Equipping for 21st Century Economy
Donald John Trump, 45th president of the United States of America, is a prolific twitter with a following of more than 20 million. Could we use a simple machine learning algorithm to identify if a given tweet is from Donald Trump? Yes!
The First Step is to learn Python, an open-source simple and powerful language that is widely used in many domains, particularly in Data Science. Python has a huge repertoire of supporting libraries (a.k.a. frameworks) for all kinds of applications, including Machine Learning.
k-Means is a simple and popular unsupervised machine learning clustering algorithm, commonly used for market segmentation. The algorithm computes the distance squared between each data point and each cluster centroid. Then each data point is assigned to the nearest cluster.
Another awesome framework (a.k.a library) in Python is scikit-learn. The framework provides a simple and consistent API for data modeling and analytics. With scikit-learn, it is easy to implement key machine learning algorithms e.g. Multivariate Regression, Naives Bayes, Neural Network, … etc.
NEW Equipping Series – Data Analytics! To kick start this new equipping series, we will be conducting a 2-hour talk Analytics for Busy Executives at Kent Ridge Guild House @NUSS on 25 June 2019 (Tue), 7.30 to 9.30 pm.
Jamie is the Human Resource Director at AirBike, an innovative company that designs and builds super fast bikes that can zip through congested city roads! AirBike headquarters in Singapore with R&D facilities in Israel and Japan, and manufacturing in China.
Without a doubt, the most accessible tool in use for Business Intelligence today is Microsoft Excel. Excel is by far the most used spreadsheet software in the market place. It is a versatile tool that can be used for data