Duration
5 days
Course Description
This course aims at providing participants with an overview of techniques and tools for machine learning and deep learning using R.
Delivery Method
This course is covered through lectures, active participation/hands-on, guided group discussions, demonstrations.
Organisational Impact
The knowledge gained from this course will prepare participants to embark on R Programming.
Organizational Benefits
To provide employees of the data analysis department
- a systematic approach to use R as alternative analytics tools in both research and commercial world.
- cost reduction because R is open source.
Personal Impact
The employee should be able to
- utilize different data mining techniques for different scenario
- apply R into solving data mining challenges
- visualizing data mining results
Competencies Emphasized
- Fundamental R Programming
- Manipulating data with R
Learning Objectives
After completing this module, participants will learn the following
- Identify different data mining techniques for different problems.
- Define steps in data mining
- Cleaning and handling with missing data
- Apply different data mining techniques using R.
DAY1
0900 – 0940
Identifying the right data mining techniques
0940 – 1040
The “must” phases in Data Mining
1040 – 1100
Morning Break
1100 – 1200
Downloading and opening files, Merging records and Data frame
1200 – 1300
Subsetting and Sorting Data
1300 – 1400
Lunch
1400 – 1500
Find and replace data.
1500 – 1540
Dates and handling missing data
1540 – 1600
Afternoon Break
1600 – 1700
Report writing: data analysis and findings
DAY2
0900 – 0940
Linear Regression
0940 – 1040
R and Linear Regression – Part I
1040 – 1100
Morning Break
1100 – 1200
Multiple Linear Regression
1200 – 1300
R and Multiple Linear Regression – Part II
1300 – 1400
Lunch
1400 – 1500
Compare models
1500 – 1540
Selecting the “best” model
1540 – 1600
Afternoon Break
1600 – 1700
Report writing: critical analysis on features
DAY3
0900-1000
Introduction to Classification, Discretization, Cross Validation
1000-1040
Installing R packages for Classification
1040-1100
Morning Break
1100-1200
Feature selection
1200-1300
K-Nearest Neighbour algorithm
1300-1400
Lunch
1400-1500
Naïve Bayes Classifier algorithm
1500-1540
R and Naïve Bayes Classifier
1540-1600
Afternoon Break
1600-1700
Report writing: writing classification results
DAY4
0900-1000
R and CART
1000-1040
Hands-on with CART
1040-1100
Morning Break
1100-1200
Introduction to Association Rules
1200-1300
R and Association Rules
1300-1400
Lunch
1400-1500
Cluster analysis with R
1500-1540
Experimenting with K-means algorithm and Hierarchical clustering
1540-1600
Afternoon Break
1600-1700
Reporting results
DAY5
0900-1000
Introduction to Deep Learning
1000-1040
Applications of Deep Learning
1040-1100
Morning Break
1100-1200
Neural Networks Basics
1200-1300
Shallow Neural Networks
1300-1400
Lunch
1400-1500
Deep Neural Networks – basics, optimization, regularization
1500-1540
Convolutional Neural Networks
1540-1600
Afternoon Break
1600-1700
Hands-on
Programme scope, delivery arrangements and suitability are confirmed during enquiry.