AI+ Engineer™

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

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Includes:

Ideal candidates for this course:

AI Engineer. Design, develop, and optimize AI systems, working on neural networks, deep learning, and NLP to solve complex challenges.
AI Solutions Architect. Create scalable AI architectures and integrate AI solutions into various business systems to drive innovation and efficiency.
Machine Learning Engineer. Develop machine learning models and algorithms, focusing on predictive analytics, deep learning, and data-driven solutions.
AI Systems Integrator. Implement AI technologies into existing infrastructures, ensuring seamless integration and scalability of AI solutions.
AI Project Manager. Lead AI-driven projects, managing timelines, resources, and stakeholder expectations to ensure successful deployment of AI solutions.

Prerequisites

AI+ Data™  or AI+ Developer™ 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.

Content

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

After completing this course you will be able to:

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