Model Interpretability Courses and Certifications

Correlation and Regression in R

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

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

Introduction to Predictive Analytics in Python

In this course you'll learn to use and present logistic regression models for making predictions.

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

Explainable AI in Python

Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.

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

Data Science Conference

Evaluating RAG Model Performance Metrics, Bias, and Interpretability

Explore RAG model evaluation techniques covering performance metrics, bias detection, and interpretability to optimize AI-driven search and knowledge retrieval systems.

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

Machine Learning Foundations for Product Managers

824 ratings at Coursera

Build foundational understanding of machine learning, including model development, evaluation, and common algorithms. Gain hands-on experience training and optimizing a model for a real-world problem.

• Coursera
• 16 hours 2 minutes
• Self-Paced
• Paid Course

AI For Medical Treatment

542 ratings at Coursera

In this course, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports.

• Coursera
• 22 hours 4 minutes
• Self-Paced
• Paid Course

Data Modeling and Prediction with R

Master linear and logistic regression in R to build predictive models, interpret relationships, and quantify uncertainty for data-driven decision making.

• Coursera
• 11 hours
• Self-Paced
• Paid Course

Coursera

Interpretable Machine Learning Applications: Part 1

103 ratings at Coursera

Learn to create interpretable machine learning applications using decision trees and random forests. Gain insights into feature importance and model behavior for more accountable and trusted ML applications.

• Coursera
• 1 hour 40 minutes
• Self-Paced
• Paid Course

Coursera

Interpretable Machine Learning Applications: Part 2

29 ratings at Coursera

Develop interpretable machine learning applications using LIME to explain individual predictions, enhancing model transparency and accountability for data scientists and decision-makers.

• Coursera
• 1 hour 55 minutes
• Self-Paced
• Paid Course

EDUCBA

SPSS: Apply & Interpret Logistic Regression Models

13 ratings at Coursera

Master logistic regression in SPSS through hands-on analysis of real datasets like heart pulse and smoking behavior, building predictive models and interpreting statistical outputs.

• Coursera
• 4 hours 59 minutes
• Self-Paced
• Paid Course

Coursera

Building and Optimizing AI Agent Workflows

Master autonomous AI agent design with reinforcement learning, ethical frameworks, modular architectures, and optimization techniques for production-ready systems.

• Coursera
• 17 hours 14 minutes
• Self-Paced
• Paid Course

Coursera Career Certificate

Machine Learning Made Easy for Software Engineers

Master the full ML lifecycle—from training and optimizing models to deploying production-ready microservices—with hands-on projects covering pipelines, monitoring, and debugging.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 4 courses

Coursera

Engineer & Explain AI Model Decisions

Discover how to build trustworthy AI systems by mastering feature engineering, model interpretability, and bias detection for ethical deployment in production environments.

• Coursera
• 3 hours 47 minutes
• Self-Paced
• Paid Course

H2O.ai

H2O Driverless AI Starter Course

Master automated machine learning with H2O Driverless AI through hands-on training in data preprocessing, model development, and advanced features like custom recipe creation and hyperparameter tuning.

• Coursera
• 2 hours 29 minutes
• Self-Paced
• Paid Course

Coursera

Engineer AI Models: Explain, Tune & Experiment

Master AI model engineering through feature tuning, explainability methods like SHAP/LIME, and structured experimentation to transform black box models into trusted, business-ready solutions.

• Coursera
• 2 hours 21 minutes
• Self-Paced
• Paid Course