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Certified Artificial Intelligence Practitioner
Certified Artificial Intelligence Practitioner
Certified Artificial Intelligence Practitioner

In this course, participants will implement AI techniques in order to solve business problems.

By the end of this course, participants will:


  • Specify a general approach to solve a given business problem that uses applied AI and ML.
  • Collect and refine a dataset to prepare it for training and testing.
  • Train and tune a machine learning model.
  • Finalize a machine learning model and present the results to the appropriate audience.
  • Build linear regression models.
  • Build classification models.
  • Build clustering models.
  • Build decision trees and random forests.
  • Build support-vector machines (SVMs).
  • Build artificial neural networks (ANNs).
  • Promote data privacy and ethical practices within AI and ML projects.

This course includes hands on activities for each topic area.

Certified Artificial Intelligence Practitioner

Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.

Certified Artificial Intelligence Practitioner

This certification course 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.
Target student maybe a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.
A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming.

Certified Artificial Intelligence Practitioner

To ensure success in this course, participants should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing.

Certified Artificial Intelligence Practitioner

Pass the CAIP Exam


Course structure

Lesson 1: Solving Business Problems Using AI and ML

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

Lesson 2: 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

Lesson 3: Setting Up and Training a Model

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

Lesson 4: Finalizing a Model

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

Lesson 5: Building Linear Regression Models

Topic A: Build a Regression Model Using Linear Algebra
Topic B: Build a Regularized Regression Model Using Linear Algebra
Topic C: Build an Iterative Linear Regression Model

Lesson 6: 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

Lesson 7: Building Clustering Models

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

Lesson 8: Building Advanced Models

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)

Lesson 11: Promoting Data Privacy and Ethical Practices

Topic A: Protect Data Privacy
Topic B: Promote Ethical Practices
Topic C: Establish Data Privacy and Ethics Policies
Appendix A: Mapping Course Content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-100)

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