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Course Details
Topic 1: R Fundamental for Data Analytics
Topic 1.1 Getting Started in R
- What is R
- Install R and RStudio IDE
- Explore RStudio Interface
- R Script
- Comment
Topic 2.1 Data Types
- Numbers
- String
- Vector
- Matrix
- Array
- Data Frame
- List
- Factor
Topic 1.3 R Packages & Datasets
- Import R Packages
- Import R Data Sets
- Import External Data
- Export Data
Topic 1.4 Data Visualization
- Scatter Plot
- Boxplot
- Bar chart
- Pie chart
- Histogram
Topic 1.5 R Programming
- Control Structures
- Loop
- Break & Next
- Function
Assessment for R Fundamental
Topic 2: Machine Learning with R
Topic 2.1 Overview of Machine Learning
- Introduction to Machine Learning
- Pattern Recognition Problems Suitable for Machine Learning
- Supervised vs Unsupervised Learnings
- Types of Machine Learning
- Machine Learning Techniques
- R Packages for Machine Learning
Topic 2.2 Regression
- What is Regression
- Applications of Regression
- Least Square Error Minimization
- Data Pre-processing
- Bias vs Variance Trade-off
- Regression Methods with Regularization
Topic 2.3 Classification
- What is Classification
- Applications of Classification
- Classification Algorithms
- Confusion Matrix
- Classification Performance Evaluation
Topic 2.4 Clustering
- What is Clustering
- Applications of Clustering
- Distance Measure
- Clustering Algorithms
- Clustering Performance Evaluation
- Anomaly Detection Problem
Topic 2.5 Principal Component Analysis
- Principal Component Analysis (PCA) and Dimension Reduction
- Applications of PCA
- PCA Workflow
Topic 3: Advanced Machine Learning with R
Topic 3.1 Neural Network
- What is Neural Network
- Activation Functions
- Deep Learning vs Machine Learning
- Classification Using Neural Network
Topic 3.2 Ensemble Methods
- Random Forest Ensemble
- Gradient Boost and XGBoost Ensemble
- Stacking Ensemble
Topic 3.3 Hyperparameter Tuning
- Exhaustive Grid Search
- Random Search
Assessment for R Machine Learning
Course Info
Promotion Code
Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
Target Age Group: 18-65 years old
Minimum Software/Hardware Requirement
Software:
TBD
Hardware: Window or Mac Laptops
Job Roles
- NSF or Full Time Students
- Data Analysts
- Marketeers
Trainers
Dwight Nuwan Fonseka: Dwight Nuwan Fonseka is a ACLP certified trainer. He have a degree in Biotechnology from NUS ,Advanced diploma in Pharmaceutical management from MDIS and Masters in Education from NTU. He have 8 years experience of teaching biology at O and A levels/ IB level in international schools in Singapore and overseas.
Marcel Leng: Marcel Leng is a ACTA certified. Marcel graduated with majors in Applied Mathematics and Physics from the National University of Singapore.
His core specialisation skills are R, Python, Machine Learning, Statistical Analysis, and Data Visualisation in Tableau. His current interests include Machine Learning, Deep Learning, Artificial Intelligence, Internet of Things, Robotics and Programming.
Customer Reviews (10)
- might recommend Review by Course Participant/Trainee
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. (Posted on 5/4/2023)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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. (Posted on 2/8/2023)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recmmend Review by Course Participant/Trainee
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. (Posted on 2/8/2023)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - might recommend Review by Course Participant/Trainee
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. (Posted on 4/1/2021)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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The teacher was very helpful. (Posted on 12/19/2020)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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. (Posted on 11/11/2020)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - might recomemnd Review by Course Participant/Trainee
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Could space out the lessons more (eg; 4-5 lessons instead of 3) (Posted on 6/27/2020)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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. (Posted on 5/31/2020)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Marcel is a very dedicated and patient tutor Review by Course Participant/Trainee
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Have a day gap in between for productivity, 3 days in a row is too much.1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
Marcel is a very dedicated and patient tutor, 100% recommend him to anyone. Very very thankful. :) (Posted on 4/10/2020) - Will Recommend Review by Course Participant/Trainee
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. (Posted on 9/12/2019)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
