Program profile · official sources
Machine Learning with Support Vector Machines
365 Data Science's 'Machine Learning with Support Vector Machines' is an intermediate-level, online, self-paced course taught by Elitsa Kaloyanova. The provider lists a headline duration of 1 hour; the curriculum covers the theoretical foundations of hard and soft margin classification, and the intuition behind kernels. Students learn to implement and optimize a Support Vector Classifier using scikit-learn in Python on a dataset classifying mushrooms, applying cross-validation and hyperparameter tuning with GridSearchCV. Earning the provider-issued course certificate requires course completion and passing the course exam, with general terms specifying a 60 percent or higher passing grade. The provider lists 2 CPE credits; acceptance depends on individual state boards. Intermediate Python skills are required; basic statistics and linear algebra are helpful but not mandatory.
Facts checked 1 Oct 2026
- Price
- See pricing
Price observation recorded 1 Oct 2026 (not a current price): As shown in the United States on 1 October 2026, the 365 pricing page Self-study card displays $36/month reference price and a promotional $12.50/month equivalent billed annually; the same pricing page FAQ separately states $29/month full access. USD is explicit in the saved pricing page state. These are platform access quotes, not a course-completion total. Certificates are included with Self-study.
- Time
- 1 hour (provider headline; module and exam timings listed separately)
- Level
- Intermediate
- Credential
- Provider-issued course certificate
Verifiable credential with Credential ID and Credential Link; sharable on LinkedIn/resumes; downloadable upon passing. Certificates are included with the Self-study learning plan.
- Format
- 100% online, self-paced course
- Prerequisites
- Python (version 3.8 or later), Streamlit library, OpenAI API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook). Intermediate Python skills are required. Familiarity with basic statistics and linear algebra is helpful but not mandatory. Advanced preparation: Introduction to Python, Machine Learning in Python.
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
Sources ↗
No accepted numeric price in this registry. See the sourced pricing details below.
See the full registry price landscape. Prices retain their stated payment scope; they are not comparable completion costs.
What’s covered
- Introduction to Support Vector Machines
- Setting up the Environment
- Support Vector Classifier - Practical Example
- Course exam
Prerequisites: Python (version 3.8 or later), Streamlit library, OpenAI API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook). Intermediate Python skills are required. Familiarity with basic statistics and linear algebra is helpful but not mandatory. Advanced preparation: Introduction to Python, Machine Learning in Python.
What you actually do
What the credential is
- Credential type
- Provider-issued course certificate · 365datascience.com / machine-learning-with-support-vector-machines
- Credential
- Course Certificate · 365datascience.com / certificates
- Issuer
- 365 Data Science · 365datascience.com / certificates
- Sharing
- Verifiable credential with Credential ID and Credential Link; sharable on LinkedIn/resumes; downloadable upon passing. Certificates are included with the Self-study learning plan. · 365datascience.com / certificates
- Exam requirement
- Complete the course and pass its course exam with 60% or above under the general certificate terms. Certificates are included with the Self-study learning plan. · 365datascience.com / certificates
Who should skip it
Alternatives in the registry
Related by topic or level.
- Machine Learning in Python : Check official price; 7 hours (provider headline; module and exam timings listed separately).
- Machine Learning with K-Nearest Neighbors : Check official price; 2 hours (provider headline; 1 hour of lessons and 25 minutes of practice exams are listed separately).
- Machine Learning with Naïve Bayes : Check official price; 2 hours (provider headline; module and exam timings listed separately).
Change log
- : Initial recorded review of official program details. · Source