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

Course Overview

* Course Introduction Preview Module 1: Foundations of Artificial Intelligence

* 1.1 Introduction to AI Preview
* 1.2 Core Concepts and Techniques in AI Preview
* 1.3 Ethical Considerations Module 2: Introduction to AI Architecture

* 2.1 Overview of AI and its Various ApplicationsPreview
* 2.2 Introduction to AI Architecture Preview
* 2.3 Understanding the AI Development Lifecycle Preview
* 2.4 Hands-on: Setting up a Basic AI Environment Module 3: Fundamentals of Neural Networks

* 3.1 Basics of Neural Networks Preview
* 3.2 Activation Functions and Their Role Preview
* 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 Tools you will explore

* TensorFlow
* Hugging Face Transformers
* Jenkins
* TensorFlow Hub

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

Instructor-led OR Self-paced course + Official exam + Digital badge