diff --git a/.gitignore b/.gitignore
index c3bb071..1737334 100644
--- a/.gitignore
+++ b/.gitignore
@@ -59,6 +59,6 @@ target/
 # IPython
 .ipynb_checkpoints
 notebooks/.ipynb_checkpoints
-
+/.idea
 # Emacs
 *~
\ No newline at end of file
diff --git a/README.md b/README.md
index 9359885..60a87f5 100644
--- a/README.md
+++ b/README.md
@@ -28,12 +28,20 @@ Once this is installed, the following command will install all required packages
 Original install (2015)
 $ conda install numpy scipy matplotlib scikit-learn ipython-notebook seaborn
 
-Or for current versions of Anaconda (Mar 2018)
+For older version Anaconda (Mar 2018)
  
 $ conda create -n skl_tut python=3.4.5 ipywidgets=5.2.2 numpy scipy matplotlib scikit-learn ipython-notebook seaborn pillow
+```
 
-$ activate skl_tut
+**For current (2023) version of the project:** 
+```
+$ conda create -n skl_tut python=3.10.8 --file requirements.txt
+```
 
+```
+$ activate skl_tut
+```
+```
 $ jupyter notebook --notebook-dir='<tutorial folder>'
 ```
 
diff --git a/notebooks/01-Preliminaries.ipynb b/notebooks/01-Preliminaries.ipynb
index 4720b07..a871920 100644
--- a/notebooks/01-Preliminaries.ipynb
+++ b/notebooks/01-Preliminaries.ipynb
@@ -115,9 +115,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPython: 8.10.0\n",
+      "numpy: 1.23.5\n",
+      "scipy: 1.10.0\n",
+      "matplotlib: 3.6.2\n",
+      "scikit-learn: 1.2.1\n",
+      "seaborn: 0.12.2\n"
+     ]
+    }
+   ],
    "source": [
     "from __future__ import print_function\n",
     "\n",
@@ -134,7 +147,10 @@
     "print('matplotlib:', matplotlib.__version__)\n",
     "\n",
     "import sklearn\n",
-    "print('scikit-learn:', sklearn.__version__)"
+    "print('scikit-learn:', sklearn.__version__)\n",
+    "\n",
+    "import seaborn\n",
+    "print('seaborn:', seaborn.__version__)"
    ]
   },
   {
@@ -156,7 +172,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -170,9 +186,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.1-Machine-Learning-Intro.ipynb b/notebooks/02.1-Machine-Learning-Intro.ipynb
index 7a60ca9..0207ff9 100644
--- a/notebooks/02.1-Machine-Learning-Intro.ipynb
+++ b/notebooks/02.1-Machine-Learning-Intro.ipynb
@@ -71,10 +71,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -194,7 +192,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from IPython.core.display import Image, display\n",
+    "from IPython.display import Image, display\n",
     "display(Image(filename='images/iris_setosa.jpg'))\n",
     "print(\"Iris Setosa\\n\")\n",
     "\n",
@@ -422,7 +420,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -436,9 +434,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/02.2-Basic-Principles.ipynb b/notebooks/02.2-Basic-Principles.ipynb
index 00b4298..7690eca 100644
--- a/notebooks/02.2-Basic-Principles.ipynb
+++ b/notebooks/02.2-Basic-Principles.ipynb
@@ -30,10 +30,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -51,7 +50,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.linear_model import LinearRegression"
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.preprocessing import normalize"
    ]
   },
   {
@@ -67,8 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = LinearRegression(normalize=True)\n",
-    "print(model.normalize)"
+    "model = LinearRegression() # normalize=True is no longer an argument, not using it does not seem to affect the result here"
    ]
   },
   {
@@ -471,7 +470,7 @@
    "outputs": [],
    "source": [
     "from sklearn.cluster import KMeans\n",
-    "k_means = KMeans(n_clusters=3, random_state=0) # Fixing the RNG in kmeans\n",
+    "k_means = KMeans(n_clusters=3, random_state=0, n_init='auto') # Fixing the RNG in kmeans\n",
     "k_means.fit(X)\n",
     "y_pred = k_means.predict(X)\n",
     "\n",
@@ -740,7 +739,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "iso = Isomap(n_components=2)\n",
+    "iso = Isomap(n_components=2, n_neighbors=7)\n",
     "data_projected = iso.fit_transform(digits.data)"
    ]
   },
@@ -807,8 +806,16 @@
    "outputs": [],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
