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AI+ Engineer™

AT-330

Innovate Engineering: Leverage AI-Driven Smart Solutions

The AI+ 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 AI, participants progress through modules covering AI Architecture, Neural Networks, Large Language Models (LLMs), Generative AI, 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 AI solutions and gain insight into AI communication and deployment pipelines. Upon completion, graduates are equipped with a robust understanding of AI concepts and techniques, ready to tackle real-world challenges and contribute effectively to the ever-evolving field of Artificial Intelligence.

Certification Duration: 40 hours (5 Days)

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Prerequisites

  • AI+ Data™ or AI+ Developer™ course should be completed.
  • Basic understanding of Python Programming: Proficiency in Python is mandatory for hands-on exercises and project work.
  • Basic Math: Familiarity with high school-level algebra and basic statistics.
  • Computer Science Fundamentals: Understanding basic programming concepts (variables, functions, loops) and data structures (lists, dictionaries).

Modules

10

Examination

1

50 MCQs

90 Minutes

Passing Score

70%

Certification Modules

  1. Course Introduction Preview
  1. 1.1 Introduction to AI
  2. 1.2 Core Concepts and Techniques in AI
  3. 1.3 Ethical Considerations
  1. 2.1 Overview of AI and its Various Applications
  2. 2.2 Introduction to AI Architecture
  3. 2.3 Understanding the AI Development Lifecycle
  4. 2.4 Hands-on: Setting up a Basic AI Environment
  1. 3.1 Basics of Neural Networks
  2. 3.2 Activation Functions and Their Role
  3. 3.3 Backpropagation and Optimization Algorithms
  4. 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework
  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
  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
  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
  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
  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
  1. 9.1 Overview of GUI-based AI Applications
  2. 9.2 Web-based Framework
  3. 9.3 Desktop Application Framework
  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

Certification Modules

  1. Course Introduction Preview
  1. 1.1 Introduction to AI
  2. 1.2 Core Concepts and Techniques in AI
  3. 1.3 Ethical Considerations
  1. 2.1 Overview of AI and its Various Applications
  2. 2.2 Introduction to AI Architecture
  3. 2.3 Understanding the AI Development Lifecycle
  4. 2.4 Hands-on: Setting up a Basic AI Environment
  1. 3.1 Basics of Neural Networks
  2. 3.2 Activation Functions and Their Role
  3. 3.3 Backpropagation and Optimization Algorithms
  4. 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework
  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
  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
  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
  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
  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
  1. 9.1 Overview of GUI-based AI Applications
  2. 9.2 Web-based Framework
  3. 9.3 Desktop Application Framework
  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

Tools

TensorFlow

Hugging Face Transformers

Jenkins

TensorFlow Hub

Exam Objectives

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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.

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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.

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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.

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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 Post-Certification

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Median Salaries

$67,583
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With AI Skills

$1,34,143
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% Difference

98
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Systems Engineers

Combine hardware and software to create high-performing AI systems, optimizing performance and reliability for emerging applications across sectors.

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AI Developers

Utilize algorithms to create tailored AI solutions, improving operational efficiency and company performance through intelligent automation.

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Technology Engineer

Research and implement cutting-edge algorithms, enhancing AI capabilities for competitive advantage in machine learning applications.

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Infrastructure Architect

Develop scalable systems for AI workflows, optimizing architecture for robust performance in complex AI operations and deployments.

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Frequently Asked Questions

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.

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.

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.

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.

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.

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