Certified Artificial Intelligence Practitioner™ (CAIP)

Lesson 1: Solving Business Problems Using AI and ML

• Topic A: Identify AI and ML Solutions for Business Problems
• Topic B: Formulate a Machine Learning Problem
• Topic C: Select Approaches to Machine Learning

Lesson 2: Preparing Data

• Topic A: Collect Data
• Topic B: Transform Data
• Topic C: Engineer Features
• Topic D: Work with Unstructured Data

Lesson 3: Training, Evaluating, and Tuning a Machine Learning Model

• Topic A: Train a Machine Learning Model
• Topic B: Evaluate and Tune a Machine Learning Model

Lesson 4: Building Linear Regression Models

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

Lesson 5: Building Forecasting Models

• Topic A: Build Univariate Time Series Models
• Topic B: Build Multivariate Time Series Models

Lesson 6: Building Classification Models Using Logistic Regression and k-Nearest Neighbor

• Topic A: Train Binary Classification Models Using Logistic Regression
• Topic B: Train Binary Classification Models Using k-Nearest Neighbor
• Topic C: Train Multi-Class Classification Models
• Topic D: Evaluate Classification Models
• Topic E: Tune Classification Models

Lesson 7: Building Clustering Models

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

Lesson 8: Building Decision Trees and Random Forests

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

Lesson 9: Building Support-Vector Machines

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

Lesson 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)

Lesson 11: Operationalizing Machine Learning Models

• Topic A: Deploy Machine Learning Models
• Topic B: Automate the Machine Learning Process with MLOps
• Topic C: Integrate Models into Machine Learning Systems

Lesson 12: Maintaining Machine Learning Operations

• Topic A: Secure Machine Learning Pipelines
• Topic B: Maintain Models in Production
• Appendix A: Mapping Course Content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210)
• Appendix B: Datasets Used in This Course

EXAM CODE: (Exam AIP-210) LAUNCH DATE: March 2023 SUNSET DATE: TBD PASSING SCORE: 60% or 59% depending on exam form NUMBER OF ITEMS: 80 ITEM FORMATS: Multiple Choice/Multiple Response TARGET CANDIDATE This certification exam is designed for practitioners who are seeking to demonstrate a vendor neutral, cross-industry skill set within AI and with a focus on ML that will enable them to design, implement, and hand off an AI solution or environment. EXAM DURATION 120 minutes (including 5 minutes for Candidate Agreement and 5 minutes for Pearson VUE tutorial) EXAM OPTIONS In person at Pearson VUE test centers or online via Pearson OnVUE online proctoring