AI+ Developer PractitionerTM
A hands-on program covering the tools and technologies behind modern AI systems, from Python and machine learning to LLMs, RAG, and deployment.
Hands-On AI Development Skills for Real-World Systems
- Core AI Foundations:
Python, deep learning, data processing, and algorithm design - Hands-On Projects:
Practical work in NLP, computer vision, and reinforcement learning - Advanced Modules:
Time series analysis, model explainability, and cloud deployment - Industry-Ready Skills:
Design and deploy complex AI systems with confidence
Prerequisites
- AI fundamentals: core concepts in machine learning, deep learning, generative AI, and NLP
- Programming fundamentals: Python syntax, data structures, functions, and software practices
- Data literacy: data preparation, cleaning, visualization, and how structured data supports AI
- Math and statistics awareness: vectors, probability, distributions, and evaluation metrics
- Technology awareness: AI models, APIs, retrieval systems, AI agents, and deployment tools
- Responsible AI understanding: security, model evaluation, human oversight, and privacy
The AI+ Developer Practitioner™ self-paced course includes:
1. 40 hours of on-demand videos, an e-book, and podcasts.
2. Modular quizzes to help learners monitor their progress.
The course allows learners to study at their own pace. After completing the course, learners can take the certification exam online through a secure remote-proctored system.
Why This Certification Matters?
- 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: Build expertise in NLP, computer vision, or reinforcement learning, plus data processing and time series analysis.
- Stay Ahead in AI Development: Organizations need developers who can deploy AI models that solve real-world problems.
- Advance Your Career in AI Development: Demand is growing across tech, finance, and healthcare, positioning you as an AI-driven development leader.
What You'll Learn?
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
Job Roles & Industry Outlook
AI Machine Learning Developer
Designs, implements, and optimizes models that learn from data and make predictions or decisions.
AI Solutions Architect
Designs AI systems that fit existing infrastructure and meet business needs.
AI Application Developer
Builds and maintains AI-driven applications that solve real-world problems.
AI System Programmer
Develops and maintains AI systems and software components that enable intelligent behavior.
Skills You’ll Gain
Tools You’ll Master
Exam Details
Duration
Passing Score
Format
Delivery Method
Exam Blueprint
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