used an introduction to statistical learning

#USED AN INTRODUCTION TO STATISTICAL LEARNING SOFTWARE#
#USED AN INTRODUCTION TO STATISTICAL LEARNING FREE#

In recent years, progress in statistical learning has been marked by the increasing availability of powerful and relatively user-friendly software, such as the popular and freely available R system. Since that time, inspired by the advent of machine learning and other disciplines, statistical learning has emerged as a new subfield in statistics, focused on supervised and unsupervised modeling and prediction.

#USED AN INTRODUCTION TO STATISTICAL LEARNING SOFTWARE#

Hastie and Tibshirani coined the term generalized additive models in 1986 for a class of non-linear extensions to generalized linear models, and also provided a practical software implementation. In mid 1980s Breiman, Friedman, Olshen and Stone introduced classification and regression trees, and were among the first to demonstrate the power of a detailed practical implementation of a method, including cross-validation for model selection. By the 1980s, computing technology had finally improved sufficiently that non-linear methods were no longer computationally prohibitive. However, they were almost exclusively linear methods, because fitting non-linear relationships was computationally infeasible at the time. In the early 1970s, Nelder and Wedderburn coined the term generalized linear models for an entire class of statistical learning methods that include both linear and logistic regression as special cases.īy the end of the 1970s, many more techniques for learning from data were available. In the 1940s, various authors put forth an alternative approach, logistic regression. In order to predict qualitative vales, such as whether a patient survives or dies, or whether the stock market increases or decreases, Fisher proposed linear discriminant analysis in 1936. Linear regression is used for predict qualitative values, such as an individual’s salary. At the beginning of the nineteenth century, Legendre and Gauss published papers on the method of least squares, which implemented the first successfully applied to problems in astronomy. I found it incredibly difficult to get going because I didn't understand how different some of the algorithms people used were.Though the term statistical learning is fairly new, many of the concepts that underlie the field were developed long ago. I would contrast Statistical learning from Linear Algebra based approaches that often get thrown together in a blender when people employ jargon like ML.

It helps lay a good foundation for future exploration in statistical learning, which I think is a good way to describe the statistical modeling approaches many people use in data science.

This is just my personal take: I think this book is the gateway drug. The book covers a lot-you can be pretty well rounded by going through it, which is pretty nice-one book and you can build the foundation to understand and access more advanced content.Trevor Hastie and Rob Tibshirani are experts in this subject.7th edition also means that it is continuing to become more clear as a teaching resource.It's currently in its 7th edition, and has been taught in universities for a while, so, I think often people end up teaching what they learned-especially if they found it helpful.Connected to the above, the book can feel a tad redundant in its explanations if you read in a hurry, but the language is incredibly specific with an effort to not ignore anything.Although the math looks intimidating, they do a pretty good job breaking down what is happening in the equations-it's just slow going and continues to build upon what was previously covered (Elements of Statistical Learning is a lot more dense-I couldn't really get into it since my math is not as strong).

#USED AN INTRODUCTION TO STATISTICAL LEARNING FREE#

The lectures are on YouTube and are also free.The book if free, which is rare for quality statistics books (RIP wallet if you want more).The reason I think people love it so much is (probably) for really practical reasons here are a few I can think of (this is just some armchair speculation):

Used an introduction to statistical learning