SVM Course Online

10 Lesson

24 Hours

Add to Wishlist

igmGuru offers comprehensive SVM (Support Vector Machine) training programs designed for learners who want to build strong foundations in machine learning. This course covers all essential SVM concepts, including linear and non-linear classification, regression (SVR), kernel functions, hyperparameter tuning, margin optimization, and model evaluation techniques. Our curriculum is developed by experienced data science and machine learning professionals with over 15 years of industry experience in analytics, AI, and real-world model deployment. Enroll in our SVM course to gain practical, hands-on exposure through real-time datasets, helping you develop strong theoretical understanding and applied machine learning skills.

Enroll Now

SVM Training - Support Vector Machine Course

Overview

Prerequisites

Basic mathematics knowledge (linear algebra, vectors, and matrices)
Understanding of probability and basic statistics
Basic programming knowledge in Python
Familiarity with fundamental machine learning concepts (recommended)

Who Should Enroll

Students and professionals interested in machine learning
Data science and AI aspirants
Software developers and data analysts
Anyone with basic knowledge of Python and ML fundamentals

What Will You Learn

Core concepts of Support Vector Machines (SVM)
Linear and non-linear SVM classification
Kernel functions and kernel selection
Hyperparameter tuning in SVM
Support Vector Regression (SVR)
Multi-class SVM techniques
Model evaluation and performance metrics
Practical SVM implementation in Python

Key Features

1 On 1 Training Option Available
24 X 7 Lifetime Support & Access
Small Batches Upto 10 Participants
Experienced & Professional Trainers
100% Job Assistance
Flexible Schedule

Course Curriculum

Lesson 1 - Introduction to Machine Learning and SVM

1. Supervised learning overview

2. Position of SVM in machine learning

3. Common use cases of SVM

Lesson 2 - Fundamentals of Support Vector Machines

1. Support Vector Machine definition

2. Decision boundary and hyperplane

3. Support vectors

4. Margin and margin maximization

Lesson 3 - Linear Support Vector Machines

1. Linearly separable data

2. Hard margin SVM

3. Soft margin SVM

4. Optimization objective and constraints

Lesson 4 - Non-Linear SVM and Kernel Methods

1. Non-linearly separable data

2. Kernel trick concept

3. Linear kernel

4. Polynomial kernel

5. Radial Basis Function (RBF) kernel

6. Kernel selection considerations

Lesson 5 - Hyperparameters and Model Tuning

1. Regularization parameter (C)

2. Kernel parameters (gamma, degree)

3. Bias–variance trade-off

4. Cross-validation techniques

Lesson 6 - Support Vector Regression (SVR)

1. Regression using SVM

2. Epsilon-insensitive loss function

3. Linear and non-linear SVR

Lesson 7 - Multi-Class SVM Classification

1. One-vs-One strategy

2. One-vs-Rest strategy

3. Multi-class prediction handling

Lesson 8 - Model Evaluation and Validation

1. Training and testing workflow

2. Confusion matrix

3. Accuracy, precision, recall, and F1-score

4. Overfitting and underfitting in SVM

Lesson 9 - Practical Implementation of SVM

1. Data preprocessing for SVM

2. Feature scaling and normalization

3. SVM implementation using Python

4. Model training, testing, and prediction

Lesson 10 - Advanced SVM Concepts

1. One-Class SVM for anomaly detection

2. Sequential Minimal Optimization (SMO)

3. Custom kernel design

Talk To Us

We are happy to help you

1-800-7430-173 (US Toll Free)

+91 7240740740 (India)

Drop Us a Query

Fields marked * are mandatory

Name

Phone Number

Comments

Request For Live Demo Class

Course Fees

Online Class Room Program

US $ 799.00

100% Money Back Guarantee

Duration : 24 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 17 Jun 2026
Weekday Batch 22 Jun 2026
Weekend Batch 20 Jun 2026

Corporate Training

Customized Training Delivery Model
Flexible Training Schedule Options
Industry Experienced Trainers
24x7 Support

Trusted By Top Companies Worldwide

Want to know Today's Offer

SVM Certification

We will provide an industry-recognized Course Completion Certificate to all learners who successfully complete the SVM (Support Vector Machine) Training course. This certificate validates your practical knowledge of machine learning fundamentals, SVM classification and regression techniques, kernel methods, hyperparameter tuning, and real-world model implementation using SVM.

Reviews

Login

Email ID*

Password*

Forgot Password?

SVM Course Online