-    "clf = LogisticRegression(penalty='l2')\n",
-    "clf.fit(Xtrain, ytrain)\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "scaler = preprocessing.StandardScaler().fit(Xtrain) # Scale data to avoid 'ConvergenceWarning: lbfgs failed to converge (status=1)'\n",
+    "X_scaled = scaler.transform(Xtrain)\n",
+    "\n",
+    "scaler_test = preprocessing.StandardScaler().fit(Xtest)\n",
+    "Xtest = scaler.transform(Xtest)\n",
+    "\n",
+    "clf = LogisticRegression(penalty='l2', max_iter=150) # Increase max_iter from 100 to 150 to avoid 'ConvergenceWarning: lbfgs failed to converge (status=1)'\n",
+    "clf.fit(X_scaled, ytrain)\n",
     "ypred = clf.predict(Xtest)"
    ]
   },
@@ -897,7 +904,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -911,9 +918,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.1-Classification-SVMs.ipynb b/notebooks/03.1-Classification-SVMs.ipynb
index 3027885..79d5e18 100644
--- a/notebooks/03.1-Classification-SVMs.ipynb
+++ b/notebooks/03.1-Classification-SVMs.ipynb
@@ -28,12 +28,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import stats\n",
     "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -58,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.datasets import make_blobs\n",
     "X, y = make_blobs(n_samples=50, centers=2,\n",
     "                  random_state=0, cluster_std=0.60)\n",
     "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring');"
@@ -240,7 +239,7 @@
     "    plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
     "                s=200, facecolors='none')\n",
     "    \n",
-    "interact(plot_svm, N=[10, 200], kernel='linear');"
+    "interact(plot_svm, N=(10, 200), kernel='linear');"
    ]
   },
   {
@@ -266,7 +265,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.datasets.samples_generator import make_circles\n",
+    "from sklearn.datasets import make_circles\n",
     "X, y = make_circles(100, factor=.1, noise=.1)\n",
     "\n",
     "clf = SVC(kernel='linear').fit(X, y)\n",
@@ -355,7 +354,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -369,9 +368,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/03.2-Regression-Forests.ipynb b/notebooks/03.2-Regression-Forests.ipynb
index 9285639..d401e37 100644
--- a/notebooks/03.2-Regression-Forests.ipynb
+++ b/notebooks/03.2-Regression-Forests.ipynb
@@ -28,12 +28,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import stats\n",
     "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -410,7 +409,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -424,9 +423,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.1-Dimensionality-PCA.ipynb b/notebooks/04.1-Dimensionality-PCA.ipynb
index 32d8b2f..770e3fb 100644
--- a/notebooks/04.1-Dimensionality-PCA.ipynb
+++ b/notebooks/04.1-Dimensionality-PCA.ipynb
@@ -26,12 +26,11 @@
    "source": [
     "from __future__ import print_function, division\n",
     "\n",
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import stats\n",
     "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -355,7 +354,7 @@
     "                 size=18)\n",
     "    plt.clim(0, 16)\n",
     "    \n",
-    "interact(plot_digits, n_components=[1, 64], nside=[1, 8]);"
+    "interact(plot_digits, n_components=(1, 64), nside=[1, 8]);"
    ]
   },
   {
@@ -388,7 +387,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -402,9 +401,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.2-Clustering-KMeans.ipynb b/notebooks/04.2-Clustering-KMeans.ipynb
index 70d63d5..c97b53b 100644
--- a/notebooks/04.2-Clustering-KMeans.ipynb
+++ b/notebooks/04.2-Clustering-KMeans.ipynb
@@ -29,12 +29,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import stats\n",
     "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -61,7 +60,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.datasets import make_blobs\n",
     "X, y = make_blobs(n_samples=300, centers=4,\n",
     "                  random_state=0, cluster_std=0.60)\n",
     "plt.scatter(X[:, 0], X[:, 1], s=50);"
@@ -81,7 +80,7 @@
    "outputs": [],
    "source": [
