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 Practitioner™ or AI+ Developer Practitioner™ course should be completed.
- Basic understanding of Python programming is mandatory for hands-on exercises and project work.
- Familiarity with high school-level algebra and basic statistics is required.
- Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential.
Course Objectives
- Master AI System Design: Develop the skills to design, implement, and optimize advanced AI systems for real-world applications.
- Build Scalable AI Solutions: Learn how to create scalable AI solutions for industries like technology, finance, and healthcare.
- Tackle Complex Engineering Challenges: This certification ensures you’re equipped to solve challenges in AI architecture, neural networks, and NLP.
- Contribute to AI-Driven Innovations: Certified AI+ Engineer Practitioner™ develop cutting-edge AI solutions that enhance business operations and drive future innovations.
- Advance Your Career in AI Engineering: As demand for skilled AI engineers rises, this certification offers a competitive advantage in the job market.
Course Content
DAY 1
- Course Introduction
- 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
- Optional Module: AI Agents for Engineering
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
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