CertNexus Certified Artificial Intelligence Practitioner Course Overview

Course Prerequisites

RoadMaps

Target Audience for CertNexus Certified Artificial Intelligence Practitioner

The CertNexus Certified AI Practitioner course equips professionals with AI and ML problem-solving skills for business applications.

• Data Scientists
• Machine Learning Engineers
• AI Product Managers
• Business Analysts
• Software Developers interested in AI/ML
• IT Professionals seeking to transition into AI roles
• Data Analysts aiming to advance in machine learning
• Research Scientists and Academics in computer science
• Technical Project Managers overseeing AI/ML projects
• Consultants specializing in AI strategy and implementation
• Entrepreneurs looking to implement AI solutions in their ventures
• Data Engineers who want to collaborate with data scientists effectively
• Professionals in tech roles considering a specialization in artificial intelligence

Learning Objectives - What you will Learn in this CertNexus Certified Artificial Intelligence Practitioner?

Introduction to Learning Outcomes

The CertNexus Certified Artificial Intelligence Practitioner course equips learners with practical AI and ML solutions to address real-world business challenges, ensuring a comprehensive grasp of data handling, model building, and ethical practices.

Learning Objectives and Outcomes

• Gain an understanding of how AI and ML can solve specific business problems and improve operational efficiency.
• Follow and apply a structured machine learning workflow from problem formulation to solution deployment.
• Acquire skills to collect, analyze, and visualize datasets to extract meaningful insights and prepare data for modeling.
• Learn to set up, train, and fine-tune various machine learning models including regression, classification, and clustering models.
• Understand the nuances of building advanced machine learning models such as decision trees and random forests.
• Develop expertise in constructing and training support-vector machines (SVMs) for both classification and regression tasks.
• Gain hands-on experience in creating artificial neural networks (ANNs), including multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs).
• Translate quantitative model results into actionable business strategies and integrate AI solutions into long-term business processes.
• Familiarize with data privacy concerns, promote ethical AI practices, and establish robust data privacy and ethics policies.
• Equip oneself with the ability to evaluate and tune model performance to ensure the models are robust, accurate, and aligned with business objectives.