alt\n",
+    "\n",
+    "from sklearn import datasets, linear_model\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "alt.themes.enable('opaque')\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "## Ejercicio 1: Diabetes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "Realizar análisis de regresión a los datos de diabetes disponibles en scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "diabetes = datasets.load_diabetes()\n",
+    "print(dir(diabetes))  ## Atributos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "print(diabetes.DESCR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "diabetes_df = (\n",
+    "    pd.DataFrame(\n",
+    "        diabetes.data,\n",
+    "        columns=diabetes.feature_names\n",
+    "    )\n",
+    "    .assign(prog=diabetes.target)\n",
+    ")\n",
+    "\n",
+    "diabetes_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "#### Pregunta 1 (1 pto):\n",
+    "\n",
+    "* ¿Por qué la columna de sexo tiene esos valores?\n",
+    "* ¿Cuál es la columna a predecir?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "**---TU RESPUESTA VA AQUÍ--**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "#### Pregunta 2 (1 pto)\n",
+    "\n",
+    "Realiza una regresión lineal con todas las _features_ incluyendo intercepto."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "X = diabetes_df.drop(## FIX ME PLEASE ##).values\n",
+    "y = diabetes_df[(## FIX ME PLEASE ##].values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "Ajusta el modelo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "regr = ## FIX ME PLEASE ##\n",
+    "regr.## FIX ME PLEASE ##"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "Imprime el intercepto y los coeficientes luego de ajustar el modelo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "print(f\"Intercept: \\n{## FIX ME PLEASE ##}\\n\")\n",
+    "print(f\"Coefficients: \\n{## FIX ME PLEASE ##}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "Haz una predicción del modelo con los datos `X`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = ## FIX ME PLEASE ##"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "Calcula e imprime el error cuadrático medio y el coeficiente de determinación de este modelo ajustado."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "# Error cuadrático medio\n",
+    "print(f\"Mean squared error: {## FIX ME PLEASE ##:.2f}\\n\")\n",
+    "\n",
+    "# Coeficiente de determinación\n",
+    "print(f\"Coefficient of determination: {## FIX ME PLEASE ##:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "**Pregunta: ¿Qué tan bueno fue el ajuste del modelo?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "**---TU RESPUESTA VA AQUÍ--**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "### Pregunta 3 (2 ptos).\n",
+    "\n",
+    "Realizar multiples regresiones lineales utilizando una sola _feature_ a la vez. \n",
+    "\n",
+    "En cada iteración:\n",
+    "\n",
+    "- Crea un arreglo `X`con solo una feature filtrando `X`.\n",
+    "- Crea un modelo de regresión lineal con intercepto.\n",
+    "- Ajusta el modelo anterior.\n",
+    "- Genera una predicción con el modelo.\n",
+    "- Calcula e imprime las métricas de la pregunta anterior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(X.shape[1]):\n",
+    "    X_i = X[:, np.newaxis, i]  # Protip! Trata de entender este paso por tu cuenta, es muy clever\n",
+    "    regr_i = ## FIX ME PLEASE ##\n",
+    "    regr_i.## FIX ME PLEASE ##\n",
+    "    y_pred_i = ## FIX ME PLEASE ##\n",
+    "    print(f\"{diabetes_df.columns[i]}:\")\n",
+    "    print(f\"\\tCoefficients: {## FIX ME PLEASE ##}\")\n",
+    "    print(f\"\\tIntercept: {## FIX ME PLEASE ##}\")\n",
+    "    print(f\"\\tMean squared error: {## FIX ME PLEASE ##:.2f}\")\n",
+    "    print(f\"\\tCoefficient of determination: {## FIX ME PLEASE ##:.2f}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "**Si tuvieras que escoger una sola _feauture_, ¿Cuál sería? ¿Por qué?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "**---TU RESPUESTA VA AQUÍ--**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "Con la feature escogida haz el siguiente gráfico:\n",
+    "\n",
+    "- Scatter Plot\n",
+    "- Eje X: Valores de la feature escogida.\n",
+    "- Eje Y: Valores de la columna a predecir (target).\n",
+    "- En color rojo dibuja la recta correspondiente a la regresión lineal (utilizando `intercept_`y `coefs_`).\n",
+    "- Coloca un título adecuado, nombre de los ejes, etc.\n",
+    "\n",
+    "Puedes utilizar `matplotlib` o `altair`, el que prefiera."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "## FIX ME PLEASE ##"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "Python [conda env:ds]",
+   "language": "python",
+   "name": "conda-env-ds-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/m04_machine_learning/m04_c02_linear_regression_and_cross_validation/m04_c02_linear_regression_and_cross_validation.ipynb b/m04_machine_learning/m04_c02_linear_regression/m04_c02_linear_regression.ipynb
similarity index 99%
rename from m04_machine_learning/m04_c02_linear_regression_and_cross_validation/m04_c02_linear_regression_and_cross_validation.ipynb
rename to m04_machine_learning/m04_c02_linear_regression/m04_c02_linear_regression.ipynb
index f4a3a43..bd6f810 100644
--- a/m04_machine_learning/m04_c02_linear_regression_and_cross_validation/m04_c02_linear_regression_and_cross_validation.ipynb
+++ b/m04_machine_learning/m04_c02_linear_regression/m04_c02_linear_regression.ipynb
@@ -1847,7 +1847,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1min 37s ± 4.09 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "1min 36s ± 3.84 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -1899,7 +1899,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "225 ms ± 5.41 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "235 ms ± 12.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -1951,7 +1951,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "17.6 µs ± 2.06 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
+      "15.7 µs ± 121 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
      ]
     }
    ],
@@ -2003,7 +2003,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "258 µs ± 26.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+      "254 µs ± 2.96 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
      ]
     }
    ],
@@ -2562,7 +2562,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
   },
   "toc-autonumbering": false,
   "toc-showtags": false