Nirmala Venkat at 07:11, 25 June 2025

2025-06-25T07:11:33Z

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;paddi..."

2025-06-24T10:38:59Z

Created page with " <div style="margin-left:1.27cm;margin-right:0cm;"></div> {| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;paddi..."

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{| border="1"
|-
|| '''Visual Cue'''
|| '''Narration'''
|-
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| <div style="color:#000000;">Show slide:</div>
'''Welcome'''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Welcome to the Spoken Tutorial on '''Support Vector Machine.'''
|-
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Show Slide:

'''Learning Objectives'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | In this tutorial, we will learn about
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Support Vector Machine (SVM)'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Linear SVM '''and</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Non Linear SVM '''</div>

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|| Show Slide:

'''System Requirements'''
|| To record this tutorial, I am using
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Ubuntu Linux OS version 24.04'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Jupyter Notebook IDE'''</div>
|-
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | <div style="color:#000000;">Show Slide:</div>

<div style="color:#000000;">'''Prerequisite'''</div>

| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | To follow this tutorial,
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">The learner must have basic knowledge of </span><span style="color:#000000;">'''Python.'''</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">For prerequisite </span><span style="color:#000000;">'''Python'''</span><span style="color:#000000;"> tutorials, please visit this website.</span></div>
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'''Code files'''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" |
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">The files used in this tutorial are provided in the </span><span style="color:#000000;">'''Code files '''</span><span style="color:#000000;">link.</span></div>
* <div style="color:#252525;margin-left:1.27cm;margin-right:0cm;">Please download and extract the files.</div>
* <div style="color:#252525;margin-left:1.27cm;margin-right:0cm;">Make a copy and then use them while practicing.</div>

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| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Show Slide

'''SVM'''
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* <div style="margin-left:1.27cm;margin-right:0cm;">'''SVM''' is a '''supervised learning algorithm''' used for classification and regression.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">It finds the best boundary, called a '''hyperplane''', to separate classes.</div>
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| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Show Slide

'''Hyperplane and Margin'''

Show margin.png

Narration
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* <div style="margin-left:1.27cm;margin-right:0cm;">The best '''hyperplane''' is the one that leaves the largest gap between classes. </div>
* <div style="margin-left:1.27cm;margin-right:0cm;">This gap is called the '''margin''', and a larger margin reduces errors.</div>
|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Narration
| style="border:0.6pt solid #000000;padding:0.106cm;" | Next we will see about Linear SVM and Non-Linear SVM.
|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Show Slide

'''Linear SVM'''
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* <div style="margin-left:1.27cm;margin-right:0cm;">If a straight line hyperplane can separate the data, we use '''Linear SVM'''.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Linear SVM''' aims to find the hyperplane that maximizes the margin.</div>
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'''Non-Linear SVM'''
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* <div style="margin-left:1.27cm;margin-right:0cm;">When data is not linearly separable, we use '''Non Linear SVM.'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Non Linear SVM uses the '''kernel trick''' to transform the data.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Kernels '''help find decision boundaries for data that isn’t linearly separable.</div>

|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Hover over the files
| style="border:0.6pt solid #000000;padding:0.106cm;" | I have created required files for the demonstration of '''SVM'''.
|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Open the file housingcalifornia.csv and point to the fields as per narration.
| style="border:0.6pt solid #000000;padding:0.106cm;" | To implement the '''SVM model, '''we use the '''californiahousing dot csv '''dataset.

The columns in the dataset helps to classify whether a house price is High or Low.
|-
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Point to the '''SVM.ipynb'''
| style="border-top:0.6pt solid #000000;border-bottom:0.75pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | '''SVM dot ipynb''' is the python notebook file for this demonstration.
|- style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;"
|| Press '''Ctrl,Alt and T''' keys

Type '''conda activate ml'''

Press '''Enter'''
|| Let us open the Linux terminal. Press '''Ctrl, Alt''' and '''T''' keys together.

Activate the machine learning environment as shown
|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Go to the '''Downloads '''folder

Type '''cd Downloads'''

Type '''jupyter notebook'''

Press '''Enter '''
| style="border-top:0.6pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | I have saved my code file in the '''Downloads''' folder.

Please navigate to the directory of your respective code file location.

Then type, '''jupyter space notebook '''and press''' Enter.'''
|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Show Jupyter Notebook Home page:

Click on''' SVM.ipynb'''
| style="border-top:0.6pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | We can see the '''Jupyter Notebook''' '''Home page''' has opened in the web browser.

Click the '''SVM dot ipynb''' file to open it.

<div style="color:#000000;">Note that each cell will have the output displayed in this file.</div>
|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight''' '''the lines

'''import pandas as pd '''
'''import seaborn as sns'''
'''from sklearn.decomposition import PCA'''

Press''' Shift '''and''' Enter'''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | We start by importing the required libraries for '''SVM classification.'''

Now, we will implement a '''Linear SVM''' model.

Make sure to Press''' Shift '''and''' Enter''' to execute the code in each cell.
|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight''' '''the lines:

'''housing_df = pd.read_csv('californiahousing.csv')'''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | First, we '''load the dataset''' from a CSV file.

|-
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'''housing_df.head() '''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Next, we display the first few rows using the '''head function'''.

