What you'll Learn
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Grasp the core concepts behind machine learning, its types, and why predictions matter in business contexts.
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Learn essential evaluation techniques like accuracy, RMSE, and cross-validation to judge the effectiveness of AI models.
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Apply machine learning workflows using Orange to solve business problems through regression, classification, and clustering.
Who Should Enroll
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Aspiring Students: Beginners and non-technical teams looking to build machine learning models using simple, no-code tools.
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Professionals: Business professionals and managers seeking to apply AI in decision-making without coding.
About the Instructor
Apoorv Vishnoi Head Training Vertical, 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.
Course curriculum
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1
Making Predictions with Machine Learning for Future Readiness
- Lesson 1 - Why do we make Predictions?
- Lesson 2 - How do we make Predictions? (Part 1)
- Lesson 2 - How do we make Predictions? (Part 2)
- Quiz - How do we make predictions
- Lesson 3 - How to Evaluate Predictions: Root Mean Squared Error
- Quiz - Root Mean Squared Error
- Lesson 3 - How to Evaluate Predictions: Accuracy
- Lesson 3 - How to Evaluate Predictions: Train-Test Split
- Quiz - Train-Test Split
- Lesson 3 - How to Evaluate Predictions: Cross Validation
- Quiz - Cross Validation
- Lesson 3 - How to Evaluate Predictions: Benchmark Performance
- Lesson 4 - What is Machine Learning - Introduction
- Lesson 4 - What is Machine Learning - Applications of ML
- Lesson 5 - Types of Machine Learning - Supervised ML
- Quiz : Supervised Machine Learning
- Lesson 5 - Types of Machine Learning -Unsupervised ML
- Quiz: Unsupervised Learning
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2
Building Machine Learning models using Orange
- Lesson 1 - An overview of No-Code tools
- Lesson 2 - Getting familiar with Orange
- Lesson 3 - ML workflow through Orange using a Case Study (Part-1)
- Lesson 3 - ML workflow through Orange using a Case Study (Part-2)
- Lesson 4 - Regression Algorithm
- Lesson 5 - Classification Algorithms
- Lesson 6 - Hands-on Case Study
- Lesson 7 - Unsupervised Machine Learning Algorithms
- Lesson 8 - When not to use ML
- Quiz - Building Machine Learning models using Orange