AI+ Developer Practitioner™

Certificate Code: AT-310

Passing Score: 70% (35/50)

Exam Info: 50 MCQs, 90 Minutes

Tagline: Formerly known as AI+ Developer™ <br> <br> Get hands-on with the tools and technologies that power the AI ecosystem.

Course Overview:

Prerequisites:

Tools Used:

• GitHub Copilot
• Lobe
• H2O.ai
• Snorkel

Modules:

• Module 1: Foundations of Modern AI for Developers
  • 1.1 Understanding Artificial Intelligence
  • 1.2 Components of an AI Application
  • 1.3 Beginner AI Development Workflow
  • 1.4 AI Development Concepts and Limitations
  • 1.5 Case Study: A Chatbot Prototype That Produced Unreliable Answers
  • 1.6 Use Case: Selecting the Right AI Approach
• Module 2: Python Programming for AI
  • 2.1 Python Foundations
  • 2.2 Python Data Structures and File Handling
  • 2.3 Beginner Software-Development Practices
  • 2.4 Case Study: An Unstructured Python Script Becomes Difficult to Maintain
  • 2.5 Use Case: Automated File Processing Utility
• Module 3: Data Handling and Visualization
  • 3.1 Working with NumPy and Pandas
  • 3.2 Data Cleaning
  • 3.3 Exploratory Data Analysis
  • 3.4 Case Study: Dirty Customer Data Produces Incorrect Sales Insights
  • 3.5 Use Case: Retail Sales Data Preparation
• Module 4: Practical Mathematics and Statistics for AI
  • 4.1 Essential Mathematical Concepts
  • 4.2 Essential Statistics
  • 4.3 Mathematical Reasoning for AI
  • 4.4 Case Study: Average Performance Hides a Major Customer Problem
  • 4.5 Use Case: Similarity-Based Product Recommendation
• Module 5: Machine Learning Fundamentals
  • 5.1 Understanding Machine Learning
  • 5.2 Supervised Machine Learning
  • 5.3 Unsupervised and Other Beginner Methods
  • 5.4 Case Study: Customer Churn Prediction
  • 5.5 Use Case: Delivery-Time Prediction
• Module 6: Model Evaluation and Improvement
  • 6.1 Model Evaluation Metrics
  • 6.2 Improving Model Performance
  • 6.3 Reliable Model Delivery
  • 6.4 Case Study: A High-Accuracy Model Misses the Important Cases
  • 6.5 Use Case: Spam Email Detection
• Module 7: Deep Learning and Computer Vision Basics
  • 7.1 Neural Network Fundamentals
  • 7.2 Beginner Deep Learning with PyTorch
  • 7.3 Computer Vision Foundations
  • 7.4 Case Study: Manufacturing Defect Detection with Transfer Learning
  • 7.5 Use Case: Product Image Classification
• Module 8: Natural Language Processing, Transformers, and LLM Fundamentals
  • 8.1 Text Processing Fundamentals
  • 8.2 Embeddings and Transformers
  • 8.3 Large Language Model Fundamentals
  • 8.4 Case Study: Choosing Between a Text Classifier and an LLM for Routing Support Tickets
  • 8.5 Use Case: Customer Review Analysis
• Module 9: Generative and Multimodal AI Application Development
  • 9.1 Prompt Engineering Foundations
  • 9.2 Building Controlled Generative AI Applications
  • 9.3 Multimodal AI Foundations
  • 9.4 Case Study: Invoice Extraction Produces Incorrect Financial Fields
  • 9.5 Use Case: Multimodal Product Information Assistant
• Module 10: Retrieval-Augmented Generation and Knowledge Assistants
  • 10.1 Retrieval Fundamentals
  • 10.2 Building a Basic RAG Workflow
  • 10.3 RAG Quality and Control
  • 10.4 Case Study: A Policy Assistant Returns an Outdated Rule
  • 10.5 Use Case: Employee Handbook Assistant
• Module 11: Simple AI Agents, APIs, and Deployment
  • 11.1 API Development for AI
  • 11.2 Basic AI Agents and Tool Use
  • 11.3 Beginner Deployment and Operations
  • 11.4 Case Study: An Over-Privileged Agent Performs an Unapproved Action
  • 11.5 Use Case: IT Support Triage Assistant
• Module 12: Responsible AI, Security, Monitoring, and Capstone
  • 12.1 Responsible AI Foundations
  • 12.2 AI Application Security
  • 12.3 Monitoring and Production Readiness
  • 12.4 Case Study: Prompt Injection Causes Confidential Data Exposure
  • 12.5 Use Case: Beginner AI Release Checklist
• Optional Module: AI Agents for Developer
  • 1.1 What Are AI Agents?
  • 1.2 Significance of AI Agents for Developers
  • 1.3 Applications and Trends of AI Agents for Developers
  • 1.4 How Does an AI Agent Work?
  • 1.5 Core Characteristics of AI Agents
  • 1.6 Importance of AI Agents
  • 1.7 Types of AI Agents
  • 1.8 Comparison Table of AI Agents in Ethics

