P E R S O N A L E D I T I O N
A I I N T E R M E D I A T E
S$ 550
IN DEVELOPMENT
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
" SOMEWHERE, SOMETHING INCREDIBLE IS WAITING TO BE KNOWN "
C A R L S A G A N
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.