CertNexus

Certified Artificial Intelligence Practitioner (CAIP)

(AIP-110.AK1) / ISBN : 978-1-64459-224-3
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Skills You’ll 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.
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1

Introduction

  • Course Description
  • How to use this Course
  • Course-Specific Technical Requirements
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
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
4

Setting Up and Training a Model

  • Topic A: Set Up a Machine Learning Model
  • Topic B: Train the Model
  • Summary
5

Finalizing a Model

  • Topic A: Translate Results into Business Actions
  • Topic B: Incorporate a Model into a Long-Term Business Solution
  • Summary
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
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
8

Building Clustering Models

  • Topic A: Build k-Means Clustering Models
  • Topic B: Build Hierarchical Clustering Models
  • Summary
9

Building Decision Trees and Random Forests

  • Topic A: Build Decision Tree Models
  • Topic B: Build Random Forest Models
  • Summary
10

Building Support-Vector Machines

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

Appendix A

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

1

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
  • Module 1 Lab
  • Splitting the Training and Testing Datasets and Labels
2

Setting Up and Training a Model

  • Setting Up a Machine Learning Model
  • Dealing with Outliers
  • Scaling and Normalizing Features
  • Module 2 Lab
3

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
4

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
5

Building Clustering Models

  • Building a k-Means Clustering Model
  • Building a Clustering Model for Customer Segmentation
  • Building a Hierarchical Clustering Model
6

Building Decision Trees and Random Forests

  • Building a Decision Tree Model
  • Building a Random Forest Model
7

Building Support-Vector Machines

  • Building an SVM Model for Classification
  • Building an SVM Model for Regression
8

Building Artificial Neural Networks

  • Building an MLP

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There are no formal prerequisites for the certification exam.

No application fee

Pearson VUE

Multiple Choice/Multiple Response

The exam contains 80 questions.

120 minutes

60%

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

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