     "from sklearn.cluster import KMeans\n",
-    "est = KMeans(4)  # 4 clusters\n",
+    "est = KMeans(4, n_init='auto')  # 4 clusters\n",
     "est.fit(X)\n",
     "y_kmeans = est.predict(X)\n",
     "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='rainbow');"
@@ -164,7 +163,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "est = KMeans(n_clusters=10)\n",
+    "est = KMeans(n_clusters=10, n_init='auto')\n",
     "clusters = est.fit_predict(digits.data)\n",
     "est.cluster_centers_.shape"
    ]
@@ -208,7 +207,7 @@
     "labels = np.zeros_like(clusters)\n",
     "for i in range(10):\n",
     "    mask = (clusters == i)\n",
-    "    labels[mask] = mode(digits.target[mask])[0]"
+    "    labels[mask] = mode(digits.target[mask], keepdims=True)[0]"
    ]
   },
   {
@@ -373,7 +372,7 @@
     "\n",
     "X = (china / 255.0).reshape(-1, 3)\n",
     "    \n",
-    "model = MiniBatchKMeans(n_colors)\n",
+    "model = MiniBatchKMeans(n_colors, n_init='auto')\n",
     "labels = model.fit_predict(X)\n",
     "colors = model.cluster_centers_\n",
     "new_image = colors[labels].reshape(china.shape)\n",
@@ -400,7 +399,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -414,9 +413,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/04.3-Density-GMM.ipynb b/notebooks/04.3-Density-GMM.ipynb
index bdf354b..b91444e 100644
--- a/notebooks/04.3-Density-GMM.ipynb
+++ b/notebooks/04.3-Density-GMM.ipynb
@@ -29,12 +29,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import stats\n",
     "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -60,7 +59,7 @@
     "x = np.concatenate([np.random.normal(0, 2, 2000),\n",
     "                    np.random.normal(5, 5, 2000),\n",
     "                    np.random.normal(3, 0.5, 600)])\n",
-    "plt.hist(x, 80, normed=True)\n",
+    "plt.hist(x, 80, density=True)\n",
     "plt.xlim(-10, 20);"
    ]
   },
@@ -143,7 +142,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.hist(x, 80, normed=True, alpha=0.3)\n",
+    "plt.hist(x, 80, density=True, alpha=0.3)\n",
     "plt.plot(xpdf, density, '-r')\n",
     "\n",
     "for i in range(clf.n_components):\n",
@@ -342,7 +341,7 @@
    "outputs": [],
    "source": [
     "from sklearn.neighbors import KernelDensity\n",
-    "kde = KernelDensity(0.15).fit(x[:, None])\n",
+    "kde = KernelDensity(bandwidth=0.15).fit(x[:, None])\n",
     "density_kde = np.exp(kde.score_samples(xpdf[:, None]))\n",
     "\n",
     "plt.hist(x, 80, density=True, alpha=0.5)\n",
@@ -369,7 +368,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -383,9 +382,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/05-Validation.ipynb b/notebooks/05-Validation.ipynb
index 5e7c95b..57adb70 100644
--- a/notebooks/05-Validation.ipynb
+++ b/notebooks/05-Validation.ipynb
@@ -29,11 +29,10 @@
    "source": [
     "from __future__ import print_function, division\n",
     "\n",
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -156,7 +155,7 @@
     "knn = KNeighborsClassifier(n_neighbors=1)\n",
     "knn.fit(X_train, y_train)\n",
     "y_pred = knn.predict(X_test)\n",
-    "print(\"{0} / {1} correct\".format(np.sum(y_test == y_pred), len(y_test)))"
+    "print(\"{0} / {1} correct\".format(np.sum(y_test == y_pred), len(y_test))) # Eventually update to use f-strings"
    ]
   },
   {
@@ -505,7 +504,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from IPython.html.widgets import interact\n",
+    "from ipywidgets import interact\n",
     "\n",
     "def plot_fit(degree=1, Npts=50):\n",
     "    X, y = make_data(Npts, error=1)\n",
@@ -520,7 +519,7 @@
     "    plt.ylim(-4, 14)\n",
     "    plt.title(\"mean squared error: {0:.2f}\".format(mean_squared_error(model.predict(X), y)))\n",
     "    \n",
-    "interact(plot_fit, degree=[1, 30], Npts=[2, 100]);"
+    "interact(plot_fit, degree=(1, 30), Npts=(2, 100));"
    ]
   },
   {
@@ -557,8 +556,8 @@
     "    return np.sqrt(np.mean((y - y_pred) ** 2))\n",
     "\n",
     "degree = np.arange(0, 18)\n",
-    "val_train, val_test = validation_curve(PolynomialRegression(), X, y,\n",
