Description

Why This Certification Matters

• Master AI System Design: Develop the skills to design, implement, and optimize advanced AI systems for real-world applications.
• Build Scalable AI Solutions: Learn how to create scalable AI solutions for industries like technology, finance, and healthcare.
• Tackle Complex Engineering Challenges: This certification ensures you’re equipped to solve challenges in AI architecture, neural networks, and NLP.
• Contribute to AI-Driven Innovations: Certified AI+ Engineers develop cutting-edge AI solutions that enhance business operations and drive future innovations.
• Advance Your Career in AI Engineering: As demand for skilled AI engineers rises, this certification offers a competitive advantage in the job market.

Course Outline:

Course Overview

• Course Introduction

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

 

Prerequisites

• AI+ Developer™ course should be complete
• 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.
• Basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential.

 

Course and Exam Overview

• Included: Instructor-led OR Self-paced course + Official exam + Digital badge
• Duration: Instructor-Led: 5 days (live or virtual) Self-Paced: 40 hours of content
• Prerequisites: AI+ Developer™ course should be completed, basic math, computer science fundamentals, Python familiarity
• Exam Format: 50 questions, 70% passing, 90 minutes, online proctored exam
• Delivery: Online labs, projects, case studies
• Outcome: Industry-recognized credential + hands-on experience