AI+ Engineer™

AI+ Engineer™

Innovate Engineering: Leverage AI-Driven Smart Solutions

Certificate Code: AT-330

About This Course

  • 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

Certificate 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+ Data™  or AI+ Developer™ course should be completed, basic math, computer science fundamentals, Python familiarity

Exam Format

50 questions, 70% passing, 90 minutes, online proctored exam

Course Modules

1

Course Overview

  1. Course Introduction Preview
2

Module 1: Foundations of Artificial Intelligence

  1. 1.1 Introduction to AI Preview
  2. 1.2 Core Concepts and Techniques in AI Preview
  3. 1.3 Ethical Considerations
3

Module 2: Introduction to AI Architecture

  1. 2.1 Overview of AI and its Various ApplicationsPreview
  2. 2.2 Introduction to AI Architecture Preview
  3. 2.3 Understanding the AI Development Lifecycle Preview
  4. 2.4 Hands-on: Setting up a Basic AI Environment
4

Module 3: Fundamentals of Neural Networks

  1. 3.1 Basics of Neural Networks Preview
  2. 3.2 Activation Functions and Their Role Preview
  3. 3.3 Backpropagation and Optimization Algorithms
  4. 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework
5

Module 4: Applications of Neural Networks

  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
6

Module 5: Significance of Large Language Models (LLM)

  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
7

Module 6: Application of Generative AI

  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
8

Module 7: Natural Language Processing

  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
9

Module 8: Transfer Learning with Hugging Face

  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
10

Module 9: Crafting Sophisticated GUIs for AI Solutions

  1. 9.1 Overview of GUI-based AI Applications
  2. 9.2 Web-based Framework
  3. 9.3 Desktop Application Framework
11

Module 10: AI Communication and Deployment Pipeline

  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
12

Optional Module: AI Agents for Engineering

  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents

AI Tools You'll Learn

TensorFlow

TensorFlow

Hugging Face Transformers

Hugging Face Transformers

Jenkins

Jenkins

TensorFlow Hub

TensorFlow Hub