About This Course
- AI Architecture
- Neural Networks
- Large Language Models (LLMs)
- Generative AI
- Natural Language Processing (NLP)
- Transfer Learning using Hugging Face
- AI Deployment Pipelines
Course Information
Course Code
AI+ Engineer™
Duration
Self-Paced: 40 hours of content
Delivery
Self Paced
Exam Voucher
Included
Course Curriculum
- Course Introduction
- 1.1 Introduction to AI
- 1.2 Core Concepts and Techniques in AI
- 1.3 Ethical Considerations
- 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
- 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
- 4.1 Introduction to Neural Networks in Image Processing
- 4.2 Neural Networks for Sequential Data
- 4.3 Practical Implementation of Neural Networks
- 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
- 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
- 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
- 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
- 9.1 Overview of GUI-based AI Applications
- 9.2 Web-based Framework
- 9.3 Desktop Application Framework
- 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
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
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All You Need to Know
Who Should Attend
- 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.
Prerequisites
AI+ Data™ or AI+ Developer™ course should be completed, basic math, computer science fundamentals, Python familiarity
Examination
50 questions, 70% passing, 90 minutes, online proctored exam
Frequently Asked Questions
AI and software engineers, machine learning enthusiasts, data scientists, and IT specialists wanting to engineer scalable AI solutions.
Completing AI+ Data or AI+ Developer first, along with basic maths, computer science fundamentals, and Python familiarity.
50 questions, 70% passing score, 90-minute online proctored exam.
Full AI stack including architecture, LLMs, NLP, neural networks, and transfer learning with tools like Hugging Face.
Yes — it focuses on practical mastery through building and deploying real AI systems.