Certified Artificial Intelligence Practitioner (CAIP)


This course includes
Mentoring (Add-on)

Gain hands-on experience to pass the CertNexus AIP-110 exam with the Certified Artificial Intelligence Practitioner (CAIP) course and lab. The lab is cloud-based, device-enabled, and can easily be integrated with an LMS. Interactive chapters comprehensively cover the AIP-110 exam objectives and provide understanding on the topics such as problem formulation, applied artificial intelligence, and machine learning in business; data collection, comprehension, cleaning, and engineering; analyze a data set to gain insights, algorithm selection, and model training, model handoff, ethics and oversight; and more.

Here's what you will get

The Certified Artificial Intelligence Practitioner certification exam is designed for professionals seeking to demonstrate a vendor-neutral, cross-industry skillset within AI and with a focus on machine learning to design, implement, and handoff an AI solution or environment. The certification exam will prove a candidate's knowledge of AI concepts, technologies, and tools that will enable them to become a capable AI practitioner in a wide variety of AI-related job functions.


13+ Lessons | 136+ Quizzes | 218+ Flashcards | 221+ Glossary of terms


50+ Pre Assessment Questions | 2+ Full Length Tests | 50+ Post Assessment Questions | 100+ Practice Test Questions

Hand on lab

27+ LiveLab | 00+ Minutes

Here's what you will learn

Download Course Outline

Lessons 1: Introduction

  • Course Description
  • How to use this Course
  • Course-Specific Technical Requirements

Lessons 2: Solving Business Problems Using AI and ML

  • Topic A: Identify AI and ML Solutions for Business Problems
  • Follow a Machine Learning Workflow
  • Topic C: Formulate a Machine Learning Problem
  • Topic D: Select Appropriate Tools
  • Summary

Lessons 3: 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
  • Summary

Lessons 4: Setting Up and Training a Model

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

Lessons 5: Finalizing a Model

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

Lessons 6: 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
  • Summary

Lessons 7: 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
  • Summary

Lessons 8: Building Clustering Models

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

Lessons 9: Building Decision Trees and Random Forests

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

Lessons 10: Building Support-Vector Machines

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

Lessons 11: Building Artificial Neural Networks

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

Lessons 12: Promoting Data Privacy and Ethical Practices

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

Appendix A

  • Mapping Certified Artificial Intelligence (AI) P...oner (Exam AIP-110) Objectives to Course Content

Hands-on LAB Activities

Collecting and Refining the Dataset

  • Examining the Structure of a Machine Learning Dataset
  • Loading the Dataset
  • Exploring the General Structure of the Dataset
  • Analyzing a Dataset Using Statistical Measures
  • Analyzing a Dataset Using Visualizations
  • Splitting the Training and Testing Datasets and Labels

Setting Up and Training a Model

  • Setting Up a Machine Learning Model
  • Dealing with Outliers
  • Scaling and Normalizing Features
  • Refitting and Testing the Model

Building Linear Regression Models

  • Building a Regression Model using Linear Algebra
  • Building a Linear Regression Model to Predict Diabetes Progression
  • Building a Regularized Linear Regression Model
  • Building an Iterative Linear Regression Model

Building Classification Models

  • Creating a Logistic Regression Model to Predict Breast Cancer Recurrence
  • Training Binary Classification Models
  • Training a Multi-Class Classification Model
  • Evaluating a Classification Model
  • Tuning a Classification Model

Building Clustering Models

  • Building a k-Means Clustering Model
  • Building a Clustering Model for Customer Segmentation
  • Building a Hierarchical Clustering Model

Building Decision Trees and Random Forests

  • Building a Decision Tree Model
  • Building a Random Forest Model

Building Support-Vector Machines

  • Building an SVM Model for Classification
  • Building an SVM Model for Regression

Building Artificial Neural Networks

  • Building an MLP

Exam FAQs

There are no formal prerequisites for the certification exam.

No application fee

Multiple Choice/Multiple Response

The exam contains 80 questions.

120 minutes


Any candidates who do not pass a CertNexus certification exam on the first attempt are eligible for one free retake after 30 calendar days from the time they took the initial exam. All CertNexus certification exam vouchers include one free retake. Candidates must purchase another voucher for any subsequent attempts beyond the first free retake.

To be declared