What You’ll Learn:

• Python Programming Proficiency — Students will gain a solid foundation in Python programming, a crucial skill for implementing AI algorithms, processing data, and building AI applications effectively.
• Deep Learning Techniques — Learners will master machine learning and deep learning techniques to address challenges in classification, regression, image recognition, and natural language processing.
• Cloud Computing in AI Development — Students will get hands-on experience in cloud-based AI application development and learn how to use AWS, Azure, and Google Cloud for scalable AI systems.
• Project Management in AI — Participations will master the skills necessary to manage AI projects effectively, from initiation to completion, including planning, resource allocation, risk management, and stakeholder communication.

Career Opportunities:

• AI Machine Learning Developer — Design, implement, and optimize algorithms and models to enable systems to learn from data and make predictions or decisions.
• AI Solutions Architect — Design and implement AI systems that integrate seamlessly with existing infrastructure to address business needs effectively and enhance system capabilities.
• AI Application Developer — Build, design, and maintain AI-driven applications that solve real-world problems, integrating AI technologies for enhanced functionality.
• AI System Programmers — Develop and maintain AI systems, including programming algorithms and software components that enable intelligent behavior in machines and applications.

Exam Blueprint:

• Foundations of Modern AI for Developers - 5%
• Python Programming for AI - 9%
• Data Handling and Visualization - 9%
• Practical Mathematics and Statistics for AI - 9%
• Machine Learning Fundamentals - 9%
• Model Evaluation and Improvement - 9%
• Deep Learning and Computer Vision Basics - 9%
• Natural Language Processing, Transformers, and LLM Fundamentals - 9%
• Generative and Multimodal AI Application Development - 8%
• Retrieval-Augmented Generation and Knowledge Assistants - 8%
• Simple AI Agents, APIs, and Deployment - 8%
• Responsible AI, Security, Monitoring, and Capstone - 8%

Self-Study Materials:

• 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.
• Labs: Interactive lab sessions to apply concepts and strengthen technical skills.
• 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.

Frequently Asked Questions:

• Q: What will I gain from completing this certification?
  A: Upon completion, you will receive an AI+ Developer Practitioner™ certification, showcasing your proficiency in AI. You'll have the skills to tackle real-world AI challenges and implement advanced AI solutions in various domains.
• Q: Do I need any prior AI knowledge to join this course?
  A: While prior AI knowledge is not mandatory, a fundamental understanding of Python programming and basic math and statistics will help you grasp the advanced concepts covered in this course.
• Q: Are there any hands-on projects in the course?
  A: Yes, the course includes various hands-on projects and practical exercises to help you apply theoretical concepts to real-world scenarios, reinforcing your learning through practical experience.
• Q: Can I choose a specialization during the course?
  A: You cannot choose a specialization in this course. However, you will be trained in areas such as Natural Language Processing (NLP), computer vision, and reinforcement learning.
• Q: How will my progress be evaluated?
  A: Your progress will be evaluated through a combination of quizzes, hands-on exercises, and a final assessment. These evaluations are designed to test your understanding and application of the material.

Certificate Features:

• High-Quality Video, E-book & Audiobook
• Modules Quizzes
• AI Mentor
• Access for Tablet & Phone
• Online Proctored Exam with One Free Retake
• LABs Practices