The Applied Machine Learning course teaches you a wide-ranging set of techniques of supervised and unsupervised machine learning approaches using Python as the programming language.
Since this course requires an intermediate knowledge of Python, you will spend the first part of this course learning Python for Data Analytics taught by Emeritus. This will provide you with the programming knowledge required to do the assignments and application projects that are part of the Applied Machine Learning course.
If you are looking to implement or lead a machine learning project or looking to incorporate machine learning capability in your software application, this course is appropriate for you. This is a programming course: you will be required to write code.
anticipated spending growth on AI and ML by 2021.SOURCE: INTERNATIONAL DATA CORPORATION (IDC)
expected wage growth for data scientists (vs. <2% average wage increase across all occupations).SOURCE: U.S. BUREAU OF LABOR STATISTICS
decrease in ‘click-to-ship’ time by Amazon using ML algorithm.SOURCE: FORBES
Going beyond the theory, our approach invites participants into a conversation, where learning is facilitated by live subject matter experts and enriched by practitioners in the field of machine learning:
● The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation etc.), calculus (derivatives), linear algebra (vectors & matrix transformation) and probability (conditional probability/Bayes theorem).
*Assessment: Students will be given an assessment to test their math skills prior to commencement of the course. You can view sample questions by clicking here.
To familiarize yourself with the topics of the assessment, refer to learning resources by clicking here.
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.
The course familiarizes you with Machine learning algorithms and applications. It will also help you understand the approach to a business problem and provide you with the tool knowledge needed to transition to a Machine Learning or a Data Science role.
The course familiarizes you with Machine learning algorithms and applications and provides a solid foundation in statistics/mathematics and problem-solving skills to help you solve enterprise-level problems. The Applied Machine Learning course augments your existing knowledge of various tools and expands your skill set as a Data Science or Machine Learning professional.
The course familiarizes you with Machine learning algorithms and applications while providing a solid foundation in statistics/mathematics and enhancing your business acumen. It augments your existing programming knowledge and expands the technologies you are familiar with, helping you further develop your skill set as a Data Science or Machine Learning professional.
Absolutely! Knowledge of Data Science and Machine Learning (ML) has quickly become a requisite across industries, and all businesses will eventually need to use these techniques to thrive. While your current role may not require Machine learning knowledge, it is almost certain that ML skills will be in high demand in most every industry in the future.
The course is a blend of theory, tools, and case studies (datasets) that are easy to assimilate and implement. For instance, students work on application projects that require them to apply the Machine Learning concepts they’ve learned to datasets and derive inferences. These application projects are intentionally made to be challenging, and students are expected to spend substantial time and effort solving them; likely 6-8 hours per week. At the end of the course, students will be able to apply Machine Learning to solve many of the business problems they face in their workplace.
Columbia Engineering Executive Education is collaborating with online education provider Emeritus to offer a portfolio of high-impact online courses. These courses leverage Columbia’s thought leadership in management practice developed over years of research, teaching, and practice.
Recommended System Requirements
Minimum System Requirements
Upon successful completion of the course, participants will receive a verified digital certificate from Emeritus in collaboration with Columbia Engineering Executive Education.Get Certified