Predictive Analytics and Machine Learning

KEY FACTS
Accreditation:	CPD - Continuing Professional Development
Duration:	30 hours
Campus:	London
Intake Dates:	February, May and September*
Timetables:	Click here for timetables
Tuition Fees:	£1,150
For Frequently Asked Questions	Click here

Machine learning concerns the study and application of algorithms and statistics in order to get machines to solve new problems that haven’t previously occurred, and then to ‘learn’ these problems and solve them in the future, should they return.

On this module, learners can expect an overview of the mathematical foundations of machine learning and its workflow. You will also learn about predictive analysis as this is the basis of machine learning.

You will learn by completing technical exercises and collaborating with fellow students on projects.

Course Structure

Course focus

Differences between Data Science, Artificial Intelligence, Machine Learning, Deep Learning

Types of Machine Learning

ML lifecycle and design (obtain – clean – train – score – predict)

ML Mathematical and statistical method overview: regression, k-means, trees and forests

Python notebooks and libraries

Two-class classification

Multi-class classification

Key Benefits

Comprehend fundamental ML concepts: how data can reveal patterns, correlations, associations and, eventually, predictions
Know the different types of ML, where each is applicable and their capabilities and limitations
Grasp mathematical concepts from statistics that are closely related to machine learning
ML in practice: code and evaluate algorithms
Understand the design of a machine learning project, why this can optimise performance and increase business competitiveness. Identify cases where ML has offered automation and better decisions