Certified Artificial Intelligence Practitioner (CAIP)

(AIP-110.AK1) / ISBN : 9781644592243

This course includes

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

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

  • 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

  • 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

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

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

  • 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

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

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

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

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

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

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

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

Hands-on LAB Activities

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

  • 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

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

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

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

  • 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