Certified Artificial Intelligence (AI) Practitioner (CAIP)

A major transformation in modern business technology has been driven by artificial intelligence (AI) and machine learning (ML). Organizations across industries are increasingly using AI and ML to uncover valuable insights, automate processes, improve decision-making, and develop innovative products and services. However, with the rapid evolution of AI technologies and methodologies, understanding how to effectively apply machine learning to real-world business problems can be challenging.

This course is designed to simplify artificial intelligence and machine learning concepts for professionals who want to build practical, data-driven solutions. It provides a structured, hands-on approach to solving business problems using AI and ML techniques, while introducing industry-standard workflows, tools, and best practices for developing, deploying, and maintaining machine learning systems.

Course Objectives

• Solve business problems using artificial intelligence and machine learning techniques.
• Prepare and transform data for machine learning applications.
• Train, evaluate, and optimize machine learning models.
• Build linear regression models.
• Build forecasting and time series models.
• Build classification models using logistic regression and k-nearest neighbor algorithms.
• Build clustering models for data segmentation and analysis.
• Build classification and regression models using decision trees and random forests.
• Build classification and regression models using support-vector machines (SVMs).
• Build artificial neural networks for deep learning applications.
• Deploy and operationalize machine learning models using automated processes.
• Maintain and secure machine learning pipelines and production models.

Who Should Attend?

This course is designed for professionals interested in applying artificial intelligence and machine learning technologies to business challenges. It is especially beneficial for software developers, data analysts, business analysts, IT professionals, engineers, and technical decision-makers who want to expand their expertise in AI and ML.

The course is ideal for individuals with strengths in software development, applied mathematics and statistics, or business analysis who are looking to build complementary skills in the other areas to effectively develop and implement machine learning solutions.

Course Prerequisites

• Several years of experience working with computing technology.
• Basic aptitude or experience in computer programming.
• Foundational understanding of business and analytical problem-solving concepts.
• General familiarity with data analysis concepts and technical systems.

Course Agenda

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

Inclusion

• Instructor-led training
• Hands-on learning activities and practical exercises
• Real-world machine learning implementation scenarios