AI+ Engineer™ - eLearning (exam included)
448,00 EUR
- 40 hours
The AI+ Engineer certification is tailored for Software Engineers, offering a structured pathway from AI fundamentals to advanced applications. The program begins with AI foundations and progresses to AI architecture, neural networks, LLMs, generative AI, NLP, and Transfer Learning using Hugging Face. Participants will also gain skills in designing advanced GUIs for AI solutions and understanding AI communication and deployment pipelines through practical, hands-on exercises.
Key Features
Language
Course and material in English
Level
Advanced level (Category: AI+ Technical)
1 year platform access
and Virtual Hands-on Lab included
40 hours of video lessons & multimedia
50 hours of study time recommendation
Material
Video, PDF Material, audio eBook, Podcasts, quizzes and assessments.
Exam
Online Proctored Exam with One Free Retake
Certificate
Certification of completion included. Valid for 1 year
Tools You’ll Master
TensorFlow, Jenkins, TensorFlow Hub, Hugging Face Transformers
About the course
Innovate Engineering: Harness AI-Powered Smart Solutions
- Comprehensive AI Stack: Explore AI architectures, LLMs, NLP, and neural networks
- Hands-On Tools: Work with Transfer Learning via Hugging Face and GUI development
- Deployment Skills: Create functional AI systems and manage communication pipelines
- Practical Expertise: Develop the ability to engineer scalable, innovative AI solutions
Ethical considerations in AI are emphasized, ensuring learners understand fairness, transparency, and accountability in AI systems. Real-world case studies and exercises help identify and mitigate biases, enhancing ethical AI deployment. This certification equips engineers with the knowledge and skills to solve practical AI challenges, innovate responsibly, and take leadership roles in the rapidly evolving AI landscape.
Why This Certification Matters
Gain expertise in designing, implementing, and optimizing advanced AI systems for practical applications.
Learning Outcomes
At the end of this course, you will be able to:
AI GUI Development
Create intuitive, user-friendly interfaces for AI applications, incorporating usability testing and integration methods.
AI Deployment & Communication
Learn to develop AI systems, manage deployment pipelines, and effectively communicate their value to stakeholders
AI Problem-Solving
Apply AI techniques to tackle real-world challenges, analyze results, and improve problem-solving approaches.
AI Project Management
Gain skills to plan, allocate resources, manage stakeholders, and successfully deliver AI-focused projects.
Course timeline
Foundations of Artificial Intelligence
Lesson 1
- 1.1 Introduction to AI
- 1.2 Core Concepts and Techniques in AI
- 1.3 Ethical Considerations
Introduction to AI Architecture
Lesson 2
- 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
Fundamentals of Neural Networks
Lesson 3
- 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
Applications of Neural Networks
Lesson 4
- 4.1 Introduction to Neural Networks in Image Processing
- 4.2 Neural Networks for Sequential Data
- 4.3 Practical Implementation of Neural Networks
Significance of Large Language Models (LLM)
Lesson 5
- 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
Application of Generative AI
Lesson 6
- 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
Natural Language Processing
Lesson 7
- 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
Transfer Learning with Hugging Face
Lesson 8
- 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
Crafting Sophisticated GUIs for AI Solutions
Lesson 9
- 9.1 Overview of GUI-based AI Applications
- 9.2 Web-based Framework
- 9.3 Desktop Application Framework
AI Communication and Deployment Pipeline
Lesson 10
- 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
AI Agents for Engineering
Optional Module
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
Who Should Enroll in this Program?
AI & Software Engineers: Master AI techniques and advanced system design.
Machine Learning Enthusiasts: Apply deep learning, NLP, and neural networks.
Data Scientists: Build and deploy scalable AI solutions.
IT Specialists & System Architects: Integrate AI to optimize infrastructure.
Industry Growth
Driving Next-Generation AI-Enabled Engineering
- By 2027, 80% of engineers will need upskilling to adapt to generative AI (GenAI) technologies (Gartner).
- Rapid AI adoption across sectors is boosting demand for professionals with advanced AI expertise.
- Organizations are seeking AI+ Engineers to build innovative solutions for AI-driven automation and decision-making.
- The global need for AI engineering skills is expanding, creating lucrative opportunities for experts in AI system design and deployment.
More Details
Prerequisites
- Completion of the AI+ Data™ or AI+ Developer™ course is recommended.
- A solid foundation in Python programming is required for practical exercises and projects.
- Basic knowledge of high school-level algebra and statistics is necessary.
- Familiarity with core programming concepts, including variables, functions, loops, and data structures like lists and dictionaries, is essential.
Exam Details
- Duration: 90 minutes
- Passing :70% (35/50)
- Format: 50 multiple-choice/multiple-response questions
- Delivery Method: Online via proctored exam platform (flexible scheduling)
- Language: English
Licensing and accreditation
This course is offered by AVC according to Partner Program Agreement and complies with the License Agreement requirements.
Equity Policy
AVC does not provide accommodations due to a disability or medical condition of any students. Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.
Frequently Asked Questions
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