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AI+ Engineer Practitioner™

Formerly known as AI+ Engineer™ Innovate Engineering: Leverage AI-Driven Smart Solutions
Instructor
ppcg
  • Description
  • Reviews

Formerly known as AI+ Engineer™ Innovate Engineering: Leverage AI-Driven Smart Solutions

Certification code: AT-330

  • 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

Overview

Duration
  • Instructor-Led: 5 days (live or virtual)
  • Self-Paced: 40 hours of content
Exam format
50 questions, 70% passing, 90 minutes, online proctored exam
Prerequisites
AI+ Data Practitioner™  or AI+ Developer Practitioner™ course should be completed, basic math, computer science fundamentals, Python familiarity
What's included
Instructor-led OR Self-paced course + Official exam + Digital badge

Tools covered

  • TensorFlowTensorFlow
  • Hugging Face TransformersHugging Face Transformers
  • JenkinsJenkins
  • TensorFlow HubTensorFlow Hub

Course modules

Course Overview
  1. Course Introduction Preview
Module 1: Foundations of Artificial Intelligence
  1. 1.1 Introduction to AI Preview
  2. 1.2 Core Concepts and Techniques in AI Preview
  3. 1.3 Ethical Considerations
Module 2: Introduction to AI Architecture
  1. 2.1 Overview of AI and its Various ApplicationsPreview
  2. 2.2 Introduction to AI Architecture Preview
  3. 2.3 Understanding the AI Development Lifecycle Preview
  4. 2.4 Hands-on: Setting up a Basic AI Environment
Module 3: Fundamentals of Neural Networks
  1. 3.1 Basics of Neural Networks Preview
  2. 3.2 Activation Functions and Their Role Preview
  3. 3.3 Backpropagation and Optimization Algorithms
  4. 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework
Module 4: Applications of Neural Networks
  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
Module 5: Significance of Large Language Models (LLM)
  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
Module 6: Application of Generative AI
  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
Module 7: Natural Language Processing
  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
Module 8: Transfer Learning with Hugging Face
  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
Module 9: Crafting Sophisticated GUIs for AI Solutions
  1. 9.1 Overview of GUI-based AI Applications
  2. 9.2 Web-based Framework
  3. 9.3 Desktop Application Framework
Module 10: AI Communication and Deployment Pipeline
  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
Optional Module: AI Agents for Engineering
  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents
AI+ Engineer Practitioner™
Course details
Duration 40 Hours
Lectures10
Level Advanced

Working hours

Monday 8:00 am - 5.00 pm
Tuesday 8:00 am - 5.00 pm
Wednesday 8:00 am - 5.00 pm
Thursday 8:00 am - 5.00 pm
Friday 8:00 am - 5.00 pm
Saturday 9:00 am - 2.00 pm
Sunday Closed