Certified Artificial Intelligence (AI) Practitioner (CAIP)

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. This course includes hands on activities for each topic area.

Course Details

Price:

$3,495.00

Days:

Location:

Course Overview

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.

A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) certification.

1 – Solving Business Problems Using AI and ML

Identify AI and ML Solutions for Business Problems
Formulate a Machine Learning Problem
Select Approaches to Machine Learning

2 – Preparing Data

Collect Data
Transform Data
Engineer Features
Work with Unstructured Data

3 – Training, Evaluating, and Tuning a Machine Learning Model

Train a Machine Learning Model
Evaluate and Tune a Machine Learning Model

4 – Building Linear Regression Models

Build Regression Models Using Linear Algebra
Build Regularized Linear Regression Models
Build Iterative Linear Regression Models

5 – Building Forecasting Models

Build Univariate Time Series Models
Build Multivariate Time Series Models

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

Train Binary Classification Models Using Logistic Regression
Train Binary Classification Models Using k-Nearest Neighbor
Train Multi-Class Classification Models
Evaluate Classification Models
Tune Classification Models

7 – Building Clustering Models

Build k-Means Clustering Models
Build Hierarchical Clustering Models

8 – Building Decision Trees and Random Forests

Build Decision Tree Models
Build Random Forest Models

9 – Building Support-Vector Machines

Build SVM Models for Classification
Build SVM Models for Regression

10 – Building Artificial Neural Networks

Build Multi-Layer Perceptrons (MLP)
Build Convolutional Neural Networks (CNN)
Build Recurrent Neural Networks (RNN)

11 – Operationalizing Machine Learning Models

Deploy Machine Learning Models
Automate the Machine Learning Process with MLOps
Integrate Models into Machine Learning Systems

12 – Maintaining Machine Learning Operations

Secure Machine Learning Pipelines
Maintain Models in Production

Class Dates & Times


06/15/2026 - 06/19/2026 Virtual 09:00:00 to 17:00:00 EST Enroll Now $3,495.00
08/17/2026 - 08/21/2026 Virtual 09:00:00 to 17:00:00 EST Enroll Now $3,495.00
12/14/2026 - 12/18/2026 Virtual 09:00:00 to 17:00:00 EST Enroll Now $3,495.00

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