13 June 2019
3 Months, Online
6-8 hours per week
This course teaches you a wide-ranging set of techniques of supervised and unsupervised machine learning approaches using Python as the programming language.
This course is appropriate for you, if you are looking to implementing or leading a machine learning project in your organization or developing custom ML functions to incorporate in your application.
At the end of this course, you will be able to
● The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation, etc.), calculus, linear algebra, and probability.
● All assignments/application projects will be done using the Python programming language. You should have an intermediate knowledge of Python or you should have completed the Emeritus Python for Data Science course prior to joining this course.
All assignments and application projects will be done using the Python programming language.DOWNLOAD SYLLABUS
You will build a movie recommendation engine by applying collaborative filtering and topic modelling techniques. You use a dataset which contains 20 million viewer ratings of 27,000 movies.
You will write code to predict house prices based on several parameters available in the Ames City dataset compiled by Dean De Cock using least squares linear regression and Bayesian linear regression.
You will predict the human activity (walking, sitting, standing) that corresponds to the accelerometer and gyroscope measurements by applying the nearest neighbours technique.
You will detect potential frauds using credit card transaction data. You will apply the random forest method to identify fraudulent transactions.
You will create market segments using the US Census dataset and by applying the k-means clustering method.
Note: All product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.
John has a PhD from Duke and has been a postdoctoral researcher in the Computer Science departments at Princeton University and UC Berkeley. John Paisley’s research focuses on developing models for large-scale text and image processing applications. He is particularly interested in Bayesian models and posterior inference techniques that address the big data problem.
Upon successful completion of the course, participants will receive a verified digital certificate from EMERITUS in collaboration with Columbia Engineering Executive Education.Get Certified
Flexible payment options available. Click here to know more.