O B J E C T I V E  ///

AI INTERMEDIATE expands participants' knowledge to practical AI applications, focusing on machine learning and data analysis techniques that drive decision-making.

^ 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  ///

• • Introduction to Machine Learning Models (supervised, unsupervised, reinforcement) 

  • Core Algorithms: Regression, Classification, and Clustering 

  • Building a Basic Model: End-to-End Workflow (data, model, evaluation) 

  • Data Collection and Feature Engineering 

  • Introduction to Predictive Analytics and Model Validation 

  • Industry Applications of AI (finance, healthcare, retail) 

  • Basic Intro to Neural Networks and Their Uses 

  • Hands-On Activity: Building a Simple Model in a Low-Code Environment

            COURSE CODE :  PER-INT-002

  COURSE DELIVERY :  ONLINE ONLY

COURSE DURATION : 1 DAY / 5 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  ///

• • Introduction to Machine Learning Models

Outcome: Understand the differences between supervised, unsupervised, and reinforcement learning and when to use each type.

• • Core Algorithms: Regression, Classification, Clustering

Outcome: Identify common algorithms and their applications, preparing to select appropriate models for given problems.

• • Building a Basic Model

Outcome: Follow a step-by-step workflow to build, train, and evaluate a basic machine learning model.

• • Data Collection and Feature Engineering

Outcome: Learn to gather data for a model and extract features, preparing data for more effective model training 

• • Introduction to Predictive Analytics and Model Validation

Outcome: Use predictive analytics techniques to make informed decisions and understand model evaluation.

• • Industry Applications of AI

Outcome: Gain insight into AI use cases across sectors, envisioning AI applications within their own industry.

• • Basic Intro to Neural Networks

Outcome: Understand the fundamentals of neural networks, identifying when they may be appropriate for certain tasks.

• • Hands-On Activity

Outcome: Develop practical skills by building a simple model in a low-code environment, applying course concepts.