O B J E C T I V E  ///

AI ADVANCE provides advanced insights into AI models, including neural networks and deep learning, with a focus on understanding and optimizing complex models.

^ Our educational framework for this course is presently in development. We will update this page when this product and service is available for purchase. Course Topics and Deliverables are subject to change until finalised.

C O U R S E    T O P I C S  ///

• • Deep Dive into Neural Networks (layers, activations, architectures) 

  • Advanced Machine Learning Models: Decision Trees, Random Forests, and SVMs 

  • Introduction to Deep Learning (CNNs, RNNs, GANs)  

  • Model Optimization: Hyperparameter Tuning and Evaluation Metrics 

  • AI for Complex Data Types (text, audio, video processing)  

  • Introduction to Transfer Learning and Pre-trained Models 

  • Exploring Agentic AI (AI Agents) and Basic Agent Design 

  • Hands-On Activity: Building and Evaluating a Deep Learning Model 

            COURSE CODE :  PER-ADV-003

  COURSE DELIVERY :  ONLINE ONLY

COURSE DURATION : 1 DAY / 6 HRS

                     AVAILABLE :  TBA

W H A T    Y O U    W I L L    L E A R N  ///

In general, each course’s outcomes are crafted to ensure participants leave with a comprehensive understanding of the material, supported by hands-on experience and knowledge to apply concepts in real-world settings.

D E L I V E R A B L E S  ///

• • Deep Dive into Neural Networks

Outcome: Grasp neural network architecture and functionality, understanding layers, activations, and model behavior.

• • Advanced Machine Learning Models: Decision Trees, Random Forests, SVMs

Outcome: Identify advanced models and apply them to solve complex classification and regression problems.

• • Introduction to Deep Learning

  • Outcome: Gain familiarity with CNNs, RNNs, and GANs, and recognize their applications in image and sequence data.

  • Model Optimization: Hyperparameter Tuning, Evaluation Metrics

Outcome: Optimize models by adjusting hyperparameters and using appropriate metrics to measure success.

• • AI for Complex Data Types

Outcome: Understand how AI can process text, audio, and video, recognizing data processing techniques for each.

• • Introduction to Transfer Learning and Pre-trained Models

Outcome: Utilize pre-trained models and apply transfer learning to accelerate model building and improve performance.

• • Exploring Agentic AI (AI Agents)

Outcome: Learn the basics of AI agents and apply introductory concepts to simple automation scenarios.

• • Hands-On Activity

Outcome: Build and evaluate a deep learning model, applying complex AI concepts practically.