AI+ Engineer Practitioner™
Formerly known as AI+ Engineer™ Innovate Engineering: Leverage AI-Driven Smart Solutions
Delivered by UpskillNexus, an AI CERTs® Authorized Training Partner.
About this certification
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
What's included
Package
Duration
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Exam format
Prerequisites
Course modules
Course Overview
- Course Introduction Preview
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
Why this certification matters
Master AI System Design:
Build Scalable AI Solutions:
Tackle Complex Engineering Challenges:
Contribute to AI-Driven Innovations:
Advance Your Career in AI Engineering:
Who should enrol
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.
Tools you'll work with
TensorFlow
Hugging Face Transformers
Jenkins
TensorFlow HubInterested in AI+ Engineer Practitioner™?
Tell us a little about yourself and our team will get back to you with pricing, schedule and next steps.