Duration
3 days
OVERVIEW
This course is an introductory course for participants who are interested in understanding the different types of Machine Learning and to enable the participants to have some real-world hands-on experience with Machine Learning techniques.
AUDIENCE
This short course intended audiences are IT professionals, aspiring Data Scientists and professionals who want to learn about Machine Learning. The programme provides an overview of the common Machine Learning algorithms and their corresponding suitability to solve various problems. Hands-On will be provided using the R programing language.
PREREQUISITES
Basic R programming knowledge will be required to effectively appreciate the various Data Mining techniques.
OBJECTIVES
After completing this module, participants will learn the following
- Differentiate between supervised and un-supervised learning.
- Understand the issue of data quality.
- Employ the right machine learning techniques for respective problems.
- Deploy R language to conduct machine learning.
- Basic ability to explain the outcome of the Machine Learning.
COURSE OUTLINE
The course content will be delivered according to this schedule (E.g. typically a short course has a maximum of 7 hours per day, 9:00 am to 5:00 pm with an hour for lunch)
DAY1
0900-1000
Introduction to Machine Learning
1000-1040
R Machine Learning libraries
1040-1100
Morning Break
1100-1200
Association Rule Learning
1200-1300
Hands-On: Apriori for market basket analysis
1300-1400
Lunch
1400-1500
Regression – Linear and Logistical
1500-1540
Hands-On: Scoring systems
1540-1600
Afternoon Break
1600-1700
Laboratory – Propensity model
DAY2
0900-1000
Supervised vs Unsupervised Learning
1000-1040
Clustering (Centroid vs Density Based)
1040-1100
Morning Break
1100-1200
Hands-On: Segmentation using Clustering
1200-1300
Classification (e.g. SVM, Naïve Bayes, ANN)
1300-1400
Lunch
1400-1500
Hands-On: Credit scoring
1500-1540
Hands-On: Sentiment Analysis
1540-1600
Afternoon Break
1600-1700
Laboratory – Churn analysis
DAY3
0900-1000
Time Series
1000-1040
Moving Averages
1040-1100
Morning Break
1100-1200
Auto-Regressive Moving Averages
1200-1300
Hands-On: Forecasting with ARMA
1300-1400
Lunch
1400-1500
Auto-Regressive Integrated Moving Averages (ARIMA), concept of stationary data
1500-1540
Hands-On: Real example of ARIMA and selection of the parameters
1540-1600
Afternoon Break
1600-1700
Laboratory exercise - TBD
Programme scope, delivery arrangements and suitability are confirmed during enquiry.