AI+ Engineer™
AI+ Engineer™
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
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.
- Course Introduction Preview
- 4.1 Introduction to Neural Networks in Image Processing
- 4.2 Neural Networks for Sequential Data
- 4.3 Practical Implementation of Neural Networks
- 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
- 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
- 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
- 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
- 9.1 Overview of GUI-based AI Applications
- 9.2 Web-based Framework
- 9.3 Desktop Application Framework
- 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
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
TensorFlow
Hugging Face Transformers
Jenkins
TensorFlow Hub
Passing Score : 70% (35/50)
Number of Multiple Choice Question : 50 MCQs, 90 Minutes
Number of exams required for certification : 1
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%
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.
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.
FAQ
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.
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