-    "                                       'polynomialfeatures__degree', degree, cv=7,\n",
+    "val_train, val_test = validation_curve(estimator=PolynomialRegression(), X=X, y=y,\n",
+    "                                       param_name='polynomialfeatures__degree', param_range=degree, cv=7,\n",
     "                                       scoring=rms_error)"
    ]
   },
@@ -634,8 +633,8 @@
     "\n",
     "def plot_learning_curve(degree=3):\n",
     "    train_sizes = np.linspace(0.05, 1, 120)\n",
-    "    N_train, val_train, val_test = learning_curve(PolynomialRegression(degree),\n",
-    "                                                  X, y, train_sizes, cv=5,\n",
+    "    N_train, val_train, val_test = learning_curve(estimator=PolynomialRegression(degree),\n",
+    "                                                  X=X, y=y, train_sizes=train_sizes, cv=5,\n",
     "                                                  scoring=rms_error)\n",
     "    plot_with_err(N_train, val_train, label='training scores')\n",
     "    plot_with_err(N_train, val_test, label='validation scores')\n",
@@ -739,7 +738,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -753,9 +752,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/Index.ipynb b/notebooks/Index.ipynb
index 831a40e..1dea58d 100644
--- a/notebooks/Index.ipynb
+++ b/notebooks/Index.ipynb
@@ -1,62 +1,74 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:60efb1a2f4e1815d3e1a54160adf3184ab956d9a4487c226bde9b22aa2728f94"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "53788279",
+   "metadata": {},
+   "source": [
+    "# Scikit-Learn Tutorial\n",
+    "\n",
+    "*Jake VanderPlas*\n",
+    "\n",
+    "This is the main index for the materials of my Scikit-Learn tutorial.\n",
+    "Please refer to the [github repository](http://github.com/jakevdp/sklearn_tutorial) for this tutorial for any updates."
+   ]
+  },
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Scikit-Learn Tutorial\n",
-      "\n",
-      "*Jake VanderPlas*\n",
-      "\n",
-      "This is the main index for the materials of my Scikit-Learn tutorial.\n",
-      "Please refer to the [github repository](http://github.com/jakevdp/sklearn_tutorial) for this tutorial for any updates."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Tutorial Notebooks\n",
-      "\n",
-      "The following links are to notebooks containing the tutorial materials.\n",
-      "Note that many of these require files that are in the directory structure of the [github repository](http://github.com/jakevdp/sklearn_tutorial) in which they are contained.\n",
-      "There is not time during the tutorial to cover all of this material, but I left it in in case attendees would like to go deeper on their own.\n",
-      "\n",
-      "### 1. Preliminaries\n",
-      "\n",
-      "  + [01-Preliminaries.ipynb](01-Preliminaries.ipynb)\n",
-      "  \n",
-      "### 2. Introduction to Machine Learning with Scikit-Learn\n",
-      "\n",
-      "  + [02.1-Machine-Learning-Intro.ipynb](02.1-Machine-Learning-Intro.ipynb)\n",
-      "  + [02.2-Basic-Principles.ipynb](02.2-Basic-Principles.ipynb)\n",
-      "  \n",
-      "### 3. Supervised Learning In-Depth\n",
-      "\n",
-      "  + [03.1-Classification-SVMs.ipynb](03.1-Classification-SVMs.ipynb)\n",
-      "  + [03.2-Regression-Forests.ipynb](03.2-Regression-Forests.ipynb)\n",
-      "\n",
-      "### 4. Unsupervised Learning In-Depth\n",
-      "\n",
-      "  + [04.1-Dimensionality-PCA.ipynb](04.1-Dimensionality-PCA.ipynb)\n",
-      "  + [04.2-Clustering-KMeans.ipynb](04.2-Clustering-KMeans.ipynb)\n",
-      "  + [04.3-Density-GMM.ipynb](04.3-Density-GMM.ipynb)\n",
-      "  \n",
-      "### 5. Model Validation In-Depth\n",
-      "\n",
-      "  + [05-Validation.ipynb](05-Validation.ipynb)"
-     ]
-    }
-   ],
-   "metadata": {}
+   "cell_type": "markdown",
+   "id": "322148f6",
+   "metadata": {},
+   "source": [
+    "## Tutorial Notebooks\n",
+    "\n",
+    "The following links are to notebooks containing the tutorial materials.\n",
