Program profile · official sources
Machine Learning with Decision Trees and Random Forests
365 Data Science's 'Machine Learning with Decision Trees and Random Forests' is an intermediate-level, online, self-paced course designed for data scientists and machine learning practitioners. Instructed by Nikola Pulev, the one-hour curriculum teaches learners to build and optimize predictive models using decision trees and random forests. Topics include theoretical foundations, Python programming, and practical applications like predicting income from census data. Earning the provider-issued course certificate requires passing the course exam, with general terms specifying a 60% or higher passing grade. The provider lists 3 CPE credits; acceptance depends on the relevant board. Prerequisites include intermediate Python skills, Python 3.8+, Streamlit, an OpenAI API key, and a code editor. Familiarity with basic statistics and linear algebra is helpful.
Facts checked 29 Sep 2026
- Price
- See pricing
Price observation recorded 29 Sep 2026 (not a current price): US observation on 29 September 2026: platform Self-study displays $36/month reference price and $12.50/month billed annually promotional offer; the 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.
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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
- Introduction to Decision Trees and Random Forests
- Setting up the Environment
- Decision Trees
- Random Forests
- 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-decision-trees-and-random-forests
- 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
- Pass the course exam; general certificate terms state 60% or above. 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