Support Vector Machine (SVM) Courses and Certifications

Coursera

Scikit-Learn For Machine Learning Classification Problems

19 ratings at Coursera

Learn to build and train classifier models using Scikit-Learn, exploring algorithms for classification, regression, and clustering, including support vector machines, random forests, and k-means.

• Coursera
• 1 hour 48 minutes
• Self-Paced
• Paid Course

CertNexus

Build Decision Trees, SVMs, and Artificial Neural Networks

14 ratings at Coursera

Learn to build decision trees, SVMs, and neural networks for machine learning and deep learning. Gain practical skills in constructing models to solve regression, classification, computer vision, and natural language processing problems.

• Coursera
• 21 hours 54 minutes
• Self-Paced
• Paid Course

Machine Learning Classification Bootcamp in Python

1059 ratings at Udemy

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn

• Udemy
• 11 hours 44 minutes
• Self-Paced
• Paid Course

Coursera

Scikit-Learn to Solve Regression Machine Learning Problems

11 ratings at Coursera

Learn to build and train regression models using Scikit-Learn, exploring algorithms like support vector machines, random forests, and gradient boosting. Gain practical skills applicable to various industries.

• Coursera
• 1 hour 56 minutes
• Self-Paced
• Paid Course

Packt

Machine Learning, Data Science and Generative AI with Python

5 ratings at Coursera

Master machine learning, data science, and generative AI using Python. Build models with TensorFlow, work with big data using Spark, and deploy real-time AI systems ethically.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 3 courses

Machine Learning Algorithms

Explore key machine learning algorithms: Naïve Bayes, Support Vector Machine, Decision Tree, and Clustering. Gain practical understanding and implementation skills in Python.

• Coursera
• 15 hours 56 minutes
• Self-Paced
• Paid Course

Statistical Learning

Explore cutting-edge statistical analysis techniques, from regression to advanced methods like kernel smoothing and support vector machines. Enhance data interpretation skills with hands-on Python examples.

• Coursera
• 4 days 18 hours 15 minutes
• Self-Paced
• Paid Course

Machine Learning for Engineers: Algorithms and Applications

Master practical machine learning algorithms through hands-on implementation in Python and PyTorch, covering supervised and unsupervised learning techniques for real-world applications in computer vision, NLP, and robotics.

• Coursera
• 17 hours 31 minutes
• Self-Paced
• Paid Course

Statistical Learning for Engineering Part 1

Explore supervised and unsupervised machine learning algorithms, from linear regression to SVMs, with hands-on Python implementation for real-world engineering applications.

• Coursera
• 1 day 11 hours 38 minutes
• Self-Paced
• Paid Course

Techniques d’intelligence artificielle : des fondements aux applications

Explorez les fondements de l'apprentissage automatique supervisé, des algorithmes de base aux réseaux de neurones profonds, avec des applications pratiques dans divers domaines.

• edX
• 6 weeks, 5-6 hours/week
• Self-Paced
• Free Online Course (Audit)

Statistical Learning for Engineering Part 2

Explore advanced machine learning algorithms including neural networks, clustering, dimensionality reduction, and reinforcement learning with hands-on Python implementation.

• Coursera
• 1 day 46 minutes
• Self-Paced
• Paid Course

Predictive Analytics

Discover how to transform enterprise data into actionable business intelligence using decision trees, neural networks, market basket analysis, and discriminant analysis tools.

• Coursera
• 2 days 7 hours 46 minutes
• Self-Paced
• Paid Course

Corporate Finance Institute

Classification - Fundamentals & Practical Applications

Master classification algorithms from logistic regression to SVM, implementing them in Excel and Python while learning to evaluate model performance and interpret results for business decision-making.

• Coursera
• 2 hours 58 minutes
• Self-Paced
• Paid Course

Packt

Intermediate Data Manipulation and Machine Learning

Explore AI fundamentals, regression analysis, model preparation, classification techniques, and advanced machine learning concepts through interactive labs and real-world applications.

• Coursera
• 13 hours 16 minutes
• Self-Paced
• Paid Course

Packt

Advanced ML Algorithms & Unsupervised Learning

Master advanced machine learning techniques including Random Forest, SVM, PCA, K-Means clustering, and deep learning fundamentals to build more powerful predictive models and uncover patterns in unlabeled data.

• Coursera
• 8 hours 43 minutes
• Self-Paced
• Paid Course