Certified Artificial Intelligence (AI) Practitioner

The Certified Artificial Intelligence (AI) Practitioner course prepares participants to develop AI and Machine Learning (ML) solutions for solving business problems. Covering a range of techniques, including regression, classification, clustering, and neural networks, the course equips learners with practical skills to design, deploy, and maintain AI systems. It emphasizes a structured workflow for building AI solutions, making it an essential program for data-driven decision-makers.

This course is ideal for:

Data Scientists and Analysts seeking to enhance their AI and ML skills
Software Developers aiming to create intelligent applications
Business Analysts interested in AI-driven decision-making
IT Professionals exploring AI-based operations and solutions

AI and ML Fundamentals: Understand core concepts and methodologies.
Data Preparation: Learn to clean, preprocess, and engineer data for ML.
Model Development: Build and evaluate models, including regression, clustering, and neural networks.
Deployment and Maintenance: Operationalize AI systems and maintain ML pipelines.

By the end of this course, participants will:

Solve business problems using AI and ML techniques.
Train, evaluate, and tune various machine learning models.
Develop and operationalize AI solutions for production environments.
Maintain AI models with robust monitoring and automation practices.

Module 1: Solving Business Problems Using AI and ML

Identifying AI and ML solutions for specific business challenges
Formulating machine learning problems
Selecting suitable AI/ML approaches

Module 2: Preparing Data

Collecting and transforming data for machine learning
Feature engineering techniques
Handling unstructured data

Module 3: Training, Evaluating, and Tuning Models

Training machine learning models
Evaluating model performance
Fine-tuning models for accuracy and efficiency

Module 4: Building Advanced Models

Linear regression and forecasting models
Classification models: Logistic regression, k-Nearest Neighbor
Clustering models: k-Means and hierarchical clustering

Module 5: Building Decision Trees and Neural Networks

Decision Trees and Random Forests
Support Vector Machines (SVMs) for classification and regression
Artificial Neural Networks (ANN): Multi-Layer Perceptrons, CNNs, and RNNs

Module 6: Operationalizing AI Models

Deploying AI models in production
Automating processes with MLOps
Integrating AI systems with enterprise workflows

Module 7: Maintaining AI Systems

Ensuring data security in pipelines
Monitoring and retraining models
Troubleshooting and improving operational AI systems

–

Participants can pursue the Certified AI Practitioner certification to validate their skills in AI concepts and tools.

Exam Code: AIP-210
Accreditation: ISO/IEC 17024:2012
Certification Outcome: Demonstrates expertise in AI and ML for various job functions.

Participants should have:

Familiarity with data science processes, including data preparation and analysis
Knowledge of statistical concepts, such as probability and summary statistics
Basic programming skills and understanding of data visualization methods