Lopez C. Machine Learning. Supervised Learning Techniques...2022 torrent

Textbook in PDF format

In this book, supervised learning techniques related to regression will be developed. More specifically, we will go deeper into the linear models, LASSO regression, LARS LASSO regression, RIDGE Regression, Least Angle Regression, Multitask LASSO regression, Elastic Net Regression, Multi task Elastic Net Regression, SGD Regression, Support Vector Regression SVR, Robust Regression, Huber Regression, Kernel regression, RANSAC Regression and other supervised techniques based in Regression. Variety of examples are solved from the SAS Enterpise Miner and MATLAB software.
In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features.
Learning problems fall into a few categories:
Supervised learning , in which the data comes with additional attributes that we want to predict.
This problem can be either:
Classification : samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class.
Regression : if the desired output consists of one or more continuous variables, then the task is called regression . An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight.
Unsupervised learning , in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called Clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization.
Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to extract information directly from data without relying on a predetermined equation as a model. The algorithms improve their performance in an adaptive way as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data.
The goal of supervised machine learning is to build a model that makes evidence-based predictions in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models