The Data Science Blog
Accessing and Manipulating Data in R Perhaps as you may have noted in following this series from the Art of Better Programming, we have gone through great lengths to provide knowledge and resources to you for learning data science. We began primarily with a variety of series that elaborated on data science with Python. WeContinue reading “Accessing and Manipulating Data in R: Using R in Data Science and ML”
An Introduction To R The Art of Better Programming has given an extensive discussion of machine learning and data science from the perspective of Python. Studies show that Python is the most represented programming language in data science, exhibiting a use in over 50% of cases. However, if you want to be an employable dataContinue reading “Transition From Python To R: Using R in Data Science and ML”
Introduction to Ridge Regresison This present article moves away from traditional linear and polynomial regression to a more nuanced form of regression known as ridge regression. Previously, we spent a great deal of time reviewing traditional machine learning models and investigated the performance measures associated with these algorithms. We then took great lengths in examiningContinue reading “Ridge Regression Linear Models: Topics of Machine Learning”
Introduction to Learning Curves For quite some time now in our machine learning series, we have belabored various aspects of regression algorithms in machine learning. Our first insight to this concept manifested in our initial article discussing supervised machine learning models, which may be found here. After exploring a variety of different machine learning models and embarkingContinue reading “Learning Curves in Machine Learning Models: Topics of Machine Learning”
Introduction to Machine Learning Resources Recently, we have received a large amount of requests for useful resources in attempting to understand machine learning from first principles. These requests inspired the creation of this article. Just so we can set the scene for you, we want to take a moment to reflect on the topics ofContinue reading “Top Five Resources For Machine Learning: Topics of Machine Learning”
Prologue Before we begin, take a second. Open this link in a new tab. Did you do it? I’ll wait. Oh, you opened it? Great. Sorry for the forwardness, but this book is one of the greatest tools I have used in taking myself from a beginner in programming to a full time data scientist.Continue reading “Regression From the Mathematical Perspective: Topics of Data Modeling”
Introduction to Polynomial Regression The present article seeks to elucidate the myriad of features associated with polynomial regression with respect to its role in machine learning. We previously brought to light a variety of features associated with linear regression in machine learning models. We first provided an introduction to linear regression in this article. FollowingContinue reading “Polynomial Regression Algorithms: Topics of Machine Learning”
Introduction to Stochastic Gradient Descent Our previous articles investigated the importance of linear regression and batch gradient descent in machine learning modeling, as well as the intricacies of Gradient Descent models. With respect to gradient descent, here we investigate linear regression as it relates to stochastic gradient descent machine learning. Before embarking on this discussion,Continue reading “Stochastic Gradient Descent Algorithms: Topics of Machine Learning”
Introduction to Batch Gradient Descent Our previous article investigated the importance of linear regression in machine learning modeling, as well as the intricacies of Gradient Descent models. With respect to gradient descent, here we investigate linear regression as it relates to batch gradient descent machine learning. Before embarking on this discussion, we would like toContinue reading “Batch Gradient Descent Algorithms: Topics of Machine Learning”
Day 3 Of 100 Days of Code Our third day of this 100 Days of Code was filled with in depth analysis of linear regression and elaboration on gradient descent algorithms. What should first be noted is the fact that we successfully published two deeply interwoven articles. The first analyzed in depth linear regression algorithms,Continue reading “Linear Regression and Academic Literature: 100 Days of Code (3/100)”
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