×
Login Register
Book a Discovery Call
×
Login Register

AI+ Engineer™

  • 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
Enroll Now
AI+ Engineer™
Self-Paced Online
USD $ 540.00
Instructor-Led Online
USD $ 4250.00

At a Glance: Course + Exam Overview

Our training approach is human‑centred and outcomes‑driven. We focus on what learners can apply confidently.

Category
AI Development
Robotics Engineer
Quantum Computing Specialist
Machine Learning Scientist
Language
English
Data Science & AI Specialist
Data Engineer
Cloud Architect
All Courses
AI System Engineer
AI Agent Developer
AI Technical
Software Developer
Program Name
AI+ Engineer™
Duration
  • Instructor‑Led: 5 Days
  • Self‑Paced: 40 hours of content
Prerequisites
    • AI+ Data™  or AI+ Developer™ course should be completed. 
    • Basic understanding of Python programming is mandatory for hands-on exercises and project work. 
    • Familiarity with high school-level algebra and basic statistics is required. 
    • Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential. 
Exam Format
Exam details not available.

What You'll Learn

GUI Develop for AI Solutions

Students will learn to develop user-friendly AI GUIs. Interface design, usability testing, and AI integration into GUIs will be covered to build intuitive and engaging user experiences.

AI Communication and Deployment Pipeline

Learners will gain knowledge of AI solution communication and deployment, including developing and managing deployment pipelines for efficient AI system rollout and maintenance, as well as explaining the value and utility of AI solutions to stakeholders and end-users.

AI Problem-Solving

Students will apply AI principles from the course to real-world issues, enhancing their skills in identifying AI methodologies, constructing models, and interpreting results to address complex problems across disciplines.

AI-Specific Project Management

Learners will build AI-specific project management abilities by engaging with AI project workflows. This involves developing, implementing, and managing AI initiatives, managing resources, schedules, and stakeholder expectations for success.

Certification Modules

Course Overview

  1. Course Introduction Preview

Module 1: Foundations of Artificial Intelligence

  1. 1.1 Introduction to AI Preview
  2. 1.2 Core Concepts and Techniques in AI Preview
  3. 1.3 Ethical Considerations

Module 2: Introduction to AI Architecture

  1. 2.1 Overview of AI and its Various ApplicationsPreview
  2. 2.2 Introduction to AI Architecture Preview
  3. 2.3 Understanding the AI Development Lifecycle Preview
  4. 2.4 Hands-on: Setting up a Basic AI Environment

Module 3: Fundamentals of Neural Networks

  1. 3.1 Basics of Neural Networks Preview
  2. 3.2 Activation Functions and Their Role Preview
  3. 3.3 Backpropagation and Optimization Algorithms
  4. 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework

Module 4: Applications of Neural Networks

  1. 4.1 Introduction to Neural Networks in Image Processing
  2. 4.2 Neural Networks for Sequential Data
  3. 4.3 Practical Implementation of Neural Networks

Module 5: Significance of Large Language Models (LLM)

  1. 5.1 Exploring Large Language Models
  2. 5.2 Popular Large Language Models
  3. 5.3 Practical Finetuning of Language Models
  4. 5.4 Hands-on: Practical Finetuning for Text Classification

Module 6: Application of Generative AI

  1. 6.1 Introduction to Generative Adversarial Networks (GANs)
  2. 6.2 Applications of Variational Autoencoders (VAEs)
  3. 6.3 Generating Realistic Data Using Generative Models
  4. 6.4 Hands-on: Implementing Generative Models for Image Synthesis

Module 7: Natural Language Processing

  1. 7.1 NLP in Real-world Scenarios
  2. 7.2 Attention Mechanisms and Practical Use of Transformers
  3. 7.3 In-depth Understanding of BERT for Practical NLP Tasks
  4. 7.4 Hands-on: Building Practical NLP Pipelines with Pretrained Models

Module 8: Transfer Learning with Hugging Face

  1. 8.1 Overview of Transfer Learning in AI
  2. 8.2 Transfer Learning Strategies and Techniques
  3. 8.3 Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks

Module 9: Crafting Sophisticated GUIs for AI Solutions

  1. 9.1 Overview of GUI-based AI Applications
  2. 9.2 Web-based Framework
  3. 9.3 Desktop Application Framework

Module 10: AI Communication and Deployment Pipeline

  1. 10.1 Communicating AI Results Effectively to Non-Technical Stakeholders
  2. 10.2 Building a Deployment Pipeline for AI Models
  3. 10.3 Developing Prototypes Based on Client Requirements
  4. 10.4 Hands-on: Deployment

Optional Module: AI Agents for Engineering

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

Finish the course and get certified

certificate

Industry Opportunities

Opportunity Image

AI Engineer

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

Opportunity Image

AI Solutions Architect

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

Opportunity Image

Machine Learning Engineer

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

Opportunity Image

AI Systems Integrator

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

Opportunity Image

AI Project Manager

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

Frequently Asked Questions

What topics are covered in the AI+ Engineer™ Certification?
The certification covers a wide range of topics including Foundations of AI, AI Architecture, Neural Networks, Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), and Transfer Learning using Hugging Face.
Who is the target audience for this certification?
This certification is ideal for individuals seeking to gain a deep understanding of AI concepts and techniques, whether they are beginners or have some prior knowledge of AI.
What practical skills will I gain from this course?
Participants will gain hands-on experience in building and deploying AI solutions. Skills include developing neural networks, fine-tuning large language models, implementing generative AI models, and crafting sophisticated GUIs for AI applications. Additionally, participants will learn to navigate AI communication and deployment pipelines.
What type of learning experience can I expect from this course?
The course emphasizes hands-on learning, enabling participants to develop practical skills in creating Graphical User Interfaces (GUIs) for AI solutions and understanding AI communication and deployment pipelines.
How does this certification benefit my career?
The AI+ Engineer™ Certification enhances your professional profile by demonstrating proficiency in AI fundamentals and advanced applications. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities in tech, healthcare, finance, and other industries.

Prerequisites

  • AI+ Data™  or AI+ Developer™ course should be completed. 
  • Basic understanding of Python programming is mandatory for hands-on exercises and project work. 
  • Familiarity with high school-level algebra and basic statistics is required. 
  • Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential. 

Exam Details

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

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%
Self-Paced Online

Self-Paced: 40 hours of content

USD $ 540.00
Purchase Self-Paced Course
Instructor-Led Online

Instructor-Led: 5 days (live or virtual)

USD $ 4250.00
Purchase Instructor-Led Course

Core AI Tools Covered

TensorFlow

TensorFlow

Hugging Face Transformers

Hugging Face Transformers

Jenkins

Jenkins

TensorFlow Hub

TensorFlow Hub