AI+ Engineer™

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Categories AI Engineering, University

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$174.50

Intermediate

AI+ Engineer.

40h

1 Year.

Training 5 or more people?

Innovate Engineering: Leverage AI-Driven Smart Solutions.

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
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+ Engineers 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.

Course Curriculum

Module 1: Foundations of Artificial Intelligence
1.1 Introduction to AI 1.2 Core Concepts and Techniques in AI 1.3 Ethical Considerations

Module 2: Introduction to AI Architecture
2.1 Overview of AI and its Various Applications 2.2 Introduction to AI Architecture 2.3 Understanding the AI Development Lifecycle 2.4 Hands-on: Setting up a Basic AI Environment

Module 3: Fundamentals of Neural Networks
3.1 Basics of Neural Networks 3.2 Activation Functions and Their Role 3.3 Backpropagation and Optimization Algorithms 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Optional Module: AI Agents for Engineering
1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents

The Al+ Engineer certification program offers a structured journey through the foundational principles, advanced techniques, and practical applications of Artificial Intelligence (AI).
Beginning with the Foundations of Al, participants progress through modules covering Al Architecture, Neural Networks, Large Language Models (LLMs), Generative Al, Natural Language Processing (NLP), and Transfer Learning using Hugging Face.
With a focus on hands-on learning, students develop proficiency in crafting sophisticated Graphical User Interfaces (GUIs) tailored for Al solutions and gain insight into Al communication and deployment pipelines.
Upon completion, graduates are equipped with a robust understanding of Al concepts and techniques, ready to tackle real-world challenges and contribute effectively to the ever-evolving field of Artificial Intelligence.

Job Roles & Industry Outlook

Al Solutions Architect

Create scalable Al architectures and integrate Al solutions into various business systems to drive innovation and efficiency.

Machine Learning Engineer

Develop machine learning models and algorithms, focusing on predictive analytics, deep learning, and data-driven solutions.

Al Systems Integrator

Implement Al technologies into existing infrastructures, ensuring seamless integration and scalability of Al solutions.

Al Project Manager

Lead Al-driven projects, managing timelines, resources, and stakeholder expectations to ensure successful deployment of Al solutions.

Al Engineer

Design, develop, and optimize Al systems, working on neural networks, deep learning, and NLP to solve complex challenges.

Exam Information

Duration

90 minutes

Passing Score

70% (35/50)

Format

50 multiple-choice/multiple-response questions

Delivery Method

Online via proctored exam platform (flexible scheduling)

Fees include

One year of access to course content, including continuous updates. Certification exam. One exam re-take.

Exam Blueprint:

Foundations of Artificial Intelligence - 5%

Introduction to AI Architecture - 10%

Fundamentals of Neural Networks - 15%

Applications of Neural Networks - 7%

Significance of Large Language Models (LLM) - 8%

Application of Generative AI - 8%

Natural Language Processing - 15%

Transfer Learning with Hugging Face - 15%

Crafting Sophisticated GUIs for AI Solutions - 10%

AI Communication and Deployment Pipeline - 7%

Explore our Schedules

Apr 12 - Apr 16

GMT 09:00 AM - 05:00 PM

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Riyadh

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5 Days

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Oct 11 - Oct 15

GMT 09:00 AM - 05:00 PM

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Dammam

880.5$

5 Days

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Aug 9 - Aug 13

GMT 09:00 AM - 05:00 PM

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Doha

880.5$

5 Days

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Jul 5 - Jul 9

GMT 09:00 AM - 05:00 PM

In-person

Abu Dhabi

880.5$

5 Days

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Nov 8 - Nov 12

GMT 09:00 AM - 05:00 PM

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Dubai

880.5$

5 Days

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Apr 12 - Apr 16

GMT 09:00 AM - 05:00 PM

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Kuwait

880.5$

5 Days

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Oct 11 - Oct 15

GMT 09:00 AM - 05:00 PM

In-person

Kuwait

880.5$

5 Days

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Aug 9 - Aug 13

GMT 09:00 AM - 05:00 PM

In-person

Muscat

880.5$

5 Days

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Certificate of Completion

AI+ Engineer.

$174.50

Intermediate

AI+ Engineer.

40h

1 Year.

Training 5 or more people?

AI+ Engineer™

Innovate Engineering: Leverage AI-Driven Smart Solutions.

Course Curriculum

Module 1: Foundations of Artificial Intelligence 1.1 Introduction to AI 1.2 Core Concepts and Techniques in AI 1.3 Ethical Considerations

Module 2: Introduction to AI Architecture 2.1 Overview of AI and its Various Applications 2.2 Introduction to AI Architecture 2.3 Understanding the AI Development Lifecycle 2.4 Hands-on: Setting up a Basic AI Environment

Module 3: Fundamentals of Neural Networks 3.1 Basics of Neural Networks 3.2 Activation Functions and Their Role 3.3 Backpropagation and Optimization Algorithms 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Optional Module: AI Agents for Engineering 1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents

Learning Objectives

Resource Center

Target Audience

Job Roles & Industry Outlook

Exam Information

Exam Blueprint:

Explore our Schedules

Certificate of Completion

AI+ Engineer.

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Module 1: Foundations of Artificial Intelligence
1.1 Introduction to AI 1.2 Core Concepts and Techniques in AI 1.3 Ethical Considerations

Module 2: Introduction to AI Architecture
2.1 Overview of AI and its Various Applications 2.2 Introduction to AI Architecture 2.3 Understanding the AI Development Lifecycle 2.4 Hands-on: Setting up a Basic AI Environment

Module 3: Fundamentals of Neural Networks
3.1 Basics of Neural Networks 3.2 Activation Functions and Their Role 3.3 Backpropagation and Optimization Algorithms 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Optional Module: AI Agents for Engineering
1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents