AI+ Engineer™

Innovate Engineering: Leverage AI-Driven Smart Solutions

Certificate Code: AT-330

Duration: 40 hours (5 Days)

Enroll Now

Course Metrics Prerequisites Curriculum Outcomes Opportunities Salary Comparison FAQs Enroll

12+

Modules

40 hours (5 Days)

Duration

50 MCQs, 90 Minutes

Examination

70% (35/50)

Passing Score

About This Certification

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

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.

Tools Covered

TensorFlow

Hugging Face Transformers

Jenkins

TensorFlow Hub

Course Curriculum

12 Modules 40 hours (5 Days)

Course Overview

Course Introduction Preview

Module 1: Foundations of Artificial Intelligence

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

Module 2: Introduction to AI Architecture

2.1 Overview of AI and its Various ApplicationsPreview
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

Module 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

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

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%

Learning Outcomes

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.

Career Opportunities

AI Engineer

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

AI Solutions Architect

Create scalable AI architectures and integrate AI 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.

AI Systems Integrator

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

AI Project Manager

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

Salary Potential

Without AI Skills

$67,583

With AI Skills

$134,143

98% Higher Earning Potential

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.

Start Your AI Journey Today