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R Machine Learning Training

This R machine learning training course will teach you how how to use R to apply powerful machine learning methods and gain an insight into real-world applications. Topics include:

  • R machine learning
  • Supervised and Unsupervised Learning methods
  • Classifications
  • Regression
  • Clustering
  • Neural Networks


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Course Code: CRS-N-0034714

Course Booking

$298.00

Course Date

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Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.


Training Grant and Subsidy

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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
  • Marketers 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

R Programming TrainerDr. 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.

R TrainerDwight 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.

BioinformaticeTrainerDr Brandon Ooi has a Bachelors degree in Computer Engineering and a PhD in Bioengineering from the National University of Singapore. He has published papers on machine learning, bioinformatics and microarray data analysis. He has six years of teaching experience at the polytechnic level and was also involved in the creation and teaching of modules for adult learners.

BioinformaticsTrainerDr Zheng Zejun has seven years’ experience in the data-mining field. Zejun worked as a (Sr.) bioinformatics scientist for six years and currently works as a data scientist manager. Dr. Zheng;s expertise covers machine learning, statistics, algorithm design, bioinformatics and high performance computing. He has published three machine learning algorithms on well-recognized academic journals together with the open source software (CUDA-CRISY, FSOM, DYSC). Zejun is professional with an extensive set of programming languages, including C/C++, Python, R, JAVA and flask for data driven analytics and scalable computing. He has also a broad knowledge of algorithms and mathematical models in the data-mining field.

R TrainerZhu 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.

R TrainerWesley 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.

Data Science TrainerDr. 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 (9)

Will RecommendReview by Nicky Ren
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
More time allocated for course or split it into two days, as content is quite heavy (Posted on 11/19/2017)
Will RecommendReview by HH ONG
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
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)
Will RecommendReview by Adriel Cheng
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
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)
Will RecommendReview by Chang Wei Lin
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
Water in classroom (Posted on 8/21/2017)
Might RecommendReview by Chia Sin Fei
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
Nil (Posted on 6/7/2017)
Will RecommndReview by Marie Wong
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
Less repetitive things like slicing data and more in depth about which situations you would use the different method (Posted on 2/26/2017)
Will recommendReview by Dwight
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
Nil (Posted on 7/31/2016)
Contents coveredReview by LIM KAH KHENG
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
Awaiting for full day course purely on SHINY APPS (Posted on 7/31/2016)
excellentReview by lkk
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
Very good course for those who already has R background.

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)

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