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Course Details
Module 1: Introduction to Machine Learning
- What is Machine Learning (ML)
- R packages for ML
- Installing R ML Packages
Module 2: Datasets
- Iris Dataset
- Mtcars Dataset
- Boston Housing Price Dataset
- Features
- Splitting Datasets for Training/Testing
Module 3: Supervised Learning
- What is Supervised Learning
- Performance Metric
- Decision Tree Classifier
- Random Forest Classifier
- Gaussian Navie Bayes (GNB) Classifier
- Support Vector Machine (SVM) Classifier
- Logistic Regression Classifier
- K-Nearest Neighbors (KNN) Classifier
- Linear Regression: Single and Multivariate,
- Variable Selection
- KNN Regression
Module 4: Unsupervised Learning
- What is Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Dimensionality Reduction
Module 5: Intro to Neural Network (Optional)
- What is Neural Network
- Multi Layer Perceptron
Who Should Attend
- Data analysts who like build R machine learning models for data analysis such as classification, clustering
- Financial analysts who use R machine learning for predicting market trend
- Marketeers who use R machine learning models for market analysis
- Researchers who want to learn R machine learning for their research topics
Prerequisite
This course assumes some basic knowledge of the R language and data science.
Trainers
Ravi Kumar Tiwari got his PhD from NUS (Chemical Engineering) in 2013. After graduation, he worked 3 years as a research scientist in the Institute of High Performance Computing (IHPC). He is currently a big data R data analyst in Rakuten. His core skills are R, big data, Hadoop and machine learning.
Dwight Nuwan Fonseka have a degree in Biotechnology (from NUS) ,Advanced diploma in Pharamceutical 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.
Zhu Tianming did her PhD in NUS, major in Statistics, and will graduate in August 2017. She has been a part-time teaching assistant for more than three years in NUS. She has taught the modules related to probability, regression analysis, categorical data analysis and multivariate statistical analysis. Her research interests are functional data classification and her core skills are R, machine learning and statistical analysis.
Zhang Liang is a PhD candidate of Statistics at NUS, his research is mainly on developing statistical methods of hypothesis testing for high-dimensional data, and application to biostatistical problems. He has a couple of years experience using MATLAB and R for his research and real data analysis.
Wesley Goi is currently in his final year of his PhD in Bioinformatics at National University of Singapore (NUS) where he previously received his degree in Molecular Biology at NUS (Honours 2nd Uppers). He was the TA for Introductory Bioinformatics LSM2241. He specialises in analysing high throughput DNA and RNA sequencing data of complex microbial communities using network analyses and various functional analyses. In his previous projects he has applied machine learning methods to vaccine discovery.
Dr. Shailey Chawla is an experienced academician and researcher with over 10 years of experience in teaching and research. Her PhD research topic was on Web Requirements Engineering. She has published various research papers and book chapters in reputed international journals and conferences. Her recent research has been in Big Data Analytics at Hong Kong Polytechnic University where she worked on Urban Data Analytics and Educational Data Mining. She is proficient in R, Python, Java, Advanced Excel, Data Mining, C, Linux programming among various other core computing subjects.
Customer Reviews (8)
- Will RecommendReview by HH ONG
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The course was effective in providing a basic understanding of various ML techniques. Trainer was knowledgable, explained content well, and was patient in answering participants' questions. (Posted on 8/21/2017)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 RecommendReview by Adriel Cheng
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Summarize key methods or approaches and specify in summary what type of data can be used for each method at the last portion of the training. (Posted on 8/21/2017)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 RecommendReview by Chang Wei Lin
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Water in classroom (Posted on 8/21/2017)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 RecommendReview by Chia Sin Fei
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Nil (Posted on 6/7/2017)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 RecommndReview by Marie Wong
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Less repetitive things like slicing data and more in depth about which situations you would use the different method (Posted on 2/26/2017)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 recommendReview by Dwight
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Nil (Posted on 7/31/2016)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 - Contents coveredReview by LIM KAH KHENG
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Awaiting for full day course purely on SHINY APPS (Posted on 7/31/2016)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 - excellentReview by lkk
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Very good course for those who already has R background.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
Trainer has simplify the ML codes using shiny apps to demo the concepts.
awaiting for Programming for Shiny - if the school has such course (Posted on 7/31/2016)
