What’s Included?
High-Quality Video, E-book & Audiobook
Modules Quizzes
AI Mentor
Access for Tablet & Phone
Online Proctored Exam with One Free Retake
Prerequisites
- AI+ Data™ or AI+ Developer™ course should be completed.
- Basic understanding of Python programming is mandatory for hands-on exercises and project work.
- Familiarity with high school-level algebra and basic statistics is required.
- Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential.
Skills You’ll Gain
- AI Architecture
- Neural Networks
- Large Language Models (LLMs)
- Generative AI
- Natural Language Processing (NLP)
- Transfer Learning using Hugging Face
- AI Deployment Pipelines
Self Study Materials Included
Videos
Engaging visual content to enhance understanding and learning experience.
Podcasts
Insightful audio sessions featuring expert discussions and real-world cases.
Audiobooks
Listen and learn anytime with convenient audio-based knowledge sharing.
E-Books
Comprehensive digital guides offering in-depth knowledge and learning support.
Module Wise Quizzes
Interactive assessments to reinforce learning and test conceptual clarity.
Additional Resources
Supplementary references and list of tools to deepen knowledge and practical application.
Tools You’ll Master
TensorFlow
Hugging Face Transformers
Jenkins
TensorFlow Hub
What You’ll Learn
GUI Develop for AI Solutions
Students will learn to develop user-friendly AI GUIs. Interface design, usability testing, and AI integration into GUIs will be covered to build intuitive and engaging user experiences.
AI Communication and Deployment Pipeline
Learners will gain knowledge of AI solution communication and deployment, including developing and managing deployment pipelines for efficient AI system rollout and maintenance, as well as explaining the value and utility of AI solutions to stakeholders and end-users.
AI Problem-Solving
Students will apply AI principles from the course to real-world issues, enhancing their skills in identifying AI methodologies, constructing models, and interpreting results to address complex problems across disciplines.
AI-Specific Project Management
Learners will build AI-specific project management abilities by engaging with AI project workflows. This involves developing, implementing, and managing AI initiatives, managing resources, schedules, and stakeholder expectations for success.
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
Frequently Asked Questions
The certification covers a wide range of topics including Foundations of AI, AI Architecture, Neural Networks, Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), and Transfer Learning using Hugging Face.
This certification is ideal for individuals seeking to gain a deep understanding of AI concepts and techniques, whether they are beginners or have some prior knowledge of AI.
Participants will gain hands-on experience in building and deploying AI solutions. Skills include developing neural networks, fine-tuning large language models, implementing generative AI models, and crafting sophisticated GUIs for AI applications. Additionally, participants will learn to navigate AI communication and deployment pipelines.
The course emphasizes hands-on learning, enabling participants to develop practical skills in creating Graphical User Interfaces (GUIs) for AI solutions and understanding AI communication and deployment pipelines.
The AI+ Engineer™ Certification enhances your professional profile by demonstrating proficiency in AI fundamentals and advanced applications. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities in tech, healthcare, finance, and other industries.