Support Vector Machine (SVM) Courses and Certifications

Statistical Learning with Python

Master statistical learning fundamentals through hands-on Python implementation, covering regression, classification, neural networks, and unsupervised methods without heavy mathematics.

• YouTube
• 20 hours 18 minutes
• Self-Paced
• Free Video

Supervised Machine Learning: Classification

469 ratings at Coursera

Train classification models — logistic regression, k-nearest neighbors, support vector machines, decision trees and tree ensembles — and handle unbalanced classes with oversampling and undersampling.

• Coursera
• 6 weeks, 4 hours a week
• Self-Paced
• Paid Course

Trees, SVM and Unsupervised Learning

10 ratings at Coursera

Build predictive models with support vector machines, neural networks, decision trees, bagging and random forests, and generate data representations through PCA and clustering.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Classification Analysis

Build and compare classifiers — KNN, decision trees, SVM, naive Bayes, and logistic regression — and evaluate them with accuracy, precision, recall, F1, and ROC curves.

• Coursera
• 6 weeks, 6 hours a week
• Self-Paced
• Paid Course

Machine Learning and its Applications

51 ratings at Coursera

Build machine learning workflows in MATLAB: prepare data, then train and evaluate support vector machines and artificial neural networks for classification and regression tasks.

• Coursera
• 5 weeks, 4 hours a week
• Self-Paced
• Paid Course

CertNexus

Build Decision Trees, SVMs, and Artificial Neural Networks

15 ratings at Coursera

Build decision trees, random forests, and SVMs for regression and classification, then move to neural networks — multi-layer perceptrons, CNNs, and RNNs — in a capstone project.

• Coursera
• 5 weeks, 4 hours a week
• Self-Paced
• Paid Course

Machine Learning and Data Analytics Part 2

Apply data mining techniques across engineering case studies: Ridge and Lasso regression, K-Means and DBSCAN clustering, Apriori association rules, SVMs, neural networks, text mining, and ARIMA forecasting.

• Coursera
• 7 weeks, 3 hours a week
• Self-Paced
• Paid Course

Statistical Learning for Engineering Part 1

Implement statistical learning algorithms in Python: maximum likelihood estimation, gradient descent, OLS and Lasso/Ridge regression, logistic regression with GLMs, and kernelized support vector machines.

• Coursera
• 7 weeks, 5 hours a week
• Self-Paced
• Paid Course

Packt

Advanced ML Algorithms & Unsupervised Learning

Build and tune Random Forest and SVM classifiers with grid search and cross-validation, reduce dimensions with PCA, segment data using K-Means, and survey deep learning.

• Coursera
• 5 weeks, 2 hours a week
• Self-Paced
• Paid Course

Packt

Data Science - Supervised Machine Learning in Python

Apply supervised machine learning theory in Python through hands-on work with real datasets, model tuning, and deployment as web services.

• Coursera
• 8 weeks, 1 hour a week
• Self-Paced
• Paid Course

Stanford CS229 - Machine Learning I Spring 2022

Master supervised and unsupervised learning, neural networks, reinforcement learning, and statistical pattern recognition through Stanford's comprehensive machine learning curriculum.

• YouTube
• 1 day 1 hour 43 minutes
• Self-Paced
• Free Video

Machine Learning Course

An introductory, theory-focused exploration of machine learning covering generalization, model complexity, neural networks, support vector machines, and kernel methods.

• YouTube
• 1 day
• Self-Paced
• Free Video

Modern Optimization Methods

Study optimality conditions for unconstrained and constrained problems, then apply line search, trust region, conjugate gradient, semismooth Newton and augmented Lagrangian methods to SVM and hypergraph matching.

• XuetangX
• Self-Paced
• Free Online Course

NPTEL;  IISc Bangalore

Neural Networks for Signal Processing - I

Introduces neural networks as optimization-driven directed graphs for signal-processing tasks, from perceptrons and multilayer models through kernels, SVMs, regularization, and PCA.

• Swayam
• 12 weeks
• Available until Oct 9, 2026
• Free Online Course

Machine Learning: Classification

This course covers key supervised machine learning (ML) and classification techniques, including logistic regression, decision trees, ensemble methods, and handling unbalanced datasets. Build and eval

• edX
• 2 weeks, 30-35 hours a week
• Self-Paced
• Free Online Course (Audit)