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ocademy-ai · Oct 12, 2023 · 59baaaf · 59baaaf
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diff --git a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "58b91c67",
+   "id": "64207a31",
    "metadata": {},
    "source": [
     "# Create a regression model\n",

diff --git a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "604ca0aa",
+   "id": "6c9cc1bf",
    "metadata": {},
    "source": [
     "# Exploring visualizations\n",

diff --git a/_sources/assignments/ml-fundamentals/ml-logistic-regression-2.ipynb b/_sources/assignments/ml-fundamentals/ml-logistic-regression-2.ipynb
@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ML logistic regression - assignment 2\n",
+    "\n",
+    "## Logistic Regression from scratch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class MyOwnLogisticRegression:\n",
+    "    def __init__(self, learning_rate=0.001, n_iters=1000):\n",
+    "        self.lr = learning_rate\n",
+    "        self.n_iters = n_iters\n",
+    "        self.weights = None\n",
+    "        self.bias = None\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        n_samples, n_features = X.shape\n",
+    "\n",
+    "        # init parameters\n",
+    "        self.weights = np.zeros(n_features)\n",
+    "        self.bias = 0\n",
+    "\n",
+    "        # gradient descent\n",
+    "        for _ in range(self.n_iters):\n",
+    "            # approximate y with linear combination of weights and x, plus bias\n",
+    "            linear_model = np.dot(X, self.weights) + self.bias\n",
+    "            # apply sigmoid function\n",
+    "            y_predicted = self._sigmoid(linear_model)\n",
+    "\n",
+    "            # compute gradients\n",
+    "            dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))\n",
+    "            db = (1 / n_samples) * np.sum(y_predicted - y)\n",
+    "            # update parameters\n",
+    "            self.weights -= self.lr * dw\n",
+    "            self.bias -= self.lr * db\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        linear_model = np.dot(X, self.weights) + self.bias\n",
+    "        y_predicted = self._sigmoid(linear_model)\n",
+    "        y_predicted_cls = [1 if i > 0.5 else 0 for i in y_predicted]\n",
+    "        return np.array(y_predicted_cls)\n",
+    "\n",
+    "    def _sigmoid(self, x):\n",
+    "        return 1 / (1 + np.exp(-x))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/assignments/ml-fundamentals/parameter-play.ipynb b/_sources/assignments/ml-fundamentals/parameter-play.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c4fa73d6",
+   "id": "ce4ea64b",
    "metadata": {},
    "source": [
     "# Parameter play\n",

diff --git a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "29f1affc",
+   "id": "b80ab429",
    "metadata": {},
    "source": [
     "# Regression with Scikit-learn\n",

diff --git a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0de76a9a",
+   "id": "196ef3c3",
    "metadata": {},
    "source": [
     "# Retrying some regression\n",

diff --git a/_sources/data-science/data-science-in-the-cloud/data-science-in-the-cloud.ipynb b/_sources/data-science/data-science-in-the-cloud/data-science-in-the-cloud.ipynb
@@ -2,39 +2,64 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2f4729e0",
+   "id": "33887561-a556-495e-8744-a192d65a947d",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "jupytext:\n",
+    "  cell_metadata_filter: -all\n",
+    "  formats: md:myst\n",
+    "  text_representation:\n",
+    "    extension: .md\n",
+    "    format_name: myst\n",
+    "    format_version: 0.13\n",
+    "    jupytext_version: 1.11.5\n",
+    "kernelspec:\n",
+    "  display_name: Python 3\n",
+    "  language: python\n",
+    "  name: python3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "53182f7d-455f-4fd6-b9e8-75bbe9d32ed0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d51de614",
    "metadata": {},
    "source": [
     "# Data Science in the cloud\n",
     "\n",
-    "![cloud-picture](../../../images/cloud-picture.jpeg)\n",
+    "![cloud-picture](https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/cloud-picture.jpeg)\n",
     "\n",
     "> Photo by [Jelleke Vanooteghem](https://unsplash.com/@ilumire) from [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape)\n",
     "\n",
     "When it comes to doing data science with big data, the cloud can be a game changer. In the next three sections, we are going to see what the cloud is and why it can be very helpful. We are also going to explore a heart failure dataset and build a model to help assess the probability of someone having heart failure. We will use the power of the cloud to train, deploy and consume a model in two different ways. One way uses only the user interface in a Low code/No code fashion, and the other way uses the Azure Machine Learning Software Developer Kit (Azure ML SDK).\n",
     "\n",
-    "![project-schema](../../../images/project-schema.png)\n",
-    "\n",
-    "---\n",
+    "![project-schema](https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/project-schema.png)\n",
     "\n",
-    "```{tableofcontents}\n",
-    "```"
+    "---\n"
    ]
   }
  ],
  "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "md:myst",
-   "text_representation": {
-    "extension": ".md",
-    "format_name": "myst",
-    "format_version": 0.13,
-    "jupytext_version": "1.11.5"
-   }
-  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -48,12 +73,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  },
-  "source_map": [
-   14
-  ]
+   "version": "3.11.5"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
diff --git a/_sources/data-science/data-science-in-the-cloud/data-science-in-the-cloud.md b/_sources/data-science/data-science-in-the-cloud/data-science-in-the-cloud.md