3 Months, Online
6-8 hours per week
This courses teaches machine learning from a practitioner’s perspective. If you want to get started with machine learning and learn the easy and practical way, this course is appropriate for you.
This course will provide an easy-to-follow roadmap to frame a given business problem and identify steps toward training, testing, scoring, and deploying an appropriate model for the situation.
At the end of the course, you will be able to
PREREQUISITES: The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation etc.) and probability. Familiarity with R (importing a data set, assigning variables, working with a variety of data structures like. numeric, character, factor etc., creating and adding columns to data frames) is required.
Assignments /application projects which require programming will be done using the R programming language.
Participants will perform an exploratory data analysis (EDA) and build a univariate or multivariate linear regression model using data from Apple’s app store.
Participants will apply logistic regression to a dataset including features on credit card users and develop a model predicting the probability of default payments based upon previous payment history, bill amount, and customer demographics.
Participants will examine classification problems and apply what they have learned to an employee attrition data set in order to make predictions about the probability of an employee leaving his/her company.
Participants will get an introduction to neural networks and make predictions based upon a dataset with information on office supply purchases.
Participants will get an introduction to unsupervised learning algorithmic techniques, such as K-means and hierarchical clustering. and employ clustering techniques to develop segments from customer data.
Participants will walk through practical examples of problem framing and identify approaches to modeling customer lifetime value and develop recommendation engines.
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For the past 15 years, Christopher Brown has worked as a data science consultant in a variety of industries, from financial services and healthcare to...
Kristen is #8 LinkedIn Global Top Voice 2018 – Data Science & Analytics. Since 2010, Kristen has been a data scientist across multiple industries, including...
Upon successful completion of the course, participants will receive a certificate from UC Berkeley Extension.