Update tp.html

tp.html

@@ -500,7 +500,6 @@
         <span class="badge badge-primary"> 6 Notebooks</span>
         <span class="badge badge-success"> Google Colab</span>
         <span class="badge badge-secondary"> Datasets Kaggle</span>
-        <span class="badge badge-accent">0 Installation</span>
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     </section>
 
@@ -524,7 +523,7 @@
               (âge, sexe, classe, etc.). Le dataset classique pour débuter en ML.
             </p>
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-          <a href="https://colab.research.google.com/drive/1TqBXWkU3XbX7QzvVv9ZqZqZqZqZqZqZq" target="_blank" class="tp-colab-btn">
+          <a href="https://colab.research.google.com/#fileId=https://huggingface.co/spaces/MAALOOUF/Machine_Learning_Training/blob/main/notebooks/TP1_Titanic_Survival.ipynb" target="_blank" class="tp-colab-btn">
             <svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor">
               <path d="M16.9 4.8C14.4 2.4 10.7 1.7 7.5 3.1L10 5.6c1.8-.4 3.8 0 5.2 1.4 2.3 2.3 2.3 6 0 8.3-1.4 1.4-3.4 1.9-5.2 1.5L7.5 19.2c3.2 1.3 6.9.6 9.4-1.8 3.4-3.4 3.4-9.2 0-12.6zm-9.8.8L4.8 8c-.8 1.2-1.2 2.6-1.2 4s.4 2.8 1.2 4l2.3-2.3c-.4-.5-.7-1.1-.8-1.7H8V9.9H6.3c.1-.6.4-1.2.8-1.7z"/>
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@@ -658,7 +657,7 @@
               riche en features avec beaucoup de prétraitement nécessaire.
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-          <a href="https://colab.research.google.com/drive/1TqBXWkU3XbX7QzvVv9ZqZqZqZqZqZqZq" target="_blank" class="tp-colab-btn">
+          <a href="https://colab.research.google.com/#fileId=https://huggingface.co/spaces/MAALOOUF/Machine_Learning_Training/blob/main/notebooks/TP2_House_Prices.ipynb" target="_blank" class="tp-colab-btn">
             <svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor">
               <path d="M16.9 4.8C14.4 2.4 10.7 1.7 7.5 3.1L10 5.6c1.8-.4 3.8 0 5.2 1.4 2.3 2.3 2.3 6 0 8.3-1.4 1.4-3.4 1.9-5.2 1.5L7.5 19.2c3.2 1.3 6.9.6 9.4-1.8 3.4-3.4 3.4-9.2 0-12.6zm-9.8.8L4.8 8c-.8 1.2-1.2 2.6-1.2 4s.4 2.8 1.2 4l2.3-2.3c-.4-.5-.7-1.1-.8-1.7H8V9.9H6.3c.1-.6.4-1.2.8-1.7z"/>
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@@ -792,7 +791,7 @@
               Identifier l'espèce d'iris à partir des mesures des pétales et sépales.
             </p>
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-          <a href="https://colab.research.google.com/drive/1TqBXWkU3XbX7QzvVv9ZqZqZqZqZqZqZq" target="_blank" class="tp-colab-btn">
+          <a href="https://colab.research.google.com/#fileId=https://huggingface.co/spaces/MAALOOUF/Machine_Learning_Training/blob/main/notebooks/TP3_Iris_Classification.ipynb" target="_blank" class="tp-colab-btn">
             <svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor">
               <path d="M16.9 4.8C14.4 2.4 10.7 1.7 7.5 3.1L10 5.6c1.8-.4 3.8 0 5.2 1.4 2.3 2.3 2.3 6 0 8.3-1.4 1.4-3.4 1.9-5.2 1.5L7.5 19.2c3.2 1.3 6.9.6 9.4-1.8 3.4-3.4 3.4-9.2 0-12.6zm-9.8.8L4.8 8c-.8 1.2-1.2 2.6-1.2 4s.4 2.8 1.2 4l2.3-2.3c-.4-.5-.7-1.1-.8-1.7H8V9.9H6.3c.1-.6.4-1.2.8-1.7z"/>
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               temporelles. Introduction aux LSTM et aux prédictions séquentielles.
             </p>
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-          <a href="https://colab.research.google.com/drive/1TqBXWkU3XbX7QzvVv9ZqZqZqZqZqZqZq" target="_blank" class="tp-colab-btn">
+          <a href="https://huggingface.co/spaces/MAALOOUF/Machine_Learning_Training/blob/main/notebooks/TP4_LSTM_TimeSeries.ipynb" target="_blank" class="tp-colab-btn">
             <svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor">
               <path d="M16.9 4.8C14.4 2.4 10.7 1.7 7.5 3.1L10 5.6c1.8-.4 3.8 0 5.2 1.4 2.3 2.3 2.3 6 0 8.3-1.4 1.4-3.4 1.9-5.2 1.5L7.5 19.2c3.2 1.3 6.9.6 9.4-1.8 3.4-3.4 3.4-9.2 0-12.6zm-9.8.8L4.8 8c-.8 1.2-1.2 2.6-1.2 4s.4 2.8 1.2 4l2.3-2.3c-.4-.5-.7-1.1-.8-1.7H8V9.9H6.3c.1-.6.4-1.2.8-1.7z"/>
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