No Code Predictive Analytics with Orange
BeginnerLevel
800+Students Enrolled
1 HrDuration
4.5Average Rating
About this Course
- This beginner-friendly course introduces business professionals to the fundamentals of machine learning through real-world use cases—no coding required.
- Learn to evaluate AI models using metrics like accuracy, RMSE, and cross-validation, ensuring your machine learning insights are both reliable and actionable.
- Explore no-code ML workflows using Orange to tackle real business challenges through regression, classification, and clustering in a practical, hands-on manner.
Learning Outcomes
ML Basics & Business Use
Understand ML concepts and how predictions aid business decisions
Model Evaluation Skills
Learn key metrics like accuracy, RMSE, and cross-validation methods
Hands-on with Orange
Apply ML techniques to real problems using no-code Orange workflows
Course Curriculum
Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.
1. Why do we make Predictions?
2. How do we make Predictions? (Part 1)
3. How do we make Predictions? (Part 2)
4. How to Evaluate Predictions: Root Mean Squared Error
5. How to Evaluate Predictions: Accuracy
6. How to Evaluate Predictions: Train-Test Split
7. How to Evaluate Predictions: Cross Validation
8. How to Evaluate Predictions: Benchmark Performance
9. What is Machine Learning - Introduction
10. What is Machine Learning - Applications of ML
11. Types of Machine Learning - Supervised ML
12. Types of Machine Learning -Unsupervised ML
1. An overview of No-Code tools
2. Getting familiar with Orange
3. ML workflow through Orange using a Case Study (Part-1)
4. ML workflow through Orange using a Case Study (Part-2)
5. Regression Algorithm
6. Classification Algorithms
7. Hands-on Case Study
8. Unsupervised Machine Learning Algorithms
9. When not to use ML
Get this Course Now
With this course you’ll get
- 1 Hour
Duration
- Apoorv Vishnoi
Instructor
- Beginner
Level
Certificate of completion
Earn a professional certificate upon course completion
- Globally recognized certificate
- Verifiable online credential
- Enhances professional credibility
Frequently Asked Questions
Looking for answers to other questions?
RMSE measures the difference between predicted and actual values. It’s used to evaluate the accuracy of regression models.
Supervised learning uses labelled data to make predictions, while unsupervised learning identifies hidden patterns in unlabeled data.
Prediction helps businesses anticipate trends, make data-driven decisions, and reduce uncertainty in planning and operations.
The course uses Orange, a beginner-friendly, drag-and-drop-based platform to build and visualise machine learning workflows.
Yes, you will receive a certificate of completion after successfully finishing the course and assessments.
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