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Expand your machine learning know-how and explore the world of spatial statistics with Supervised Learning: Regression and Spatial Statistics in R! Find Bigfoot by using space-time clustering, a technique used in spatial statistics. And in supervised learning, you'll learn about multiple different regression models in this useful machine learning course. [DataCamp]( New Courses! [Spatial Statistics]( Supervised Learning in R: Regression Taught by Nina Zumel, Co-founder & Principal Consultant at Win-Vector, LLC From a machine learning perspective, regression is the task of predicting numerical outcomes from various inputs. In this course, you'll learn about different regression models, how to train these models in R, how to evaluate the models you train and use them to make predictions. [Play now ›]( Spatial Statistics in R Taught by Barry Rowlingson, Research Fellow at Lancaster University Everything happens somewhere, and increasingly the place where all these things happen is being recorded in a database. This course will start you on your journey of spatial data analysis. You'll learn what classes of statistical problems present themselves with spatial data, and the basic techniques of how to deal with them. [Play now ›]( Supervised Learning in R: Regression: What You'll Learn Chapter 1: What is Regression? In this chapter we introduce the concept of regression from a machine learning point of view Chapter 2: Training and Evaluating Regression Models Now that we have learned how to fit basic linear regression models, we will learn how to evaluate how well our models perform. Chapter 3: Issues to Consider Before moving on to more sophisticated regression techniques, in this chapter we will look at some other modeling issues. Chapter 4: Dealing with Non-Linear Responses Now that we have mastered linear models, we will begin to look at techniques for modeling situations that don't meet the assumptions of linearity. Chapter 5: Tree-Based Methods In this chapter we will look at modeling algorithms that do not assume linearity or additivity. [Play Now ›]( Spatial Statistics in R: What You'll Learn Chapter 1: Introduction After a quick review of spatial statistics as a whole, you'll go through some point-pattern analysis. Chapter 2: Point Pattern Analysis Point Pattern Analysis answers questions about why things appear where they do. Chapter 3: Areal Statistics So much data is collected in administrative divisions that there are specialized techniques for analyzing them. This chapter presents several methods for exploring data in areas. Chapter 4: Geostatistics Geostatistics covers the analysis of location-based measurement data. [Play Now ›]( DataCamp Inc. 2067 Massachusetts avenue Cambridge MA 02140 [Unsubscribe](