The quick brown fox jumps over the lazy dog.

The quick brown fox jumps over the lazy dog.

1. SOLVING BUSINESS PROBLEMS USING AI AND ML
   • Topic A: Identify AI and ML Solutions for Business Problems
   • Topic B: Follow a Machine Learning Workflow
   • Topic C: Formulate a Machine Learning Problem
   • Topic D: Select Appropriate Tools
2. COLLECTING AND REFINING THE DATASET
   • Topic A: Collect the Dataset
   • Topic B: Analyze the Dataset to Gain Insights
   • Topic C: Use Visualizations to Analyze Data
   • Topic D: Prepare Data
3. SETTING UP AND TRAINING A MODEL
4. Topic A: Set Up a Machine Learning Model
5. Topic B: Train the Model
6. FINALIZING A MODEL
7. Topic A: Translate Results into Business Actions
8. Topic B: Incorporate a Model into a Long-Term Business Solution
9. BUILDING LINEAR REGRESSION MODELS
10. Topic A: Build Regression Models Using Linear Algebra
11. Topic B: Build Regularized Regression Models Using Linear Algebra
12. Topic C: Build Iterative Linear Regression Models
13. BUILDING CLASSIFICATION MODELS
14. Topic A: Train Binary Classification Models
15. Topic B: Train Multi-Class Classification Models
16. Topic C: Evaluate Classification Models
17. Topic D: Tune Classification Models
18. BUILDING CLUSTERING MODELS
19. Topic A: Build k-Means Clustering Models
20. Topic B: Build Hierarchical Clustering Models
21. BUILDING DECISION TREES AND RANDOM FORESTS
22. Topic A: Build Decision Tree Models
23. Topic B: Build Random Forest Models
24. BUILDING SUPPORT-VECTOR MACHINES
25. Topic A: Build SVM Models for Classification
26. Topic B: Build SVM Models for Regression
27. BUILDING ARTIFICIAL NEURAL NETWORKS
28. Topic A: Build Multi-Layer Perceptrons (MLP)
29. Topic B: Build Convolutional Neural Networks (CNN)
30. Topic C: Build Recurrent Neural Networks (RNN)
31. PROMOTING DATA PRIVACY AND ETHICAL PRACTICES
32. Topic A: Protect Data Privacy
33. Topic B: Promote Ethical Practices
34. Topic C: Establish Data Privacy and Ethics Policies