AI+ Developer™
AT-310
Get hands-on with the tools and technologies that power the AI ecosystem.- Core AI Foundations: Covers Python, deep learning, data processing, and algorithm design
- Hands-on Projects: Focus on NLP, computer vision, and reinforcement learning
- Advanced Modules: Includes time series, model explainability, and cloud deployment
- Industry-Ready Skills: Prepares learners to design and deploy complex AI systems
Why This Certification Matters
At a Glance: Course + Exam Overview
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Job Roles & Industry Outlook
AI Systems Engineer
Design and optimize data pipelines, warehouses, and systems to enable AI-driven analytics and applications.
User Experience Engineer
Design and build engaging, intuitive, and responsive digital interfaces that bring ideas to life and enhance user journeys.
Industry Growth: AI+ Developer™
- Master Key AI Development Skills: Learn Python, deep learning, advanced concepts, and optimization techniques to build robust AI solutions.
- Specialize in Cutting-Edge AI Domains: Gain expertise in NLP, computer vision, or reinforcement learning, alongside data processing, exploratory analysis, and time series analysis.
- Stay Ahead in AI Development: AI is transforming industries, and organizations seek developers with strong proficiency in deploying AI models to solve real-world problems.
- Advance Your Career in AI Development: With growing demand across tech, finance, and healthcare sectors, this certification positions you as a leader in AI-driven development.
Who Should Enroll?
- Software Developers: Enhance your coding expertise by mastering AI algorithms and deep learning techniques.
- Data Enthusiasts: Apply AI-driven data analysis, machine learning models, and deep learning to solve complex problems.
- Computer Vision & NLP Researchers: Dive into specialized AI fields, including computer vision and natural language processing.
- IT Specialists & System Architects: Integrate AI solutions into existing systems and optimize performance.
- Students & Fresh Graduates: Build a strong foundation in AI development and prepare for future opportunities in tech.
What You'll Learn
- Course IntroductionPreview
- 3.1 Python Fundamentals Preview
- 3.2 Python Libraries
- 4.1 Introduction to Machine Learning
- 4.2 Supervised Machine Learning Algorithms
- 4.3 Unsupervised Machine Learning Algorithms
- 4.4 Model Evaluation and Selection
- 5.1 Neural Networks
- 5.2 Improving Model Performance
- 5.3 Hands-on: Evaluating and Optimizing AI Models
- 6.1 Image Processing Basics
- 6.2 Object Detection
- 6.3 Image Segmentation
- 6.4 Generative Adversarial Networks (GANs)
- 7.1 Text Preprocessing and Representation
- 7.2 Text Classification
- 7.3 Named Entity Recognition (NER)
- 7.4 Question Answering (QA)
- 8.1 Introduction to Reinforcement Learning
- 8.2 Q-Learning and Deep Q-Networks (DQNs)
- 8.3 Policy Gradient Methods
- 9.1 Cloud Computing for AI
- 9.2 Cloud-Based Machine Learning Services
- 10.1 Understanding LLMs
- 10.2 Text Generation and Translation
- 10.3 Question Answering and Knowledge Extraction
- 11.1 Neuro-Symbolic AI
- 11.2 Explainable AI (XAI)
- 11.3 Federated Learning
- 11.4 Meta-Learning and Few-Shot Learning
- 12.1 Communicating AI Projects
- 12.2 Documenting AI Systems
- 12.3 Ethical Considerations
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
Tools You'll Explore
GitHub Copilot
Lobe
H2O.ai
Snorkel
Prerequisites
- Basic math, computer science fundamentals, fundamental programming skills
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via AI proctored exam platform (flexible scheduling)
Exam Blueprint
- Module 1: Foundations of Artificial Intelligence - 7%
- Module 2: Mathematical Concepts for AI - 7%
- Module 3: Python for Developer - 7%
- Module 4: Mastering Machine Learning - 7%
- Module 5: Deep Learning - 8%
- Module 6: Computer Vision - 8%
- Module 7: Natural Language Processing - 8%
- Module 8: Reinforcement Learning - 8%
- Module 9: Cloud Computing in AI Development - 8%
- Module 10: Large Language Models - 8%
- Module 11: Cutting-Edge AI Research - 8%
- Module 12: AI Communication and Documentation - 8%
- Optional Module: AI Agents for Developers - 8%
Choose the Format That Fits Your Schedule
What's Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone
Frequently Asked Questions
The course includes a mix of theoretical knowledge and practical applications, culminating in an interactive capstone project. This structure ensures that participants gain both conceptual understanding and hands-on experience.
This course is ideal for developers, IT professionals, and anyone with a foundational understanding of AI and cloud computing who wants to enhance their skills in integrating AI with cloud platforms like AWS, Azure, or Google Cloud.
Participants will learn to develop, deploy, and manage AI models on leading cloud platforms. Skills include optimizing AI model performance, ensuring security, meeting compliance standards, and applying AI and cloud concepts to solve real-world problems.
This certification enhances your professional profile by demonstrating proficiency in integrating AI with cloud computing. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities.
The certification includes an interactive capstone project where participants apply their knowledge to design and implement AI solutions within cloud environments. This project is designed to simulate real-world scenarios and challenges.