AI+ Engineer™

Target group

AI & Software Engineers: Enhance your development skills by mastering AI techniques and designing advanced AI systems.

Machine Learning Enthusiasts: Apply deep learning, neural networks, and NLP techniques to real-world AI challenges.

Data Scientists: Strengthen your AI toolkit with engineering techniques for building and deploying scalable AI solutions.

IT Specialists & System Architects: Integrate AI solutions into existing infrastructures, optimizing performance and scalability.

Students & New Graduates: Develop in-demand AI engineering skills and prepare for a successful career in the rapidly growing AI field.

Course structure

Course Overview

1. Course Introduction Preview

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

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

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

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

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

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

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

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

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

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

Optional Module: AI Agents for Engineering

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

Certification

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