AI+ Developer Practitioner™

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

Certificate Code: AT-310

Duration: 40 hours (5 Days)

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Course Metrics Prerequisites Curriculum Outcomes Opportunities Salary Comparison FAQs Enroll

13+

Modules

40 hours (5 Days)

Duration

50 MCQs, 90 Minutes

Examination

70% (35/50)

Passing Score

About This Certification

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

AI Fundamentals: Understand basic AI concepts, machine learning, deep learning, generative AI, natural language processing, and their applications in software development.
Programming Fundamentals: Understand basic programming concepts, Python syntax, variables, data types, functions, control flow, data structures, and software development practices.
Data Literacy: Understand data handling concepts, data preparation, data cleaning, visualization, and how structured data supports AI workflows.
Mathematics and Statistics Awareness: Understand basic mathematical and statistical concepts, including variables, functions, vectors, probability, distributions, and evaluation metrics used in AI development.
Technology Awareness: Recognize AI models, APIs, generative AI applications, retrieval systems, AI agents, deployment tools, and emerging AI development technologies.
Responsible AI Understanding: Understand AI security, responsible AI principles, model evaluation, human oversight, privacy considerations, and practices required for developing reliable and trustworthy AI applications.

Tools Covered

GitHub Copilot

Lobe

H2O.ai

Snorkel

Course Curriculum

13 Modules 40 hours (5 Days)

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

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%

Learning Outcomes

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.

Salary Potential

Without AI Skills

$77,311

With AI Skills

$125,439

62% Higher Earning Potential

Frequently Asked Questions

What will I gain from completing this certification?

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.

Do I need any prior AI knowledge to join this course?

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.

Are there any hands-on projects in the course?

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.

Can I choose a specialization during the course?

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.

How will my progress be evaluated?

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.

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