The course provided me with a solid understanding of IT service desk operations and troubleshooting techniques. It has improved my ability to handle user issues with confidence and professionalism.
AI+ Developer Practitioner™
AT-310
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
- High-Quality Video, E-book & Audiobook
- Modules Quizzes
- AI Mentor
- Access for Tablet & Phone
- Online Proctored Exam with One Free Retake
- Hands-on Practices
Why This Certification Matters
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Master Key AI Development Skills: Learn Python, deep learning, advanced concepts, and optimization techniques to build robust AI solutions.
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Specialize in Cutting-Edge AI Domains: Gain expertise in NLP, computer vision, or reinforcement learning, alongside data processing, exploratory analysis, and time series analysis.
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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.
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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.
At a Glance: Course + Exam Overview
Program Name
AI+ Developer Practitioner™
Included
Instructor-led OR Self-paced course + Official exam + Digital badge
Prerequisites
Basic math, computer science fundamentals, fundamental programming skills
Exam Format
50 questions, 70% passing, 90 minutes, online proctored exam
Delivery
Online labs, projects, case studies
Outcome
Industry-recognized credential + hands-on experience
Who Should Enroll?
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Software Developers: Enhance your coding expertise by mastering AI algorithms and deep learning techniques.
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Data Enthusiasts: Apply AI-driven data analysis, machine learning models, and deep learning to solve complex problems.
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Computer Vision & NLP Researchers: Dive into specialized AI fields, including computer vision and natural language processing.
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IT Specialists & System Architects: Integrate AI solutions into existing systems and optimize performance.
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Students & Fresh Graduates: Build a strong foundation in AI development and prepare for future opportunities in tech.
Skills You'll Gain
Python for AI DevelopmentAdvanced Mathematics and StatisticsOptimization TechniquesDeep Learning FundamentalsData Processing and Exploratory AnalysisNLP, Computer Vision, or Reinforcement Learning SpecializationTime Series AnalysisModel Explainability and Deployment
What You'll Learn
- 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
- 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
Tools You'll Master
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.
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via AI proctored exam platform (flexible scheduling)
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%
Choose the Format That Fits Your Schedule
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
Instructor-Led (Live Virtual/Classroom)
- 5 days (live or virtual) Self-Paced: 40 hours of content
- Real-time Q&A, peer collaboration, and hands-on labs
- Led by AI Certified Trainers and delivered through Authorized Training Partners
Self-Paced Online
- 40 hours of content
- Learn anywhere, anytime, with modular quizzes to track progress
Need More Flexibility?
To help you along your way, we can help you set up a customizable payment plan. Just book a call, we’ll provide you with the details!
Let's Arrange a Call9/10 of our learners achieve their learning objectives after successful completionProcess Advisors
The course gave me a clear understanding of network security concepts and practical exposure. It helped me build confidence in handling real-world security tasks.
My path to cloud architecture had its hurdles, but Fortray was there every step of the way. They provided valuable insights into cloud technologies and architectural design. With their guidance, I turned challenges into opportunities, and today, as a Cloud Architect, I help organizations leverage the cloud for their growth and success.
The path to becoming a Cybersecurity Engineer felt like decrypting an impossible algorithm—until Fortray provided the key. Their mentorship was like a robust IPS, detecting and eliminating my weaknesses while optimizing my strengths. Now, I stand as a protector in the ever-evolving IT landscape, proud to carry their legacy forward.
This course strengthened my full-stack development skills and practical understanding. I am more confident in building and managing complete web applications.
This course improved my understanding of project management methodologies and workflows. I gained practical skills to plan and execute projects effectively.
The course provided valuable hands-on experience in data analysis tools and techniques. It enhanced my ability to extract meaningful insights from data.
The training helped me build strong analytical skills and understand how to work with real-world data science problems effectively.
The course helped me understand automation testing frameworks and best practices. I gained practical experience in writing and executing automated test cases.
Frequently Asked Questions
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.
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.
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.
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.
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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