Machine learning has been around for decades, but its usage was limited to specialized applications such as Optical Character Recognition (OCR) due to constraints in computing resources.
Machine learning has been around for decades, but its usage was limited to specialized applications such as Optical Character Recognition (OCR) due to constraints in computing resources.
With advancements in computing and communication technology, powerful computing resources are becoming more affordable, and it is becoming possible to implement and use machine learning to solve complex problems in various application domains effectively. Whether you are in healthcare, banking, or manufacturing domains, machine learning may suit your needs.
This course assumes you know close to nothing about Machine Learning. You will learn the concepts and tools needed to implement programs that learn from data by using production-ready Python frameworks.
The course comprises of two parts: 1) Fundamentals of Machine Learning and 2) Building and implementing machine learning models with Jupyter Notebook. The first part introduces the types of machine learning techniques, the typical workflow of a machine learning project, and going through an example project using some datasets.
The second part covers the basics of Jupyter Notebook, implementation of the Machine Learning Models in both non-distributed and distributed computing environment. At the end of the course, you will have the ability to utilize machine learning models to solve some real problems.?
Objectives:
Upon completion of this course, participants will be able to:
The Centre for Advanced and Professional Education or CAPE UTP was established in May 2016 to provide flexible post-bachelor degree opportunities by developing sustainable programmes and modules of professional short courses.
CAPE aims to be the sought after partner to the industry by providing a platform for flexible learning to diverse learners and access to the Universiti Teknologi PETRONAS’s intellectual resources.
Amazon Sage Maker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale.
The Machine Safety course is offered by the Malaysia Steel Institute. Malaysia Steel Institute (MSI) is an industry-driven enterprise supported and funded by the Ministry of International Trade & Industry (MITI) on a shared responsibility basis with the industry.
Machine Learning and Deep Learning course is offered by AA Knowledge Malaysia Sdn Bhd. We fill needs creatively and seek new ways of looking at old problems.  By promoting learning, research, and education, we contribute to the advancement of education and training.
Machine Learning for Risk Managers course is offered by Symphony Digest. Whether you are looking for public courses or HR looking for in-house courses, Symphony has over 200 courses to suit your needs.
Machine Learning & Deep Learning course is offered by Academy Adelphi Worldwide Edu Sdn Bhd. Our in-house training option enables you to select the mix of participants to ensure optimum results and promote team spirit.
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