update list course sites

eavelardev · Jan 25, 2023 · b0a5203 · b0a5203
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diff --git a/job_prep/course_providers_info.md b/job_prep/course_providers_info.md
@@ -1,6 +1,22 @@
-- **Google Cloud Skills Boost** - 29 month, 299 year (24.9 month) (30 day trial) - [link](https://www.cloudskillsboost.google/subscriptions)
-- **Zero to Mastery** - 39 month, 279 year (23 month), lifetime 999 - [link](https://zerotomastery.io/academy/)
-- **A Cloud Guru** - 35, 47 month, 348 year (29 month) - [link](https://acloudguru.com/pricing)
-- **Coursera** - 59 month, 399 year (33.25 month) (7 day trial) - [link](https://www.coursera.org/courseraplus)
-- **RealLife** - 119.97 Full Course 41 week of lessons, 337.97/165.6 promotion - Get Fluent with Friends
-- **365 DataScience** - 36 month, 348 year (29 month) - [link](https://365datascience.com/pricing/)
+
+- **DataCamp**
+    * 15 month, 58 year (4.83 month) - [link](https://www.datacamp.com/promo/zero-to-job-ready-sale-jan-2023)
+- **365 DataScience** - [link](https://365datascience.com/pricing/)
+    * 36 month, 147 year (12.25 month)
+    * 36 month, 348 year (29 month)
+    * 97 quarter (32.33 month)
+- **interviewquery**
+    * 198.96 year (16.58 month) - [link](https://www.interviewquery.com/pricing)
+- **Zero to Mastery**
+    * 39 month, 279 year (23 month), lifetime 999 - [link](https://zerotomastery.io/academy/)
+- **Dataquest**
+    * 294 year (24.5 month)
+- **Google Cloud Skills Boost**
+    * 29 month, 279 year (24.9 month) - [link](https://www.cloudskillsboost.google/subscriptions)
+- **A Cloud Guru**
+    * 35, 47 month, 348 year (29 month) - [link](https://acloudguru.com/pricing)
+- **Coursera**
+    * 59 month, 399 year (33.25 month) - [link](https://www.coursera.org/courseraplus)
+- **RealLife**
+    * 119.97 Full Course 41 week of lessons
+    * 337.97/165.6 promotion - Get Fluent with Friends
diff --git a/mle_certificate/certification_exam_guide.ipynb b/mle_certificate/certification_exam_guide.ipynb
@@ -147,10 +147,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* Google Cloud Skills Boost - [Vertex AI](https://www.cloudskillsboost.google/catalog?keywords=vertex) (34 results)\n",
+    "* Google Cloud Skills Boost - [Vertex AI](https://www.cloudskillsboost.google/catalog?keywords=vertex) (35 results)\n",
     "    * [Course](https://www.cloudskillsboost.google/course_templates/55) (Fundamental) - Smart Analytics, Machine Learning, and AI on Google Cloud\n",
     "    * [Course](https://www.cloudskillsboost.google/course_templates/13) - Building Conversational Experiences with Dialogflow\n",
     "    * [Quest](https://www.cloudskillsboost.google/quests/34) (Fundamental) - **Baseline: Data, ML, AI**\n",
@@ -664,7 +665,7 @@
   },
   "language_info": {
    "name": "python",
-   "version": "3.10.8"
+   "version": "3.10.8 | packaged by conda-forge | (main, Nov 24 2022, 14:07:00) [MSC v.1916 64 bit (AMD64)]"
   },
   "orig_nbformat": 4,
   "vscode": {

diff --git a/mle_certificate/mle_exam.ipynb b/mle_certificate/mle_exam.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,9 +23,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of total questions: 241\n"
+     ]
+    }
+   ],
    "source": [
     "print(f'Number of total questions: {len(questions)}')\n",
     "questions = random.sample(questions, len(questions))\n",
@@ -37,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,18 +125,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You developed a model for a classification task where the minority class appears in 10% of the data set. You ran the training on the original imbalanced data set and have checked the resulting model performance. The confusion matrix indicates that the model did not learn the minority class. You want to improve the model performance while minimizing run time and keeping the predictions calibrated. What should you do?\n",
+      "\n",
+      "* Tune the classification threshold, and calibrate the model with isotonic regression on the validation set.\n",
+      "\n",
+      "* Downsample the majority class in the training set, and update the weight of the downsampled class by the same sampling factor.\n",
+      "\n",
+      "* Upsample the minority class in the training set, and update the weight of the upsampled class by the same sampling factor.\n",
+      "\n",
+      "* Update the weights of the classification function to penalize misclassifications of the minority class.\n"
+     ]
+    }
+   ],
    "source": [
     "i, c = get_question(i)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Downsample the majority class in the training set, and update the weight of the downsampled class by the same sampling factor.\n",
+      "\n",
+      "* Downsampling with upweighting improves performance on the minority class while speeding up convergence and keeping the predictions calibrated.\n",
+      "* This approach does not guarantee calibrated predictions and does not improve training run time.\n",
+      "* This approach increases run time by adding threshold tuning and calibration on top of model training.\n",
+      "* Upsampling increases training run time by providing more data samples during training.\n",
+      "\n",
+      "* https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data\n",
+      "* https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/structured_data/imbalanced_data.ipynb\n",
+      "* https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/calibration/calibration-node-classification.ipynb\n",
+      "* https://developers.google.com/machine-learning/glossary#calibration-layer\n"
+     ]
+    }
+   ],
    "source": [
     "c = get_answers(i, c)"
    ]
@@ -150,7 +192,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.8 | packaged by conda-forge | (main, Nov 24 2022, 14:07:00) [MSC v.1916 64 bit (AMD64)]"
   },
   "orig_nbformat": 4,
   "vscode": {