Program profile · official sources
Machine Learning with Naïve Bayes
365 Data Science's 'Machine Learning with Naïve Bayes' is an intermediate-level, online, self-paced course taught by Hristina Hristova. The provider lists a headline duration of 2 hours, including 1 hour of lessons and 30 minutes for practice exams. The curriculum introduces the Bayesian approach and Bayes' theorem before transitioning to a practical project using Python's scikit-learn library to build a spam comment detector for a YouTube dataset. Students learn to interpret performance metrics like accuracy, precision, recall, and F1 score. Earning the provider-issued course certificate requires course completion and passing the course exam with a 60 percent or higher grade. The provider lists 2 CPE credits; acceptance depends on individual state boards. Intermediate Python skills are required; basic statistics and linear algebra are recommended.
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
- 2 hours (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.
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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
- Bayes' Theorem
- Setting up the Environment
- Naïve Bayes Algorithm - 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-naive-bayes
- 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
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Change log
- : Initial recorded review of official program details. · Source