CertPREP Courseware: IT Specialist Artificial Intelligence - Self-Paced

Training outline:

Lesson 1: Reviewing AI Fundamentals

• Topic A: AI Concepts
• Topic B: Uses for AI
• Topic C: Benefits of AI
• Topic D: Challenges of AI

Lesson 2: Defining the Problem for AI

• Topic A: Machine Learning Workflow
• Topic B: Formulate the Machine Learning Problem
• Topic C: Select AI/ML Tools

Lesson 3: Accessing and Managing Data for AI

• Topic A: Collect and Assess Data
• Topic B: Extract Data
• Topic C: Transform Data
• Topic D: Load Data

Lesson 4: Analyzing Data

• Topic A: Examine Data
• Topic B: Analyze Data Distribution
• Topic C: Visualize Data
• Topic D: Preprocess Data for AI and ML

Lesson 5: Designing a Machine Learning Approach

• Topic A: Identify ML Algorithms
• Topic B: Test a Hypothesis

Lesson 6: Developing Classification Models

• Topic A: Select, Train, and Tune Classification Models
• Topic B: Evaluate Classification Models

Lesson 7: Developing Regression Models

• Topic A: Train Regression Models
• Topic B: Regularize Regression Models
• Topic C: Evaluate Regression Models

Lesson 8: Developing Cluster Models

• Topic A: Train and Tune Cluster Models
• Topic B: Evaluate Cluster Models

Lesson 9: Launching an AI/ML Project

• Topic A: Security and Privacy in AI/ML Projects
• Topic B: Considerations for Ethical Use of AI/ML
• Topic C: Communicate Results

Lesson 10: Deploying and Monitoring an AI/ML Model in Production

• Topic A: Communicate Model Capabilities and Limitations
• Topic B: Deploy and Test Models in Apps
• Topic C: Support and Monitor AI/ML Solutions