AI+ Developer™

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$174.50

Intermediate

AI+ Developer.

40h

1 Year.

Training 5 or more people?

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
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.

Course Curriculum

Module 1: Foundations of Artificial Intelligence
1.1 Introduction to AI 1.2 Types of Artificial Intelligence 1.3 Branches of Artificial Intelligence 1.4 Applications and Business Use Cases

Module 2: Mathematical Concepts for AI
2.1 Linear Algebra 2.2 Calculus 2.3 Probability and Statistics 2.4 Discrete Mathematics

Module 3: Python for Developer
3.1 Python Fundamentals 3.2 Python Libraries

Module 4: Mastering Machine Learning
4.1 Introduction to Machine Learning 4.2 Supervised Machine Learning Algorithms 4.3 Unsupervised Machine Learning Algorithms 4.4 Model Evaluation and Selection

Module 5: Deep Learning
5.1 Neural Networks 5.2 Improving Model Performance 5.3 Hands-on: Evaluating and Optimizing AI Models

Module 6: Computer Vision
6.1 Image Processing Basics 6.2 Object Detection 6.3 Image Segmentation 6.4 Generative Adversarial Networks (GANs)

Module 7: Natural Language Processing
7.1 Text Preprocessing and Representation 7.2 Text Classification 7.3 Named Entity Recognition (NER) 7.4 Question Answering (QA)

Module 8: Reinforcement Learning
8.1 Introduction to Reinforcement Learning 8.2 Q-Learning and Deep Q-Networks (DQNs) 8.3 Policy Gradient Methods

Module 9: Cloud Computing in AI Development
9.1 Cloud Computing for AI 9.2 Cloud-Based Machine Learning Services

Module 10: Large Language Models
10.1 Understanding LLMs 10.2 Text Generation and Translation 10.3 Question Answering and Knowledge Extraction

Module 11: Cutting-Edge AI Research
11.1 Neuro-Symbolic AI 11.2 Explainable AI (XAI) 11.3 Federated Learning 11.4 Meta-Learning and Few-Shot Learning

Module 12: AI Communication and Documentation
12.1 Communicating AI Projects 12.2 Documenting AI Systems 12.3 Ethical Considerations

Optional Module: AI Agents for Developers
1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents

AI+ Developer™ certification program offers a tailored journey in key AI domains for developers.
Master Python, advanced concepts, math, stats, optimization, and deep learning.
The curriculum covers data processing, exploratory analysis, and allows specialization in NLP, computer vision, or reinforcement learning.
The program includes time series analysis, model explainability, and deployment intricacies.
Upon completion, you'll receive a certification, showcasing your AI proficiency for real-world challenges.

Job Roles & Industry Outlook

Al Solutions Architect

Design and implement Al systems that integrate seamlessly with existing infrastructure to address business needs effectively and enhance system capabilities.

Al Application Developer

Build, design, and maintain Al- driven applications that solve real- world problems, integrating Al technologies for enhanced functionality.

Al System Programmers

Develop and maintain Al systems, including programming algorithms and software components that enable intelligent behavior in machines and applications.

Al Machine Learning Developer

Design, implement, and optimize algorithms and models to enable systems to learn from data and make predictions or decisions.

Exam Information

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)

Fees include

One year of access to course content, including continuous updates. Certification exam. One exam re-take.

Exam Blueprint:

Foundations of Artificial Intelligence (AI) - 5%

Mathematical Concepts for AI - 5%

Python for AI Development - 10%

Mastering Machine Learning - 15%

Deep Learning - 10%

Computer Vision - 10%

Natural Language Processing (NLP) - 15%

Reinforcement Learning - 5%

Cloud Computing in AI Development - 10%

Large Language Models (LLMs) - 5%

Cutting-Edge AI Research - 5%

AI Communication and Documentation - 5%

Explore our Schedules

Oct 4 - Oct 8

GMT 09:00 AM - 05:00 PM

In-person

Doha

880.5$

5 Days

Enroll Now

Sep 27 - Oct 1

GMT 09:00 AM - 05:00 PM

In-person

Dubai

880.5$

5 Days

Enroll Now

Oct 4 - Oct 8

GMT 09:00 AM - 05:00 PM

In-person

Muscat

880.5$

5 Days

Enroll Now

Jul 7 - Jul 15

GMT 06:00 PM- 10:00 PM

LVT

Zoom

643.2$

6 Days

Enroll Now

Nov 30 - Dec 7

GMT 06:00 PM- 10:00 PM

LVT

Zoom

643.2$

6 Days

Enroll Now

Certificate of Completion

AI+ Developer.

