AI & Data ScienceIntermediate

SVM: Precision in Classification

Name: SVM: Precision in Classification
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Support Vector Machine (SVM) is a machine learning algorithm used for classification and regression tasks. It works by finding the optimal boundary (hyperplane) that separates data into different classes with maximum margin, ensuring high accuracy and robustness.

3.9

|3h 14m|10 lessons

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What's included

Lessons

3h 14m

Duration

Certificate

What you'll learn

Skills and outcomes you'll walk away with

Module 1: Geometric Foundations of Linear Separability

Module 2: Mathematical Optimization and Hard Margin SVM

Module 3: Soft Margin Classification and Regularization

Module 4: The Kernel Trick and Non-Linear Mapping

Module 5: Hyperparameter Tuning for RBF Kernels

Module 6: Multi-Class SVM Strategies

Course curriculum

10 lessons • 3h 14m total

SVM: Precision in Classification10 lessons

Module 1: Geometric Foundations of Linear Separability

22m

Module 2: Mathematical Optimization and Hard Margin SVM

22m

Module 3: Soft Margin Classification and Regularization

17m

Module 4: The Kernel Trick and Non-Linear Mapping

19m

Module 5: Hyperparameter Tuning for RBF Kernels

19m

Module 6: Multi-Class SVM Strategies

18m

Module 7: Support Vector Regression (SVR)

19m

Module 8: Unsupervised Learning with One-Class SVM

19m

Module 9: The SMO Algorithm and Computational Scaling

18m

Module 10: Real-World Applications and Advanced Architectures

21m

Certification path

Certification exam

36 multiple-choice questions • 70% passing score required

Final Project: Building an SMS Spam Classifier using Naive Bayes

In this final assignment, you will apply the concepts learned throughout the course to build a robust SMS spam detection system. You are required to: 1) Load and preprocess the SMS Spam Collection dataset through cleaning text, removing stop words, and tokenization. 2) Implement a Multinomial Naive Bayes classifier. 3) Split the data into training and testing sets. 4) Evaluate the model using Accuracy, Precision, Recall, and an F1-score. 5) Provide a brief report discussing why Naive Bayes is suitable for this specific task despite the naive assumption of feature independence.

Verified certificate

Earn a verified PDF certificate with unique verification ID upon completion • ₹199