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AI+ Developer™

  • 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
Enroll Now
AI+ Developer™
Self-Paced Online
USD $ 540.00
Instructor-Led Online
USD $ 4250.00

At a Glance: Course + Exam Overview

Our training approach is human‑centred and outcomes‑driven. We focus on what learners can apply confidently.

Category
AI Development
Software Developer
Robotics Engineer
Quantum Computing Specialist
Quality Assurance Engineer
Machine Learning Scientist
Language
English
DevOps Engineer
Blockchain Developer
All Courses
AI System Engineer
AI Agent Developer
AI Technical
User Experience Engineer
Program Name
AI+ Developer™
Duration
  • Instructor‑Led: 5 Days
  • Self‑Paced: 40 hours of content
Prerequisites
    • Basic math, including familiarity with high school-level algebra and basic statistics, is desirable. 
    • Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential. 
    • A fundamental knowledge of programming skills is required. 
Exam Format
Exam details not available.

What You'll Learn

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.

Certification Modules

Course Overview

  1. Course IntroductionPreview

Module 1: Foundations of Artificial Intelligence

  1. 1.1 Introduction to AI Preview
  2. 1.2 Types of Artificial Intelligence Preview
  3. 1.3 Branches of Artificial Intelligence
  4. 1.4 Applications and Business Use Cases

Module 2: Mathematical Concepts for AI

  1. 2.1 Linear Algebra Preview
  2. 2.2 Calculus Preview
  3. 2.3 Probability and Statistics Preview
  4. 2.4 Discrete Mathematics

Module 3: Python for Developer

  1. 3.1 Python Fundamentals Preview
  2. 3.2 Python Libraries

Module 4: Mastering Machine Learning

  1. 4.1 Introduction to Machine Learning
  2. 4.2 Supervised Machine Learning Algorithms
  3. 4.3 Unsupervised Machine Learning Algorithms
  4. 4.4 Model Evaluation and Selection

Module 5: Deep Learning

  1. 5.1 Neural Networks
  2. 5.2 Improving Model Performance
  3. 5.3 Hands-on: Evaluating and Optimizing AI Models

Module 6: Computer Vision

  1. 6.1 Image Processing Basics
  2. 6.2 Object Detection
  3. 6.3 Image Segmentation
  4. 6.4 Generative Adversarial Networks (GANs)

Module 7: Natural Language Processing

  1. 7.1 Text Preprocessing and Representation
  2. 7.2 Text Classification
  3. 7.3 Named Entity Recognition (NER)
  4. 7.4 Question Answering (QA)

Module 8: Reinforcement Learning

  1. 8.1 Introduction to Reinforcement Learning
  2. 8.2 Q-Learning and Deep Q-Networks (DQNs)
  3. 8.3 Policy Gradient Methods

Module 9: Cloud Computing in AI Development

  1. 9.1 Cloud Computing for AI
  2. 9.2 Cloud-Based Machine Learning Services

Module 10: Large Language Models

  1. 10.1 Understanding LLMs
  2. 10.2 Text Generation and Translation
  3. 10.3 Question Answering and Knowledge Extraction

Module 11: Cutting-Edge AI Research

  1. 11.1 Neuro-Symbolic AI
  2. 11.2 Explainable AI (XAI)
  3. 11.3 Federated Learning
  4. 11.4 Meta-Learning and Few-Shot Learning

Module 12: AI Communication and Documentation

  1. 12.1 Communicating AI Projects
  2. 12.2 Documenting AI Systems
  3. 12.3 Ethical Considerations

Optional Module: AI Agents for Developers

  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents

Finish the course and get certified

certificate

Industry Opportunities

Opportunity Image

AI Machine Learning Developer

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

Opportunity Image

AI Solutions Architect

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

Opportunity Image

AI Application Developer

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

Opportunity Image

AI System Programmers

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

Frequently Asked Questions

What will I gain from completing this certification?
Upon completion, you will receive an AI+ Developer™ 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.

Prerequisites

  • Basic math, including familiarity with high school-level algebra and basic statistics, is desirable. 
  • Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential. 
  • A fundamental knowledge of programming skills is required. 

Exam Details

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

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%
Self-Paced Online

Self-Paced: 40 hours of content

USD $ 540.00
Purchase Self-Paced Course
Instructor-Led Online

Instructor-Led: 5 days (live or virtual)

USD $ 4250.00
Purchase Instructor-Led Course

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