This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket
This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.
Audience
Data scientists
Business analysts
Clients who want to learn about machine learning models
Prerequisites
Knowledge of your business requirements
Objective
Introduction to machine learning models
Taxonomy of machine learning models
Identify measurement levels
Taxonomy of supervised models
Build and apply models in IBM SPSS Modeler
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This course provides the latest concepts, tools and techniques to build and influence the development of a successful data science and machine learning capability. Delivered through an interactive approach utilising the latest tools, participants of this course are exposed to basic techniques
This course explores a collaborative project between the UTS Data Science Institute and Sydney Trains. The objective of the project was to develop a timetable robustness evaluation model using analytical/statistical methods, or machine learning techniques.
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars
Perform Cloud Data Science with Azure Machine Learning course is offered by Wardy IT Solutions. As a Microsoft Partner we’ve been awarded with the Silver Learning competency. Every training session we run is taught by a Microsoft Certified Trainer.
In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and Python.
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