Who should attend
Any professional from domains (e.g. healthcare, finance, manufacturing) that need to manage and work with data.
Data engineers, researchers, healthcare professionals and more.
About the course
Machine Learning uses techniques to deal with data in the most intelligent way – by developing algorithms – to derive actionable insights. With machine learning, you can glean useful patterns from the deep, focused troves of data specific to your chosen domain. Analysing that data provide insights that can drive successive new waves of efficiency and automation, reducing operational costs and potentially pinpointing new sources of revenue. This course is part of a series of courses around Machine Learning-driven Data Science (MLDDS), under the StackUp – Startup Tech Talent Development programme offered by NUS-ISS.
- Concepts and intuition: Participants learn and apply the concepts of machine learning using a methodology. You will learn to navigate smoothly through the data sciences and machine learning space by not only creating but also debugging your products with ease. You will then apply the same concepts covered in a real world scenario while in class.
- Architecture: Participants learn what is required to architect a data science platform / team and how to effectively design a machine learning driven data sciences product using a wide range of techniques that are taught and practised in class. This includes, but is not limited to, understanding different parts of a data science product. The main goal of this course is to provoke thinking, establish the context of learning with the objective of developing and enhancing your capabilities in establishing a machine learning product.
- Implementation: Finally, participants will have the opportunity to apply the concepts using industry standard libraries and tools to develop their own machine learning driven data science project.
Upon completion of the course, the participants will be able to:
- Explain the concept and applications of deep learning on sequential information
- Apply recurrent neural networks on time series and text data applications
- Describe state of the art improvements such as Encoder/Decoder RNNs and Attention
What Will Be Covered
- Introduction to sequential learning
- Recurrent neural networks
- Applications such as Sentiment Analysis, Machine Translation, and Image Captioning
- Bridging Recap: Feature Extraction and Modeling Deep Learning Concepts
- Lecture: Introduction to Recurrent Neural Networks
- Workshop: Setting up Deep Learning Tools and Environments
- Lecture: Recurrent Neural Networks: Applications in Time Series
- Workshop: Recurrent Neural Networks for time series prediction
- Lecture: Recurrent Neural Networks: Applications for Natural Language Processing
- Workshop: Recurrent Neural Networks for Text Sentiment Classification
- Lecture: Advanced Recurrent Networks: Encoder / Decoder, Attention for Sequence Generation
- Workshop: Recurrent Neural Networks for Text Sequence generation
Project Work and Presentations
Lisa is with the Software Engineering and Design Practice, StackUp program for National University of Singapore, Institute of Systems Science (NUS-ISS). Lisa has multiple years of extensive experience in software product research and development at Microsoft Corporation (USA). Her background in...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.