Stochastic Gradient Descent is an important and widely used algorithm in machine learning. In this post you will discover how to use Stochastic Gradient Descent to learn the coefficients for a simple linear regression model by minimizing the error on a training dataset. Linear Regression Tutorial Using Gradient Descent for Machine Learning Photo by Stig Nygaard, some rights reserved. Here is the raw data. The attribute x is the input variable and y is the output variable that we are trying to predict.
One of the most important tasks in Machine Learning are the Classification tasks (a.k.a. Classification is used to make an accurate prediction of the class of entries in the test set (a dataset of which the entries have not been labelled yet) with the model which was constructed from a training set. You could think of classifying crime in the field of Pre-Policing, classifying patients in the Health sector, classifying houses in the Real-Estate sector. Another field in which classification is big, is Natural Lanuage Processing (NLP). This is the field of science with the goal to makes machines (computers) understand (written) human language.
The core of many machine learning algorithms is optimization. Optimization algorithms are used by machine learning algorithms to find a good set of model parameters given a training dataset. The most common optimization algorithm used in machine learning is stochastic gradient descent. In this tutorial, you will discover how to implement stochastic gradient descent to optimize a linear regression algorithm from scratch with Python. How to Implement Linear Regression With Stochastic Gradient Descent From Scratch With Python Photos by star5112, some rights reserved.
Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning. In this post you will discover the linear regression algorithm, how it works and how you can best use it in on your machine learning projects. You do not need to know any statistics or linear algebra to understand linear regression. This is a gentle high-level introduction to the technique to give you enough background to be able to use it effectively on your own problems. Linear Regression for Machine Learning Photo by Nicolas Raymond, some rights reserved.
When building prediction models, the primary goal should be to make a model that most accurately predicts the desired target value for new data. The measure of model error that is used should be one that achieves this goal. In practice, however, many modelers instead report a measure of model error that is based not on the error for new data but instead on the error the very same data that was used to train the model. The use of this incorrect error measure can lead to the selection of an inferior and inaccurate model. Naturally, any model is highly optimized for the data it was trained on.