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AI+ Developer Practitioner™

AI+ Developer Practitioner™
  • 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
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Certificate Code

AT-310

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Basic math, computer science fundamentals, fundamental programming skills
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

AI Tools Covered

GitHub Copilot
Lobe
H2O.ai
Snorkel
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