AI+ Engineer Practitioner™

Ártól 200 000 Ft + Áfa
Önálló online Azonnal kezdhető
Időtartam 40 óra tananyag, önálló tempóban
Nyelv angol
Terület AI Development

Kinek szól
Mit tartalmaz az ár
Tematika
A vizsgáról

Áttekintés

Miről szól a képzés

A kurzus, a tananyag és a vizsga angol nyelvű. Az alábbi leírás az AI CERTs® eredeti szövege.

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

Előfeltételek

AI+ Data Practitioner™ or AI+ Developer Practitioner™ course should be completed, basic math, computer science fundamentals, Python familiarity

Célcsoport

Kinek szól

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.

Az árban

Mit tartalmaz az ár

Önálló tempójú online kurzus, a teljes tananyaggal
A hivatalos AI CERTs® vizsga
Digitális badge a sikeres vizsga után

Tananyag

Tematika

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

Vizsga

A vizsgáról

50 Kérdés
90 perc Időkeret
70% Ponthatár

Online proctored exam

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