Added things to the list

Author: Michael Galarnyk

Statistics/Sample_With_Replacement/.ipynb_checkpoints/SampleWithReplacement-checkpoint.ipynb

@@ -502,7 +502,6 @@
    "metadata": {},
    "source": [
     "![](images/SampleWithoutReplacement.png)\n",
-    "Caption: Sampling without replacement. Image by [Michael Galarnyk](https://twitter.com/GalarnykMichael).\n",
     "\n",
     "Sampling without replacement can be defined as random sampling that DOES NOT allow sampling units to occur more than once. Let's now go over a quick example of how sampling <b>without</b> replacement works.\n",
     "\n",
@@ -606,14 +605,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Sampling without replacement is used throughout data science. One very common use is in model validation procedures like [train test split](https://towardsdatascience.com/understanding-train-test-split-scikit-learn-python-ea676d5e3d1) and [cross validation](https://scikit-learn.org/stable/modules/cross_validation.html). In short, each of these procedures allows you to simulate how a machine learning model would perform on new/unseen data. \n",
+    "Sampling without replacement is used throughout data science. One very common use is in model validation procedures like [train test split](https://builtin.com/data-science/train-test-split) and [cross validation](https://scikit-learn.org/stable/modules/cross_validation.html). In short, each of these procedures allows you to simulate how a machine learning model would perform on new/unseen data. \n",
     "\n",
     "The image below shows the train test split procedure which consists of splitting a dataset into two pieces: a training set and a testing set. This consists of randomly sampling WITHOUT replacement about 75% (you can vary this) of the rows and putting them into your training set and putting the remaining 25% to your test set. Note that the colors in “Features” and “Target” indicate where their data will go (“X_train”, “X_test”, “y_train”, “y_test”) for a particular train test split.\n",
     "\n",
     "![](images/TrainTestSplit.png)\n",
-    "Caption: train test split procedure. Image by [Michael Galarnyk](https://twitter.com/GalarnykMichael).\n",
     "\n",
-    "If you would like to learn more about train test split, you can check out my blog post [Understanding Train Test Split](https://towardsdatascience.com/understanding-train-test-split-scikit-learn-python-ea676d5e3d1)."
+    "If you would like to learn more about train test split, you can check out my blog post [Understanding Train Test Split](https://builtin.com/data-science/train-test-split)."
    ]
   },
   {

Statistics/Sample_With_Replacement/SampleWithReplacement.ipynb

@@ -605,13 +605,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Sampling without replacement is used throughout data science. One very common use is in model validation procedures like [train test split](https://towardsdatascience.com/understanding-train-test-split-scikit-learn-python-ea676d5e3d1) and [cross validation](https://scikit-learn.org/stable/modules/cross_validation.html). In short, each of these procedures allows you to simulate how a machine learning model would perform on new/unseen data. \n",
+    "Sampling without replacement is used throughout data science. One very common use is in model validation procedures like [train test split](https://builtin.com/data-science/train-test-split) and [cross validation](https://scikit-learn.org/stable/modules/cross_validation.html). In short, each of these procedures allows you to simulate how a machine learning model would perform on new/unseen data. \n",
     "\n",
     "The image below shows the train test split procedure which consists of splitting a dataset into two pieces: a training set and a testing set. This consists of randomly sampling WITHOUT replacement about 75% (you can vary this) of the rows and putting them into your training set and putting the remaining 25% to your test set. Note that the colors in “Features” and “Target” indicate where their data will go (“X_train”, “X_test”, “y_train”, “y_test”) for a particular train test split.\n",
     "\n",
     "![](images/TrainTestSplit.png)\n",
     "\n",
-    "If you would like to learn more about train test split, you can check out my blog post [Understanding Train Test Split](https://towardsdatascience.com/understanding-train-test-split-scikit-learn-python-ea676d5e3d1)."
+    "If you would like to learn more about train test split, you can check out my blog post [Understanding Train Test Split](https://builtin.com/data-science/train-test-split)."
    ]
   },
   {