No Code Predictive Analytics with Orange

  • BeginnerLevel

  • 427+Students Enrolled

  • 1 HrDuration

  • 4.5Average Rating

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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 predictive analysis, 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

Who Should Enroll

  • Aspiring Students eager to understand machine learning fundamentals without diving into code.
  • Professionals : Working professionals aiming to apply AI in business without a technical background.
  • Anyone curious about machine learning and looking for a hands-on, no-code introduction to its concepts

Course Curriculum

Learn no-code predictive analytics with Orange—build ML models, explore workflows, and apply algorithms through real-world case studies.

tools

  1. 1. Why do we make Predictions?

  2. 2. How do we make Predictions? (Part 1)

  3. 3. How do we make Predictions? (Part 2)

  4. 4. How to Evaluate Predictions: Root Mean Squared Error

  5. 5. How to Evaluate Predictions: Accuracy

  6. 6. How to Evaluate Predictions: Train-Test Split

  7. 7. How to Evaluate Predictions: Cross Validation

  8. 8. How to Evaluate Predictions: Benchmark Performance

  9. 9. What is Machine Learning - Introduction

  10. 10. What is Machine Learning - Applications of ML

  11. 11. Types of Machine Learning - Supervised ML

  12. 12. Types of Machine Learning -Unsupervised ML

  1. 1. An overview of No-Code tools

  2. 2. Getting familiar with Orange

  3. 3. ML workflow through Orange using a Case Study (Part-1)

  4. 4. ML workflow through Orange using a Case Study (Part-2)

  5. 5. Regression Algorithm

  6. 6. Classification Algorithms

  7. 7. Hands-on Case Study

  8. 8. Unsupervised Machine Learning Algorithms

  9. 9. When not to use ML

Meet the instructor

Our instructor and mentors carry years of experience in data industry

company logo
Apoorv Vishnoi

Training Head, Analytics Vidhya

Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.

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

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

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.

Business professionals and beginners who want practical ML without coding, using predictive analytics to solve real problems.

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