Build Predictive & Supervised Models
Build Predictive & Supervised Models
This course is part of Statistical Inference & Predictive Modeling Foundations Specialization
Instructor: Professionals in the Industry
Access provided by Vinnytsia National Technical University
Intermediate level
Recommended experience
Flexible schedule
Learn at your own pace
What you'll learn
Successful ML focuses on reliable production systems that deliver sustained business value, not just high model accuracy.
Model performance can degrade quietly, making statistical drift monitoring essential for long-term ML reliability.
Strong feature engineering balances predictive power with interpretability so stakeholders can trust model decisions.
Cross-validation and algorithm comparison ensure models generalize well to new and changing data patterns.
Skills you'll gain
- Applied Machine Learning
- Model Training
- Machine Learning Algorithms
- Predictive Modeling
- Supervised Learning
- Business Metrics
- Continuous Monitoring
- Regression Analysis
- Performance Metric
- Statistical Methods
- MLOps (Machine Learning Operations)
- Feature Engineering
- Data Preprocessing
- Random Forest Algorithm
- Model Evaluation
Tools you'll learn
Details to know
Shareable certificate
Add to your LinkedIn profile
Taught in English
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Build your subject-matter expertise
This course is part of the Statistical Inference & Predictive Modeling Foundations Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
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