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CAVE HILL HOME > Lifelong Learning > Courses > Short Courses > Business and Data Analytics > Applied Machine Learning and Data Mining using Matlab and Python

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Business and Data Analytics

Applied Machine Learning and Data Mining using Matlab and Python

Overview

This course in data mining is designed to equip you with the knowledge and skills needed to understand, analyse, and derive insights from vast stores of digital information assets using Matlab and Python. It will prepare you to be competent in the world of “Big Data”, and will help you to develop a good understanding of the basic concepts, principles and techniques of data mining.

Mode of Delivery: Face-to-Face Online

What will I Learn?

On successful completion of the course, delegates should be able to:

  • Define and interpret the core concepts and terminologies;
  • Justify the application of various supervised learning techniques to solve real-world data science problems;
  • Critically evaluate key supervised learning techniques;
  • Analyse large datasets using a range of supervised learning techniques;
  • Design a decision support system which applies supervised learning methods to real-world date;
  • Generate and use effective supervised learning solutions to solve challenging data science problems.
  •  

Who Should do this Course

Computer Scientists, Marketing and Business Management Professionals and Data Analysts.

Important Information

This course has been offered at a special rate, CLICK HERE to view Terms and Conditions
 
 

At a Glance

  • Admissions Term: 2020/2021 Semester I
  • Date: October 12th - 25th, 2020
  • Time: Monday to Thursday, 5pm-8pm; Saturday 10am-1pm
  • Duration: 2 weeks (30 Hours)
  • Certificate Awarded: Professional Development Certificate of Competence
  • Course Code: PDLL102
  • CEUs: 3
  • Capacity: 20
  • Cost: BDS $2,690 (US $1,345) BDS$ 500.00 (US $250.00)

The following topics will be addressed:
  • Introduction to Supervised Learning
  • Classification and Regression
  • Kernel Methods and Support Vector Machines (SVMs)
  • Decision Trees
  • Na├»ve Bayes Classifier
  • Neural Networks
  • K-means
  • Recommender systems
  •  

No prior knowledge is required or assumed in this course.

This course consists of two two-hour lectures and two one-hour tutorials per week.

Terry Harris, PhD

Dr. Terry Harris is currently an Assistant Professor at Durham University Business School and has a BSc in Computer Science and Accounting and a Master’s degree in Computer Science from the University of the West Indies. Over his academic career, Terry has taught extensively in the fields of Accounting, Finance and Computer Science.


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