Support Vector Machine (SVM) Courses and Certifications

Hausdorff Center for Mathematics

Non Asymptotic Analysis of L1 Support Vector Machines

Explores non-asymptotic analysis of l1 support vector machines for sparse classification in high-dimensional data, providing error guarantees applicable to real-world scenarios.

• YouTube
• 48 minutes
• Self-Paced
• Free Video

Linear Classifiers in Python

32 ratings at DataCamp

In this course you will learn the details of linear classifiers like logistic regression and SVM.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Supervised Machine Learning in Python

Master the most popular supervised machine learning techniques to begin making predictions with labeled data.

• DataCamp
• 1 day 1 hour
• Self-Paced
• Free Trial Available

Supervised Machine Learning in R

Generate, explore, evaluate, and tune the parameters of different supervised machine learning models.

• DataCamp
• 1 day 1 hour
• Self-Paced
• Free Trial Available

Advanced Machine Learning Models for Prediction

3 ratings

Master advanced machine learning algorithms including SVM, decision trees, random forests, and neural networks for predictive modeling using Scikit-Learn, with focus on practical implementation and optimization.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Cracking Classification

Master essential classification algorithms including logistic regression, decision trees, KNN, Naive Bayes, and SVM. Learn to evaluate model performance and apply techniques to real-world datasets.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Credential

Predictive Modeling with Python

Master predictive modeling in Python through hands-on regression analysis, data preprocessing, and advanced machine learning techniques using the California Housing Dataset.

• CodeSignal
• 13 hours
• Self-Paced
• Free Certificate
• 5 courses

Introduction to Modeling Techniques for Text Classification

Master essential text classification techniques using Naive Bayes, SVM, Decision Trees, Random Forests, and Logistic Regression. Learn preprocessing, model tuning, and evaluation methods for effective NLP implementations.

• CodeSignal
• 6 hours
• Self-Paced
• Free Certificate

Introduction to Machine Learning with caret

Master machine learning fundamentals using R's caret package, covering data preprocessing, model training, evaluation, and cross-validation through hands-on practice with real datasets like iris.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Kaggle;  Amazon Web Services;  Amazon Credential

Introduction to Machine Learning with TensorFlow

5 ratings

Learn supervised and unsupervised machine learning techniques using TensorFlow and Python. Master regression, neural networks, clustering, and more to solve real-world problems and build an image classifier.

• Udacity
• 49 hours
• Self-Paced
• Paid Course

Introduction to Machine Learning in Sports Analytics

27 ratings at Coursera

Explore supervised machine learning techniques using Python and real athletic data to predict sports outcomes, applying methods like SVM, decision trees, and ensembles to analyze professional leagues and wearable device data.

• Coursera
• 12 hours 55 minutes
• Self-Paced
• Paid Course

Introduction to AI for Cybersecurity

97 ratings at Coursera

Gain foundational knowledge of AI in cybersecurity, exploring techniques to enhance security, detect threats, and secure systems through hands-on implementations using tools like Jupyter Notebooks.

• Coursera
• 9 hours 55 minutes
• Self-Paced
• Paid Course

Introduction to AI: Key Concepts and Applications

25 ratings at Coursera

Master essential AI foundations, from R.O.A.D framework to algorithm selection, data quality, and performance metrics. Gain practical insights for evaluating and implementing AI systems while balancing technical depth with strategic decision-making.

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

Machine Learning and Emerging Technologies in Cybersecurity

Master machine learning applications in cybersecurity, from neural networks to intrusion detection systems. Gain hands-on experience with RapidMiner and Security Onion while exploring Tor networking and incident response strategies.

• Coursera
• 1 day 15 hours 8 minutes
• Self-Paced
• Paid Course

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