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.
- 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 applications of Machine Learning
- Apply machine learning workflow to perform data engineering, feature engineering, and to train a simple machine learning model
- Use industry-standard machine learning tools to perform data engineering, feature engineering, and to train a simple machine learning model
- Evaluate and compare performance of machine learning models
What Will Be Covered
- Introduction and applications of Machine Learning
- Supervised vs. Unsupervised learning
- Machine learning workflow
- Python machine learning libraries
- Data cleaning, sampling, and inspection
- Feature selection & reduction
- Training a Linear Regression model using Gradient Descent
- Overfitting, Underfitting
- Lecture: Machine Learning Introduction
- Workshop: Setting up the environment
- Workshop: Matplotlib
- Workshop: Pandas
- Lecture: Data Engineering: data scaling, encoding, sampling
- Workshop: Data Engineering
- Lecture: Feature selection and reduction, Principal Component Analysis
- Workshop: Feature selection and reduction, Principal Component Analysis
- Project Part 1: Data and Feature Engineering
- Lecture: Linear Regression, Gradient Descent
- Workshop: Linear Regression, Gradient Descent
- Lecture: Evaluation metrics, learning curve, overfitting, underfitting
- Workshop: Evaluation metrics
- Project Part 2: Linear Regression and Evaluation metrics
- Project 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.