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|| Highlight the lines:

'''housing_df.shape '''
|| Then, we check the '''dataset’s shape''' to see the number of rows and columns.
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|| Highlight the lines:

'''selected_features = ["MedInc", "HouseAge", "AveRooms", "AveBedrms", "Housing Price"] '''
|| Now, let’s visualize relationships between features using a pair''' plot'''.
|- style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;"
|| Show the output
|| Here is the output displaying feature relationships in the dataset.
|- style="border-top:0.5pt solid #000000;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;"
|| Highlight the lines:

|| Since our data has categories, we use '''Label Encoding''' to convert them.
|-
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight the lines:
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Next, we separate the '''features''' and '''target '''variable for model training.
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'''X'''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Then we print the '''feature set X.'''
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'''y'''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Similarly, we print '''target variable y.'''
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'''X_train, X_test, y_train, y_test ='''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Now, we split the data into '''training''' and '''testing''' '''sets.'''
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'''scaler = MinMaxScaler()'''
'''X_train_scaled ='''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Following this, we apply '''Min Max Scaler''' to keep the data within a fixed range.
|- style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;"
|| Highlight the lines:
|| Now, we train a '''Linear SVM''' model using the training data.

To set up a '''Linear SVM''', we use the''' Linear''' '''kernel'''.

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|| Highlight the lines:

'''y_train_pred_linear = svc_linear.predict(X_train_scaled)'''
|| Once trained, we make predictions on the training data.

|-
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight the lines:
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Now, we check the training '''accuracy''' to evaluate model learning.

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'''y_pred_linear ='''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Next, we predict target values for the test data.

|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight the lines:
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Then, we compare the actual target values with the predicted values.
|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight the lines:
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | We now calculate and display the '''accuracy''' of '''the Linear SVM model.'''

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'''Accuracy: 0.840'''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | We see the accuracy is '''0.84''', indicating strong model performance.

|-
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight the lines:
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Now, we generate a classification report to evaluate model performance.
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|| Highlight the lines:

'''train_sizes, train_scores, test_scores = learning_curve'''

|| Next, we plot a '''learning curve''' to see how accuracy changes with training size.
|-
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Show the output

Hover over training accuracy line and validation accuracy line in the plot.
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | <div style="color:#000000;">The plot shows how '''accuracy changes with different training sizes.'''</div>

<div style="color:#000000;">The blue and red lines show '''training''' and '''validation accuracy''' respectively.</div>

<div style="color:#000000;">The learning curve helps to analyze model performance before further tuning.</div>
|-
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Narration
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Now that '''Linear SVM''' is done, let’s move to '''Non Linear SVM'''.
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'''svc_rbf = SVC(kernel='rbf', C=10,'''

| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | To set up a '''Non Linear SVM''', we use the''' Radial Basis Function kernel'''.

We set the '''regularization parameter C''' to 10 for better separation.

We also use '''class weighting''' to handle '''class imbalance'''.
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'''y_train_pred_rbf = svc_rbf.predict(X_train_scaled) '''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Now, we predict the training labels using the trained '''Non Linear SVM''' model.
|-
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight the lines:
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Next, we calculate and display the '''training accuracy.'''

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|| Highlight the lines:

'''y_pred_rbf = svc_rbf.predict(X_test_scaled)'''
|| Now, we generate predictions on the test data.
|-
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight the lines:

| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Then we compare actual values with predicted values using a Dataframe.
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| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight the lines:
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | We now check the model’s final '''accuracy'''.

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'''Accuracy: 0.840'''
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | With an accuracy of '''84 percent''', the model performs well.
|-
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight the lines:
| style="border-top:none;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Now, let's analyze it further with a '''classification report'''.
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'''pca = PCA(n_components=2) '''
'''X_train_pca = pca.fit_transform(X_train_scaled)'''
'''X_test_pca = pca.transform(X_test_scaled)'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | After evaluating the model, let's visualize how '''SVM''' separates the classes.

We now plot the '''support vectors''', which help define the '''decision boundary'''.
|- style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;"
|| Show the output
|| This plot shows an '''SVM model''' trained with an '''RBF kernel'''.

Each point represents a data sample from the dataset.'''Red '''and '''blue''' colors indicate '''two different target classes'''.Black '''X marks''' represent the '''model's support vectors.'''Support vectors are the key points defining the '''decision boundary.'''

Thus, this is a '''2D visualization''' of an originally 9D dataset.
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|| Show Slide:

'''Summary'''
|| This brings us to the end of the tutorial. Let us summarize.
|-
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Show Slide:
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | In Linear SVM code,
* <div style="margin-left:1.27cm;margin-right:0cm;">Change the '''value of C to''' '''5''' as shown here</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Observe the change in '''accuracy'''.</div>

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|| Show Slide:

'''Assignment Solution '''

Show '''Linear.PNG''' image file
|| After completing the assignment, the output should match the expected result.
|-
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'''FOSSEE Forum'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | For any general or technical questions on '''Python for Machine Learning''', visit the '''FOSSEE forum''' and post your question
|-
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Show Slide:

'''Thank you'''
| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | This is '''Harini Theiveegan''', a FOSSEE Summer Fellow 2025, IIT Bombay signing off

Thanks for joining.
|-
|}

Python-for-Machine-Learning/C3/Support-Vector-Machine/English - Revision history

Madhurig at 16:13, 10 July 2025

Nirmala Venkat at 07:11, 25 June 2025

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;paddi..."

@@ Line 243: / Line 243: @@
 |-
 | style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Narration
-| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Now that '''Linear SVM''' is done, let’s move to '''Non Linear SVM'''.
+| style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:none;border-right:0.6pt solid #000000;padding:0.106cm;" | Let’s move to '''Non Linear SVM'''.
 |-
 | style="border-top:0.5pt solid #000000;border-bottom:0.6pt solid #000000;border-left:0.6pt solid #000000;border-right:0.6pt solid #000000;padding:0.106cm;" | Highlight the lines:

Python-for-Machine-Learning/C3/Support-Vector-Machine/English - Revision history

Madhurig at 16:13, 10 July 2025

Nirmala Venkat at 07:11, 25 June 2025

Nirmala Venkat: Created page with " {| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;paddi..."

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;paddi..."