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

235,000 تومان

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خرید دانلودی فوری

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تولید کننده: Udemy