Certified Artificial Intelligence (AI) Practitioner (CAIP)

Agenda
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

Topic A: Set Up a Machine Learning Model
Topic B: Train the Model

4 - FINALIZING A MODEL

Topic A: Translate Results into Business Actions
Topic B: Incorporate a Model into a Long-Term Business Solution

5 - BUILDING LINEAR REGRESSION MODELS

Topic A: Build Regression Models Using Linear Algebra
Topic B: Build Regularized Regression Models Using Linear Algebra
Topic C: Build Iterative Linear Regression Models

6 - BUILDING CLASSIFICATION MODELS

Topic A: Train Binary Classification Models
Topic B: Train Multi-Class Classification Models
Topic C: Evaluate Classification Models
Topic D: Tune Classification Models

7 - BUILDING CLUSTERING MODELS

Topic A: Build k-Means Clustering Models
Topic B: Build Hierarchical Clustering Models

8 - BUILDING DECISION TREES AND RANDOM FORESTS

Topic A: Build Decision Tree Models
Topic B: Build Random Forest Models

9 - BUILDING SUPPORT-VECTOR MACHINES

Topic A: Build SVM Models for Classification
Topic B: Build SVM Models for Regression

10 - BUILDING ARTIFICIAL NEURAL NETWORKS

Topic A: Build Multi-Layer Perceptrons (MLP)
Topic B: Build Convolutional Neural Networks (CNN)
Topic C: Build Recurrent Neural Networks (RNN)

11 - PROMOTING DATA PRIVACY AND ETHICAL PRACTICES

Topic A: Protect Data Privacy
Topic B: Promote Ethical Practices
Topic C: Establish Data Privacy and Ethics Policies