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Monday to Friday: 10AM - 6PM
Weekend: CLOSED
AI+ Developer Practitioner™
USD$495.00 (GST excl.)
Formerly known as AI+ Developer™
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
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Duration
Instructor-Led: 5 days (live or virtual)
Self-Paced: 40 hours of content
Self-Paced: 40 hours of content
Format
Online, self-paced
Exam
50 questions, 70% passing, 90 minutes, online proctored exam
Overview
- 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
Prerequisites
Basic math, computer science fundamentals, fundamental programming skills
What you’ll learn
- 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.
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: 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.
Tools you’ll use
GitHub Copilot
Lobe
H2O.ai
Snorkel
Learning Modules
13 modules
1Module 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
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
2Module 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
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
3Module 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
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
4Module 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
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
5Module 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
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
6Module 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
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
7Module 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
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
8Module 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
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
9Module 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
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
10Module 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
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
11Module 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
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
12Module 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
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
13Optional 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
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
| Format | Online, self-paced |
|---|---|
| Duration | Instructor-Led: 5 days (live or virtual) |
| Exam | 50 questions, 70% passing, 90 minutes, online proctored exam |
| Included | Instructor-led OR Self-paced course + Official exam + Digital badge |
| Prerequisites | Basic math, computer science fundamentals, fundamental programming skills |
| Delivery | Projects & case studies |
| Outcome | Industry-recognized credential + hands-on experience |
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