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AI+ Engineer™

Innovate Engineering: Leverage AI-Driven Smart Solutions Full AI Stack: Learn AI architecture, LLMs, NLP, and neural networks Tool Proficiency: Includes Transfer Learning with...
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  • AI Architecture
  • Neural Networks
  • Large Language Models (LLMs)
  • Generative AI
  • Natural Language Processing (NLP)
  • Transfer Learning using Hugging Face
  • AI Deployment Pipelines
  • 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.
AI+ Data™  or AI+ Developer™ course should be completed, basic math, computer science fundamentals, Python familiarity
50 questions, 70% passing, 90 minutes, online proctored exam

  1. Course Introduction

  1. 1.1 Introduction to AI
  2. 1.2 Core Concepts and Techniques in AI
  3. 1.3 Ethical Considerations

  1. 2.1 Overview of AI and its Various Applications
  2. 2.2 Introduction to AI Architecture
  3. 2.3 Understanding the AI Development Lifecycle
  4. 2.4 Hands-on: Setting up a Basic AI Environment

  1. 3.1 Basics of Neural Networks
  2. 3.2 Activation Functions and Their Role
  3. 3.3 Backpropagation and Optimization Algorithms
  4. 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework

  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

  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

  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

  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

  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

  1. 9.1 Overview of GUI-based AI Applications
  2. 9.2 Web-based Framework
  3. 9.3 Desktop Application Framework

  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

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

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