Originally published at: http://valuecourses.com/courses/nicf-pattern-recognition-and-machine-learning-with-r-wsq-courses/
Machine learning is a branch in computer science that studies the design of algorithms that can learn. It uses supervised learning, unsupervised learning, deep learning, reinforcement learning algorithms to recognise patterns in the data and perform prediction tasks. These tasks are learned through available data that were observed through experiences or instructions. The ultimate goal is to improve the learning in such a way that it becomes automatic, so that humans like ourselves don’t need to interfere any more.
R is one of best platforms to learn machine learning. It has built in datasets to test machine learning aglorithms. It also has many excellent third-party machine learning packages to perform various machine learning tasks.
This 2 days WSQ Machine Learning and Pattern Recognition with R course will teach you how to recognize patterns in the data and perform predictive modeling with machine learning..
Learning Outcomes
By end of the course, learners should be able to
- model pattern recognition problems suitable for machine learning
- apply supervised regression techniques to predict pattern in the data
- apply supervised classification techniques to classify pattern in the data
- apply unsupervised clustering techniques to cluster patterns and detect anomaly in the data
- apply principal component analysis as alternative method to detect pattern in the data
- apply deep neural network and CNN models for visual recognition
Course Objectives
- Learners will be able to derive useful hidden pattern in the data using machine learning methods.
Course Brochure
Download NICF – Pattern Recognition and Machine Learning with R Brochure
Skills Framework
This course follows the guideline of Pattern Recognition Systems ICT-DIT-4026-1.1 TSC under ICT Skills Framework
Certification
Two e-certificates will be awarded to trainees who have demonstrated competency in the WSQ assessment and achieved at least 75% attendance.
- A SkillsFuture WSQ Statement of Attainment (SOA) issued by WSG. Typically will take 3-4 weeks.
- Certification of Achievement issued by Tertiary Infotech Pte Ltd issued after the class.
WSQ Funding
WSQ funding is only applicable to Singaporeans and PR. Subject to eligibility, the funding support is subjected to funding caps.
| Effective from 1 Jan 2022 | |||
| Full Fee | GST | Nett Fee after Funding (Incl. GST) | |
| Normal | MCES / SME | ||
| $688 | $48.16 | $392.16 | $254.56 |
Normal: Singaporean/PR age 21 and above
MCES: Singaporean age 40 and above
| Effective for courses starting from 1 Jan 2023 | |||
| Full Fee | GST | Nett Fee after Funding (Incl. GST) | |
| Normal | MCES / SME | ||
| $688 | $55.04 | $399.04 | $261.44 |
Normal: Singaporean/PR age 21 and above
MCES: Singaporean age 40 and above
WSQ TG Application Form
Eligible individuals and company can apply for WSQ TG. Please login to Skilleto and fill in the required details for grant application. Please click here to submit for WSQ TG
Please do not pay upfront. We will advise you on the eligibility and nett fee after registration. Company sponsored staff are eligible for Absentee Payroll.
After the WSQ subsidy, you can offset the nett fee by the following :
SkillsFuture Enterprise Credit (SFEC)
Eligible Singapore-registered companies can tap on $10000 SFEC to cover out-of-pocket expenses.Click here to submit SkillsFuture Enterprise Credit
SkillsFuture Credit
Eligible Singapore Citizens can use their SkillsFuture Credit to offset course fee payable after funding. Click here for SkillsFuture Credit submission
UTAP
Eligible NTUC members can apply for 50% of the unfunded fee from UTAP, capped at $250 per year. Click here to submit UTAP
PSEA
Eligible Singapore Citizens can use their PSEA funds to offset course fee payable after funding. Click here for ad-hoc PSEA application form.
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Courses are provided by tertiarycourses.com.sg