SVM Resources for AI Tutorials

Supervised Machine Learning is a powerful tool in the field of AI. Support Vector Machines (SVM) are a class of algorithms that work well with high-dimensional data and are suitable for various problems such as classification and regression. Below are some resources to help you understand and implement SVMs.

Books

"An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
- This book provides a comprehensive introduction to statistical learning methods, including SVMs.
- Read More

Online Courses

"Machine Learning" on Coursera by Andrew Ng
- A classic course that covers SVMs and other machine learning algorithms.
- Enroll Now

Tutorials

"Support Vector Machines: An Overview"
- A detailed tutorial that explains the basics of SVMs, including examples and Python code.
- Read Tutorial

Videos

"SVM Explained in 5 Minutes" on YouTube
- A concise and clear explanation of SVMs.
- Watch Video

Example Code

Here is a simple example of how to create an SVM classifier using scikit-learn in Python:

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# Load the dataset
iris = datasets.load_iris()
X, y = iris.data, iris.target

# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create the SVM classifier
clf = SVC(kernel='linear')

# Train the classifier
clf.fit(X_train, y_train)

# Predict the labels
y_pred = clf.predict(X_test)

# Calculate the accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')