/Machine Learning and Predictive Analytics - Professional course
Provided by: UWE
Course Area: South West
Course Code: Z41000092
Course Type: Short Course
Start date: 20210512
Subjects: Artificial Intelligence, Data Analytics, Data Science, Machine Learning, Python
Price: £500 to £2,000
Delivery Method: Face to face
Course Overview
Learning and Teaching
The module is delivered through weekly combined lecture and tutorial sessions. Each session will direct the course and introduce the new ideas and skills required. Then tutorial sessions will enable you to carry out the study and research exercises described in the associated work-sheet under the guidance of a Tutor. The teaching material will be made available from Blackboard (our online learning environment). A course text is also recommended.
Assessment
The module will be assessed though a coursework project and a written exam.
Who Is This Course For
Our professional courses will bring you up to date with current information, science and technology trends, and are offered as individual stand-alone modules or can be used to build up credit towards a named postgraduate qualification (PG Certificate, PG Diploma or Masters) within our Information Management and Information Technology Awards.
What Will I Learn
Course Content includes a detailed examination of the following topics:
Defining predictive analytics, business relevance and applications pattern recognition, classification, optimisation and big data, sources of data and value of knowledge, marketing and recommender systems, fraud detection, business process analytics, credit-risk modelling, web analytics, social media and human behaviour analytics, predictive models, survival models, descriptive models, inferring data and data visualisation, briefing learning and regression approaches, machine learning, learning problems (classification, clustering and reinforcement) and literature, learning approaches – supervised, unsupervised and reinforcement, machine learning techniques and genetic algorithms, analytics in the context of big data, predictive analytics as art and science, software tools; the R project and python, trends and challenges in predictive analytics.
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