AI+ Engineer
Innovate Engineering: Unlock the Potential of AI-Driven Solutions.
Description
The AI+ Engineer™ certification equips participants with a comprehensive understanding of Artificial Intelligence (AI) principles, advanced engineering techniques, and practical applications.
The program covers AI architecture, neural networks, Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP). It also introduces cutting-edge tools like Transfer Learning using frameworks such as Hugging Face.
Learners will develop expertise in designing Graphical User Interfaces (GUIs) for AI systems, managing communication pipelines, and deploying AI applications.
With hands-on experience and practical projects, graduates emerge as proficient AI engineers ready to tackle complex industry challenges and contribute to innovation in the ever-evolving AI landscape.
Prerequisites
- AI+ Data™ or AI+ Developer™ Certification: Completion is recommended for foundational knowledge.
- Python Programming Proficiency: Hands-on experience in Python is essential for project work.
- Mathematics Basics: High-school-level algebra and statistics are desirable.
- Computer Science Fundamentals: Familiarity with programming concepts like variables, functions, loops, and data structures.
Course Outline
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