AI+ Engineer
The AI Engineer course offers comprehensive AI engineer training, covering AI fundamentals, neural networks, and large language models. Gain hands-on experience with AI architecture, generative models, and NLP applications. Learn to deploy AI models and communicate results effectively. This artificial engineer certification course is designed to equip you with practical skills for building sophisticated AI-driven solutions. Perfect for those pursuing expertise in AI/ML.
All students receive:
- One-Year Subscription (with all updates)
- High-Quality E-Book
- Al Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Exam Study Guide
- Proctored Exam with one Free Retake
Check out our full list of training locations and learning formats. Please note that the location you choose may be an Established HD-ILT location with a virtual live instructor.
Train face-to-face with the live instructor.
Access to on-demand training content anytime, anywhere.
Attend the live class from the comfort of your home or office.
Interact with a live, remote instructor from a specialized, HD-equipped classroom near you. An SLI sales rep will confirm location availability prior to registration confirmation.
All Sunset Learning dates are guaranteed to run!
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Prerequisites:
Required:
- Proficiency in Python is mandatory for hands-on exercises and project work.
- Familiarity with high school-level algebra and basic statistics.
- Understanding basic programming concepts (variables, functions, loops) and data structures (lists, dictionaries).
Recommended:
- AI+ Executive and AI+ Developer or AI+ Data
Target Audience:
- AI Engineer
- IT Professional
Course Objectives:
- Foundational Understanding
- Ethical Considerations
- Understanding AI Architecture and Development Lifecycle
- Practical Implementation and Hands-on Experience
- Effective Communication and Deployment
Course Outline:
Foundations of Artificial Intelligence
- Introduction to AI
- Core Concepts and Techniques in AI
- Ethical Considerations
Introduction to AI Architecture
- Overview of AI and its Various Applications
- Introduction to AI Architecture
- Understanding the AI Development Lifecycle
- Hands-on: Setting up a Basic AI Environment
Fundamentals of Neural Networks
- Basics of Neural Networks
- Activation Functions and Their Role
- Backpropagation and Optimization Algorithms
- Hands-on: Building a Simple Neural Network Using a Deep Learning Framework
Applications of Neural Networks
- Introduction to Neural Networks in Image Processing
- Neural Networks for Sequential Data
- Practical Implementation of Neural Networks
Significance of Large Language Models (LLM)
- Exploring Large Language Models
- Popular Large Language Models
- Practical Finetuning of Language Models
- Hands-on: Practical Finetuning for Text Classification