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دسته بندی دوره ها

ML for Business professionals using No-Code AI tools

سرفصل های دوره

Master data-driven decisions in business with No-Code AI. Explore Tableau, ML, Deep Learning & ChatGPT for insights.


1. Setting context of the course
  • 1. Course Introduction
  • 2. Modules in the course

  • 2. Art of Taking Decisions with Data
  • 1. Business Decision Exercise
  • 2. Helping Pineapple make Better Decisions.html
  • 3.1 Different Approaches to Take Decisions.zip
  • 3. Different Approaches to Take Decisions
  • 4.1 Calculator.html
  • 4.2 Calculator.html
  • 4.3 Different Approaches to Take Decisions.zip
  • 4. Common Biases in Decision Making and how to address them (Part-1)
  • 5. Common Biases in Decision Making and how to address them (Part-2)
  • 6. Common Biases in Decision Making and how to address them (Part-3)
  • 7. Common Biases in Decision Making and how to address them..html
  • 8.1 Case Study Madani Airlines.zip
  • 8. Case Study Madani Airlines (Part 1)
  • 9. Case Study Madani Airlines (Part 1).html
  • 10. Case Study Madani Airlines (Part 2)
  • 11. Case Study Madani Airlines (Part 2).html
  • 12. Business Decision Exercise Revisited
  • 13. Helping Pineapple Make Better Decisions.html
  • 14. Conclusion

  • 3. Powering Your Decisions with Chart
  • 1.1 CasaElektra.xlsx
  • 1. Introduction to the Module
  • 2.1 Installation guide Tableau Windows and Mac.pptx
  • 2. Why do we need charts
  • 3. Getting Familiar with Tableau
  • 4. Getting Familiar with Tableau.html
  • 5.1 Headers and UnPivot.xlsx
  • 5.2 Product with Null Values.xlsx
  • 5. Basic Data Preparation
  • 6.1 nocodemodule2lesson4-230501-152524.zip
  • 6. Introduction to Tableau with a case study
  • 7.1 Cheat sheet document.pdf
  • 7.2 How to Save and Retrieve Files from Tableau.pdf
  • 7.3 no code.zip
  • 7. Advanced charts in Tableau with a case study
  • 8. Assignment.html

  • 4. Making Predictions with Machine Learning for Future Readiness
  • 1. Why do we make Predictions
  • 2. How do we make Predictions (Part 1)
  • 3. How do we make Predictions (Part 2)
  • 4. How do we make predictions.html
  • 5.1 Predictions.zip
  • 5. How to Evaluate Predictions Root Mean Squared Error
  • 6. Root Mean Squared Error.html
  • 7. How to Evaluate Predictions Accuracy
  • 8. How to Evaluate Predictions Train-Test Split
  • 9. Train-Test Split.html
  • 10. How to Evaluate Predictions Cross Validation
  • 11. Cross Validation.html
  • 12.1 PopularMetrics for Classification and Regression Models.pdf
  • 12. How to Evaluate Predictions Benchmark Performance
  • 13. What is Machine Learning - Introduction
  • 14. What is Machine Learning - Applications of ML
  • 15. Types of Machine Learning - Supervised ML
  • 16. Supervised Machine Learning.html
  • 17. Types of Machine Learning -Unsupervised ML
  • 18. Unsupervised Learning.html

  • 5. Building Machine Learning models using Orange
  • 1. An overview of No-Code tools
  • 2. Getting familiar with Orange
  • 3.1 FurnishEazy NewData.xlsx
  • 3.2 FurnishEazy TrainData.xlsx
  • 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
  • 7.1 KeepSafe NewData.xlsx
  • 7.2 KeepSafe TrainData.xlsx
  • 7. Hands-on Case Study
  • 8.1 TapToBuy NewData (2).csv
  • 8.2 TapToBuy TrainData (2).csv
  • 8.3 Where to use which algorithm.pdf
  • 8. Unsupervised Machine Learning Algorithms
  • 9. Assessment - Machine Learning.html
  • 10.1 Checklist for ML.pdf
  • 10. When not to use ML
  • 11. Building Machine Learning models using Orange.html

  • 6. Advanced AI today and tomorrow
  • 1. Introduction to Deep Learning
  • 2. Introduction to Deep Learning.html
  • 3.1 Brinn Employee Reviews Data csv.zip
  • 3. NLP Hands On in Orange
  • 4. Assessment - NLP.html
  • 5.1 Pizza Topping Images.zip
  • 5. Computer Vision Hands on in Orange
  • 6. Computer Vision.html
  • 7. Generative AI
  • 8. Exploring Simple Applications through Generative AI Tools
  • 9. Ethics in AI
  • 10. Ethics in AI.html
  • 11. Course Conclusion
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    مدرس:
    شناسه: 24196
    حجم: 2721 مگابایت
    مدت زمان: 290 دقیقه
    تاریخ انتشار: 12 آذر 1402
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