AI CERTS® certification

AI+ Engineer Practitioner™

Formerly known as AI+ Engineer™ Innovate Engineering: Leverage AI-Driven Smart Solutions

Enquire about this certificationCourse code: AT-330

AI+ Engineer Practitioner™ certification badge

Course overview

Review the course before deciding whether it is suitable.

Full AI Stack: Learn AI architecture, LLMs, NLP, and neural networks Tool Proficiency: Includes Transfer Learning with Hugging Face and GUI design Deployment Focus: Build real AI systems and manage communication pipelines Practical Mastery: Gain the skills to engineer scalable AI solutions for innovation

Included: Instructor-led OR Self-paced course + Official exam + Digital badge
Duration: Instructor-Led: 5 days (live or virtual)Self-Paced: 40 hours of content
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Prerequisites: AI+ Data Practitioner™ or AI+ Developer Practitioner™ course should be completed, basic math, computer science fundamentals, Python familiarity

Course focus

Why this certification may be relevant.

Master AI System Design:

Develop the skills to design, implement, and optimize advanced AI systems for real-world applications.

Build Scalable AI Solutions:

Learn how to create scalable AI solutions for industries like technology, finance, and healthcare.

Tackle Complex Engineering Challenges:

This certification ensures you’re equipped to solve challenges in AI architecture, neural networks, and NLP.

Contribute to AI-Driven Innovations:

Certified AI+ Engineer Practitioner™ develop cutting-edge AI solutions that enhance business operations and drive future innovations.

Advance Your Career in AI Engineering:

As demand for skilled AI engineers rises, this certification offers a competitive advantage in the job market.

Who it may suit

Check the intended audience against your experience and goals.

AI & Software Engineers: Enhance your development skills by mastering AI techniques and designing advanced AI systems.
Machine Learning Enthusiasts: Apply deep learning, neural networks, and NLP techniques to real-world AI challenges.
Data Scientists: Strengthen your AI toolkit with engineering techniques for building and deploying scalable AI solutions.
IT Specialists & System Architects: Integrate AI solutions into existing infrastructures, optimizing performance and scalability.
Students & New Graduates: Develop in-demand AI engineering skills and prepare for a successful career in the rapidly growing AI field.

Course content

Modules supplied by AI CERTs.

Open each module to review the stated topics.

01Course Overview

Course Introduction Preview

02Module 1: Foundations of Artificial Intelligence

1.1 Introduction to AI Preview
1.2 Core Concepts and Techniques in AI Preview
1.3 Ethical Considerations

03Module 2: Introduction to AI Architecture

2.1 Overview of AI and its Various Applications Preview
2.2 Introduction to AI Architecture Preview
2.3 Understanding the AI Development Lifecycle Preview
2.4 Hands-on: Setting up a Basic AI Environment

04Module 3: Fundamentals of Neural Networks

3.1 Basics of Neural Networks Preview
3.2 Activation Functions and Their Role Preview
3.3 Backpropagation and Optimization Algorithms
3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework

05Module 4: Applications of Neural Networks

4.1 Introduction to Neural Networks in Image Processing
4.2 Neural Networks for Sequential Data
4.3 Practical Implementation of Neural Networks

06Module 5: Significance of Large Language Models (LLM)

5.1 Exploring Large Language Models
5.2 Popular Large Language Models
5.3 Practical Finetuning of Language Models
5.4 Hands-on: Practical Finetuning for Text Classification

07Module 6: Application of Generative AI

6.1 Introduction to Generative Adversarial Networks (GANs)
6.2 Applications of Variational Autoencoders (VAEs)
6.3 Generating Realistic Data Using Generative Models
6.4 Hands-on: Implementing Generative Models for Image Synthesis

08Module 7: Natural Language Processing

7.1 NLP in Real-world Scenarios
7.2 Attention Mechanisms and Practical Use of Transformers
7.3 In-depth Understanding of BERT for Practical NLP Tasks
7.4 Hands-on: Building Practical NLP Pipelines with Pretrained Models

09Module 8: Transfer Learning with Hugging Face

8.1 Overview of Transfer Learning in AI
8.2 Transfer Learning Strategies and Techniques
8.3 Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks

10Module 9: Crafting Sophisticated GUIs for AI Solutions

9.1 Overview of GUI-based AI Applications
9.2 Web-based Framework
9.3 Desktop Application Framework

11Module 10: AI Communication and Deployment Pipeline

10.1 Communicating AI Results Effectively to Non-Technical Stakeholders
10.2 Building a Deployment Pipeline for AI Models
10.3 Developing Prototypes Based on Client Requirements
10.4 Hands-on: Deployment

12Optional Module: AI Agents for Engineering

1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents

Tools referenced by AI CERTs

Platforms and tools included in the course information.

TensorFlow

Hugging Face Transformers

Jenkins

TensorFlow Hub

Before you enrol

This course information is supplied through the AI CERTs catalogue. Kiwi Fern Education will confirm current availability, delivery arrangements, inclusions and fees before enrolment. The programme is not presented as a New Zealand Qualifications Authority qualification.

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