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AI+ Developerā„¢ ā€“ Instructor-Led Programme Delivery

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Industry Growth: Equipping Developers to Create Intelligent, Scalable Solutions

  • he 2024 Stack Overflow Developer Survey indicates that approximately 82% developers are currently using AI tools to write code.ā€¯ (Statista)
  • Designing AI-driven applications to improve system performance and solve real-world problems using advanced programming techniques.
  • Leveraging deep learning and optimization techniques to enhance model accuracy and efficiency, leading to smarter AI solutions.
  • Specializing in NLP, computer vision, and reinforcement learning to build sophisticated AI models tailored to various industry needs.
  • As AI-driven development roles expand rapidly, there is a growing demand for developers with AI expertise, leading to high-paying job opportunities globally.

Skills Youā€™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

What You'll Learn

  1. Course Introduction
  1. 1.1 Introduction to AI
  2. 1.2 Types of Artificial Intelligence
  3. 1.3 Branches of Artificial Intelligence
  4. 1.4 Applications and Business Use Cases
  1. 2.1 Linear Algebra
  2. 2.2 Calculus
  3. 2.3 Probability and Statistics
  4. 2.4 Discrete Mathematics
  1. 3.1 Python Fundamentals
  2. 3.2 Python Libraries
  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
  1. 5.1 Neural Networks
  2. 5.2 Improving Model Performance
  3. 5.3 Hands-on: Evaluating and Optimizing AI Models
  1. 6.1 Image Processing Basics
  2. 6.2 Object Detection
  3. 6.3 Image Segmentation
  4. 6.4 Generative Adversarial Networks (GANs)
  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)
  1. 8.1 Introduction to Reinforcement Learning
  2. 8.2 Q-Learning and Deep Q-Networks (DQNs)
  3. 8.3 Policy Gradient Methods
  1. 9.1 Cloud Computing for AI
  2. 9.2 Cloud-Based Machine Learning Services
  1. 10.1 Understanding LLMs
  2. 10.2 Text Generation and Translation
  3. 10.3 Question Answering and Knowledge Extraction
  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
  1. 12.1 Communicating AI Projects
  2. 12.2 Documenting AI Systems
  3. 12.3 Ethical Considerations
  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents