CI-CCEAA
Tel: +971 (4) 42 89 440
AT-330
AI+ Engineer
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Price:
Duration:
Please call
5 Days
Prerequisites
• AI+ Data or AI Developer course should be completed
• Basic understanding of Python
• Basic Math: Familiarity with high school-level algebra and basic statistics
• Python Programming: Proficiency in Python is mandatory for hands-on exercises and project work.
• Computer Science Fundamentals: Understanding basic programming concepts (variables, functions, loops) and data structures (lists, dictionaries).
What you’ll learn in this course
With a focus on hands-on learning, students develop proficiency in crafting sophisticated Graphical User Interfaces (GUIs) tailored for AI solutions and gain insight into AI communication and deployment pipelines. Upon completion, graduates are equipped with a robust understanding of AI concepts and techniques, ready to tackle real-world challenges and contribute effectively to the ever-evolving field of Artificial Intelligence
With a focus on hands-on learning, students develop proficiency in crafting sophisticated Graphical User Interfaces (GUIs) tailored for AI solutions and gain insight into AI communication and deployment pipelines. Upon completion, graduates are equipped with a robust understanding of AI concepts and techniques, ready to tackle real-world challenges and contribute effectively to the ever-evolving field of Artificial Intelligence
Course Objectives
• Attain a comprehensive understanding of AI fundamentals, from basic principles to advanced applications.
• Gain hands-on experience in building and deploying AI solutions.
• Learn about AI architecture, neural networks, LLM, generative AI, and NLP.
• Utilize Transfer Learning techniques with frameworks like Hugging Face to efficiently adapt pre-trained models for various tasks.
• Develop skills to create sophisticated GUIs for AI applications.
• Navigate AI communication and deployment pipelines effectively.
Course Outline
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
Further information
If you would like to know more about this course please contact us
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