+    "Note that many of these require files that are in the directory structure of the [github repository](http://github.com/jakevdp/sklearn_tutorial) in which they are contained.\n",
+    "There is not time during the tutorial to cover all of this material, but I left it in in case attendees would like to go deeper on their own.\n",
+    "\n",
+    "### 1. Preliminaries\n",
+    "\n",
+    "  + [01-Preliminaries.ipynb](01-Preliminaries.ipynb)\n",
+    "  \n",
+    "### 2. Introduction to Machine Learning with Scikit-Learn\n",
+    "\n",
+    "  + [02.1-Machine-Learning-Intro.ipynb](02.1-Machine-Learning-Intro.ipynb)\n",
+    "  + [02.2-Basic-Principles.ipynb](02.2-Basic-Principles.ipynb)\n",
+    "  \n",
+    "### 3. Supervised Learning In-Depth\n",
+    "\n",
+    "  + [03.1-Classification-SVMs.ipynb](03.1-Classification-SVMs.ipynb)\n",
+    "  + [03.2-Regression-Forests.ipynb](03.2-Regression-Forests.ipynb)\n",
+    "\n",
+    "### 4. Unsupervised Learning In-Depth\n",
+    "\n",
+    "  + [04.1-Dimensionality-PCA.ipynb](04.1-Dimensionality-PCA.ipynb)\n",
+    "  + [04.2-Clustering-KMeans.ipynb](04.2-Clustering-KMeans.ipynb)\n",
+    "  + [04.3-Density-GMM.ipynb](04.3-Density-GMM.ipynb)\n",
+    "  \n",
+    "### 5. Model Validation In-Depth\n",
+    "\n",
+    "  + [05-Validation.ipynb](05-Validation.ipynb)"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "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": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/URL.ipynb b/notebooks/URL.ipynb
index 23d6a3f..323b02f 100644
--- a/notebooks/URL.ipynb
+++ b/notebooks/URL.ipynb
@@ -1,7 +1,28 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9beb9520",
+   "metadata": {},
+   "source": [
+    "# Scikit-Learn Tutorial\n",
+    "\n",
+    "---\n",
+    "\n",
+    "<br><br><br>\n",
+    "\n",
+    "# Download all materials here:\n",
+    "# http://github.com/jakevdp/sklearn_tutorial\n",
+    "\n",
+    "<br><br><br><br><br><br>\n",
+    "\n",
+    "---"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -15,36 +36,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.3.5"
-  },
-  "name": "",
-  "signature": "sha256:5941485b19c03919025faae3887fd7ef842661eae6780930de41597909742c69"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Scikit-Learn Tutorial\n",
-      "\n",
-      "---\n",
-      "\n",
-      "<br><br><br>\n",
-      "\n",
-      "# Download all materials here:\n",
-      "# http://github.com/jakevdp/sklearn_tutorial\n",
-      "\n",
-      "<br><br><br><br><br><br>\n",
-      "\n",
-      "---"
-     ]
-    }
-   ],
-   "metadata": {}
+   "version": "3.10.8"
   }
- ]
-}
\ No newline at end of file
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/fig_code/figures.py b/notebooks/fig_code/figures.py
index 311ba50..6650194 100644
--- a/notebooks/fig_code/figures.py
+++ b/notebooks/fig_code/figures.py
@@ -119,7 +119,7 @@ def interactive_tree(depth=1):
 def plot_kmeans_interactive(min_clusters=1, max_clusters=6):
     from ipywidgets import interact
     from sklearn.metrics.pairwise import euclidean_distances
-    from sklearn.datasets.samples_generator import make_blobs
+    from sklearn.datasets import make_blobs
 
     with warnings.catch_warnings():
         warnings.filterwarnings('ignore')
diff --git a/notebooks/fig_code/sgd_separator.py b/notebooks/fig_code/sgd_separator.py
index fe9c805..b3bbd38 100644
--- a/notebooks/fig_code/sgd_separator.py
+++ b/notebooks/fig_code/sgd_separator.py
@@ -1,7 +1,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.linear_model import SGDClassifier
-from sklearn.datasets.samples_generator import make_blobs
+from sklearn.datasets import make_blobs
 
 def plot_sgd_separator():
     # we create 50 separable points
diff --git a/requirements.txt b/requirements.txt
index 30f39a8..afb0a85 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
-numpy
-scipy
-matplotlib
-scikit-learn
-ipython
-seaborn
\ No newline at end of file
+numpy==1.23.5
+matplotlib==3.6.2
+scikit-learn==1.2.1
+scipy==1.10.0
+ipython==8.10.0
+seaborn==0.12.2
\ No newline at end of file