Introduction to Machine Learning

About this Course 146,612 recent views This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more). Simple Introduction to Machine Learning The focus of this module is to introduce the concepts of machine learning with as little mathematics as possible. We will introduce basic concepts in machine

Dealing With Missing Data

Dealing With Missing Data About this Course 3,877 recent views This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®. Flexible deadlines Reset deadlines in accordance to your schedule. Shareable Certificate Earn a Certificate upon completion 100% online Start instantly and learn at your own schedule. Course 5 of 7 in the Survey Data Collection and Analytics Specialization Approx. 18 hours to complete ENROLL NOW