$174.50

Intermediate

AI+ Developer.

40h

1 Year.

Training 5 or more people?

AI+ Developer™

Get hands-on with the tools and technologies that power the AI ecosystem.

Course Curriculum

Module 1: Foundations of Artificial Intelligence 1.1 Introduction to AI 1.2 Types of Artificial Intelligence 1.3 Branches of Artificial Intelligence 1.4 Applications and Business Use Cases

Module 2: Mathematical Concepts for AI 2.1 Linear Algebra 2.2 Calculus 2.3 Probability and Statistics 2.4 Discrete Mathematics

Module 3: Python for Developer 3.1 Python Fundamentals 3.2 Python Libraries

Module 4: Mastering Machine Learning 4.1 Introduction to Machine Learning 4.2 Supervised Machine Learning Algorithms 4.3 Unsupervised Machine Learning Algorithms 4.4 Model Evaluation and Selection

Module 5: Deep Learning 5.1 Neural Networks 5.2 Improving Model Performance 5.3 Hands-on: Evaluating and Optimizing AI Models

Module 6: Computer Vision 6.1 Image Processing Basics 6.2 Object Detection 6.3 Image Segmentation 6.4 Generative Adversarial Networks (GANs)

Module 7: Natural Language Processing 7.1 Text Preprocessing and Representation 7.2 Text Classification 7.3 Named Entity Recognition (NER) 7.4 Question Answering (QA)

Module 8: Reinforcement Learning 8.1 Introduction to Reinforcement Learning 8.2 Q-Learning and Deep Q-Networks (DQNs) 8.3 Policy Gradient Methods

Module 9: Cloud Computing in AI Development 9.1 Cloud Computing for AI 9.2 Cloud-Based Machine Learning Services

Module 10: Large Language Models 10.1 Understanding LLMs 10.2 Text Generation and Translation 10.3 Question Answering and Knowledge Extraction

Module 11: Cutting-Edge AI Research 11.1 Neuro-Symbolic AI 11.2 Explainable AI (XAI) 11.3 Federated Learning 11.4 Meta-Learning and Few-Shot Learning

Module 12: AI Communication and Documentation 12.1 Communicating AI Projects 12.2 Documenting AI Systems 12.3 Ethical Considerations

Optional Module: AI Agents for Developers 1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents

Learning Objectives

Resource Center

Target Audience

Job Roles & Industry Outlook

Exam Information

Exam Blueprint:

Explore our Schedules

Certificate of Completion

AI+ Developer.

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Module 1: Foundations of Artificial Intelligence
1.1 Introduction to AI 1.2 Types of Artificial Intelligence 1.3 Branches of Artificial Intelligence 1.4 Applications and Business Use Cases

Module 2: Mathematical Concepts for AI
2.1 Linear Algebra 2.2 Calculus 2.3 Probability and Statistics 2.4 Discrete Mathematics

Module 3: Python for Developer
3.1 Python Fundamentals 3.2 Python Libraries

Module 4: Mastering Machine Learning
4.1 Introduction to Machine Learning 4.2 Supervised Machine Learning Algorithms 4.3 Unsupervised Machine Learning Algorithms 4.4 Model Evaluation and Selection

Module 5: Deep Learning
5.1 Neural Networks 5.2 Improving Model Performance 5.3 Hands-on: Evaluating and Optimizing AI Models

Module 6: Computer Vision
6.1 Image Processing Basics 6.2 Object Detection 6.3 Image Segmentation 6.4 Generative Adversarial Networks (GANs)

Module 7: Natural Language Processing
7.1 Text Preprocessing and Representation 7.2 Text Classification 7.3 Named Entity Recognition (NER) 7.4 Question Answering (QA)

Module 8: Reinforcement Learning
8.1 Introduction to Reinforcement Learning 8.2 Q-Learning and Deep Q-Networks (DQNs) 8.3 Policy Gradient Methods

Module 9: Cloud Computing in AI Development
9.1 Cloud Computing for AI 9.2 Cloud-Based Machine Learning Services

Module 10: Large Language Models
10.1 Understanding LLMs 10.2 Text Generation and Translation 10.3 Question Answering and Knowledge Extraction

Module 11: Cutting-Edge AI Research
11.1 Neuro-Symbolic AI 11.2 Explainable AI (XAI) 11.3 Federated Learning 11.4 Meta-Learning and Few-Shot Learning

Module 12: AI Communication and Documentation
12.1 Communicating AI Projects 12.2 Documenting AI Systems 12.3 Ethical Considerations

Optional Module: AI Agents for Developers
1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents