I am a data scientist, big data engineer, and full stack software engineer. For my masters thesis I worked on brain-computer interfaces using near-infrared spectroscopy. These assist non-verbal and non-mobile persons communicate with their family and caregivers. I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

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.

Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). In this post you will discover the logistic regression algorithm for machine learning. This post was written for developers interested in applied machine learning, specifically predictive modeling. You do not need to have a background in linear algebra or statistics.

Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization can be significantly reduced in practice, both in terms of space and run-time. Prompted by this observation, we study a convex optimization scheme for block-sparse recovery from linear measurements. To obtain linear sketches, we use expander matrices, i.e., sparse matrices containing only few non-zeros per column. Hitherto, to the best of our knowledge, such algorithmic solutions have been only studied from a non-convex perspective. Our aim here is to theoretically characterize the performance of convex approaches under such setting. Our key novelty is the expression of the recovery error in terms of the model-based norm, while assuring that solution lives in the model. To achieve this, we show that sparse model-based matrices satisfy a group version of the null-space property. Our experimental findings on synthetic and real applications support our claims for faster recovery in the convex setting -- as opposed to using dense sensing matrices, while showing a competitive recovery performance.

P-values for coefficients of cylinders, horsepower and acceleration are all greater than 0.05. This means that the relationship between the dependent and these independent variables is not significant at the 95% certainty level. I'll drop 2 of these variables and try again. High p-values for these independent variables do not mean that they definitely should not be used in the model. It could be that some other variables are correlated with these variables and making these variables less useful for prediction (check Multicollinearity).

With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines. This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product.

Uplift modeling, also known as incremental modeling, true lift modeling, or net-lift modeling is a predictive modeling technique that directly models the incremental impact of a treatment (such as a direct marketing action) on an individual's behavior. Uplift modeling has applications in customer relationship management for up-sell, cross-sell and retention modeling. It has also been applied to personalized medicine. Unlike the related Differential Prediction concept in psychology, Uplift modeling assumes an active agent. All of your marketing effort are about Return on Investment (ROI), ultimately, unless you are a non-profit.

There's no way to give you a good answer within a forum posts so I'll summarize my thoughts in a few small sentences. RF can be considered a very powerful modeling approach but is pretty much a black box. To put it in terms of linear regression, it is like building 200 linear regression models, with predictors and data chosen at random for each tree, and letting the overall prediction being an average (or voted) prediction of all 200 models. With linear regression, you have one model built on all predictors, or predictors chosen by a modeling approach whether selection, stepwise or best subsets. You can also see with that example how different the prediction equations would be, with linear regression fairly easy to understand.

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.

Let's walk through an example of predictive analytics using a data set that most people can relate to:prices of cars. In this case, we have a data set with historical Toyota Corolla prices along with related car attributes. Let's load in the Toyota Corolla file and check out the first 5 lines to see what the data set looks like: Price, Age, KM(kilometers driven), Fuel Type, HP(horsepower), Automatic or Manual, Number of Doors, and Weight in pounds are the data collected in this file for Toyota Corollas. In predictive models, there is a response variable(also called dependent variable), which is the variable that we are interested in predicting. The independent variables(the predictors also called features in the machine learning community) are one or more numeric variables we are using to predict the response variable.