AI+ Engineer (AIENG) – Perfil

Esquema Detallado del Curso

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