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Essential Machine Learning with R

Dive deep into the intricate world of machine learning with our Essential Machine Learning with R course at Tertiary Courses. Participants will begin by understanding the foundational concept of machine learning, paving the way for an immersive experience into Regression and Classification models. This comprehensive approach ensures that learners are well-equipped to identify and apply the most fitting machine learning methods to address complex problems effectively.

With a strong emphasis on practical application, participants will be adept at performing nuanced data analyses using R, a powerful tool in the machine learning arsenal. The course culminates in enabling students to draw insightful inferences from data, ensuring they can translate their skills into actionable, data-driven decisions in real-world scenarios. Through this rigorous training, attendees will emerge as proficient machine learning practitioners, skilled in leveraging the capabilities of R for impactful results.

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant Applications

No funding is available for this course

For WSQ funding, please checkout the details at NICF - Pattern Recognition and Machine Learning with R

Course Code: C323

Course Booking

The course fee listed below is before subsidy/grant, if applicable. We will apply for the grant and send you the invoice with nett fee.

$300.00 (GST-exclusive)
$327.00 (GST-inclusive)

Course Date

Course Time

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Post-Course Support

We provide free consultation related to the subject matter after the course. Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts and get back to you asap.

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.


Course Details

Topic 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 Regression

  • What is Regression
  • Applications of Regression
  • Least Square Error Minimization
  • Data Pre-processing
  • Bias vs Variance Trade-off
  • Regression Methods with Regularization

Topic 3 Classification

  • What is Classification
  • Applications of Classification
  • Classification Algorithms
  • Confusion Matrix
  • Classification Performance Evaluation

Topic 4 Clustering

  • What is Clustering
  • Applications of Clustering
  • Distance Measure
  • Clustering Algorithms
  • Clustering Performance Evaluation
  • Anomaly Detection Problem

Topic 5 Principal Component Analysis

  • Principal Component Analysis (PCA) and Dimension Reduction
  • Applications of PCA
  • PCA Workflow

Course Admin

Prerequisite

This is an intermediate course. the following knowledge is assumed

Software Requirement

Please download and install the following software prior to the class

Who Should Attend

  • Data Scientist
  • Machine Learning Engineer
  • Data Analyst
  • Research Scientist
  • Business Intelligence Specialist
  • Data Engineer
  • Software Developer (interested in ML)
  • Statistician
  • Predictive Modeler
  • AI Solutions Architect
  • Quantitative Researcher
  • Data Visualization Specialist
  • Analytics Consultant
  • Product Manager (focused on AI/ML products)
  • Innovation Specialist

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 (13)

will recommend Review by Course Participant/Trainee
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
. (Posted on 9/26/2020)
Will Recommend Review by Course Participant/Trainee
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
Thank you for running the R-machine learning short course on 6 August, despite being the sole participant. Lisa was an excellent instructor! I have learnt heaps.

FYI, I will be looking to attend a series of courses at your centre in the next few months, to upskill myself (Posted on 8/8/2018)
Excellent course with experienced trainer. Review by Course Participant/Trainee
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
Dr Ravi Kumar is an excellent trainer in the subject, with relevant domain expertise. He is able to provide intuitive insight on the inner workings of machine learning algorithms without diving into the math. Interactive RShiny visualizations were used to explain models and algorithms, minimizing confusion and .
I highly recommend this course for anybody seeking to gain a foothold into machine learning, as it provides both practical examples for practice and an understanding of how the algorithms work. (Posted on 6/26/2018)
I learned a lot in this course, and the instructor is very good. Review by Course Participant/Trainee
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 (Posted on 6/25/2018)
Will Recommend Review by Course Participant/Trainee
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 Recommend Review by Course Participant/Trainee
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 Recommend Review by Course Participant/Trainee
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 Recommend Review by Course Participant/Trainee
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 Recommend Review by Course Participant/Trainee
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 Recommnd Review by Course Participant/Trainee
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 recommend Review by Course Participant/Trainee
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 covered Review by Course Participant/Trainee
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)
excellent Review by Course Participant/Trainee
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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