My clients are looking for computer scientists, statisticians, biostatisticians, physicists, computational social scientists, economists, engineers, operations researchers- in short they are looking for Data Scientist with strong hands on experience in "Big Data" as well as predictive modeling, optimization, machine learning, neural networks using a range of advanced technical tools (Python, SAS, Hadoop, R, Matlab, Machine learning, natural language processing, CPlex, C, etc.) This is a position that will be responsible for helping develop quantitative solutions to solve complex applications. The candidates will be involved in developing leading-edge, "out of the box" advanced analytic solutions and processes. Substantial data analysis experience utilizing standard tools such as SAS, SPSS, or R In depth knowledge of standard algorithms such as linear regression, logistic regression, clustering, decision trees, and affinity analysis Very strong SQL skills including complex query structures Comfort with relational database theory, third normal form, and data models Previous presales experience in the software industry and an understanding of sales cycles: partnering with account executives, sales approaches, and sales support for large customers. Sufficient depth and breadth of technical knowledge to design and scope multiple deliverables across a number of technologies Demonstrated innovation and communication of new deliverables and offerings.

In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output. Linear regression with one variable is also known as "univariate linear regression." Univariate linear regression is used when you want to predict a single output value from a single input value . We're doing supervised learning here, so that means we already have an idea about what the input/output cause and effect should be. The "error", at each point, between the line fit and the data is the difference between the right- and left-hand sides of the equations above.

MM (majorization--minimization) algorithms are an increasingly popular tool for solving optimization problems in machine learning and statistical estimation. This article introduces the MM algorithm framework in general and via three popular example applications: Gaussian mixture regressions, multinomial logistic regressions, and support vector machines. Specific algorithms for the three examples are derived and numerical demonstrations are presented. Theoretical and practical aspects of MM algorithm design are discussed.

This article explains how to run linear regression with R. Check out all this information, here.

H2O Sparkling Water supports a wide array of algorithms, and as illustrated above it's easy to chain these functions together with dplyr pipelines. To learn more see the H2O Sparkling Water section.

You'll find the rest of the chapter in the full version of the IPython Cookbook, by Cyrille Rossant, Packt Publishing, 2014.

Recently, I did a webinar on Machine Learning and R. I received a number of questions during the presentation. Due to time constraints, I was unable to answer all of them, so I have provided the Question and Answers here. Question: Can I Use R in SQL Server to plot non-linear regression curves? We use IC50 and others in Michaelis-Menten kinetics for bio-chemical work. R running on SQL Server provides the functionality of standard CRAN R packages with the additional capability to run the SCALER functions provided by SQL Server's implementation of R. Any other functionality performed in R can therefore also be performed on SQL Server.

For those following along here or on my Twitter account it's no secret that I am currently enrolled at Metis in their 12-week data science bootcamp, which marries the structure of daily morning problem solving with highly self-guided and project-based afternoons/evenings/weekends. The expectations are high, and the deadlines are "intentionally unfair", giving the three months a hackathon-lite vibe. Some projects featured on this blog, this post included, accompany projects completed and presented for Metis. For this project I scraped Box Office Mojo in order to build a predictive linear regression model. At first blush, predicting domestic box office gross is hardly worthy of machine learning: instinctively we know it must be a function of increasing marketing and production budgets.

Variable selection for models including interactions between explanatory variables often needs to obey certain hierarchical constraints. The weak or strong structural hierarchy requires that the existence of an interaction term implies at least one or both associated main effects to be present in the model. Lately, this problem has attracted a lot of attention, but existing computational algorithms converge slow even with a moderate number of predictors. Moreover, in contrast to the rich literature on ordinary variable selection, there is a lack of statistical theory to show reasonably low error rates of hierarchical variable selection. This work investigates a new class of estimators that make use of multiple group penalties to capture structural parsimony. We give the minimax lower bounds for strong and weak hierarchical variable selection and show that the proposed estimators enjoy sharp rate oracle inequalities. A general-purpose algorithm is developed with guaranteed convergence and global optimality. Simulations and real data experiments demonstrate the efficiency and efficacy of the proposed approach.

Although the Lasso has been extensively studied, the relationship between its prediction performance and the correlations of the covariates is not fully understood. In this paper, we give new insights into this relationship in the context of multiple linear regression. We show, in particular, that the incorporation of a simple correlation measure into the tuning parameter can lead to a nearly optimal prediction performance of the Lasso even for highly correlated covariates. However, we also reveal that for moderately correlated covariates, the prediction performance of the Lasso can be mediocre irrespective of the choice of the tuning parameter. We finally show that our results also lead to near-optimal rates for the least-squares estimator with total variation penalty.