Certified Artificial Intelligence (AI) Practitioner

Solving Business Problems Using AI and ML

• Identify AI and ML Solutions for Business Problems
• Follow a Machine Learning Workflow
• Formulate a Machine Learning Problem
• Select Appropriate Tools

Collecting and Refining the Dataset

• Collect the Dataset
• Analyze the Dataset to Gain Insights
• Use Visualizations to Analyze Data
• Prepare Data

Setting up and Training a Model

• Set Up a Machine Learning Model
• Train the Model

Finalizing a Model

• Translate Results into Business Actions
• Incorporate a Model into a Long-Term Business Solution

Building Linear Regression Models

• Build Regression Models Using Linear Algebra
• Build Regularized Regression Models Using Linear Algebra
• Build Iterative Linear Regression Models

Building Classification Models

• Train Binary Classification Models
• Train Multi-Class Classification Models
• Evaluate Classification Models
• Tune Classification Models

Building Clustering Models

• Build k-Means Clustering Models
• Build Hierarchical Clustering Models

Building Decision Trees and Random Forests

• Build Decision Tree Models
• Build Random Forest Models

Building Support-Vector Machines

• Build SVM Models for Classification
• Build SVM Models for Regression

Building Artificial Neural Networks

• Build Multi-Layer Perceptrons (MLP)
• Build Convolutional Neural Networks (CNN)
• Build Recurrent Neural Networks (RNN)

Promoting Data Privacy and Ethical Practices

• Protect Data Privacy
• Promote Ethical Practices
• Establish Data Privacy and Ethics Policies

Certified Artificial Intelligence (AI) Practitioner Breakdown

• Exam Structure
• Exam Registration Process
• Time Management
• Topics and Concepts
• Certification Question Structure
• Vendor Interpretation Techniques