About the AI+ Developer™
Skills You’ll Gain
- Python for AI Development
- Advanced Mathematics and Statistics
- Optimization Techniques
- Deep Learning Fundamentals
- Data Processing and Exploratory Analysis
- NLP, Computer Vision, or Reinforcement Learning Specialization
- Time Series Analysis
- Model Explainability and Deployment
Course Information
Course Code
AI+ Developer™
Duration
40 hours of content
Delivery
Self Paced
Exam Voucher
Included
Course Curriculum
- Course Introduction
- 1.1 Introduction to AI
- 1.2 Types of Artificial Intelligence
- 1.3 Branches of Artificial Intelligence
- 1.4 Applications and Business Use Cases
- 2.1 Linear Algebra
- 2.2 Calculus
- 2.3 Probability and Statistics
- 2.4 Discrete Mathematics
- 3.1 Python Fundamentals
- 3.2 Python Libraries
- 4.1 Introduction to Machine Learning
- 4.2 Supervised Machine Learning Algorithms
- 4.3 Unsupervised Machine Learning Algorithms
- 4.4 Model Evaluation and Selection
- 5.1 Neural Networks
- 5.2 Improving Model Performance
- 5.3 Hands-on: Evaluating and Optimizing AI Models
- 6.1 Image Processing Basics
- 6.2 Object Detection
- 6.3 Image Segmentation
- 6.4 Generative Adversarial Networks (GANs)
- 7.1 Text Preprocessing and Representation
- 7.2 Text Classification
- 7.3 Named Entity Recognition (NER)
- 7.4 Question Answering (QA)
- 8.1 Introduction to Reinforcement Learning
- 8.2 Q-Learning and Deep Q-Networks (DQNs)
- 8.3 Policy Gradient Methods
- 9.1 Cloud Computing for AI
- 9.2 Cloud-Based Machine Learning Services
- 10.1 Understanding LLMs
- 10.2 Text Generation and Translation
- 10.3 Question Answering and Knowledge Extraction
- 11.1 Neuro-Symbolic AI
- 11.2 Explainable AI (XAI)
- 11.3 Federated Learning
- 11.4 Meta-Learning and Few-Shot Learning
- 12.1 Communicating AI Projects
- 12.2 Documenting AI Systems
- 12.3 Ethical Considerations
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
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All You Need to Know
Who Should Attend
- 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.
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.
Examination
50 questions, 70% passing, 90 minutes, online proctored exam
Frequently Asked Questions
Software developers, data enthusiasts, and computer vision/NLP researchers who want to build practical AI development skills.
Yes — understanding of basic programming concepts like variables, functions, loops, and data structures is essential.
AI algorithms, deep learning techniques, computer vision, and natural language processing.
Basic maths, including high-school-level algebra and statistics, is desirable but not a strict prerequisite.
Yes — it's designed to help build a strong foundation in AI development for those starting their tech careers.