Artificial intelligence (AI) can obtain unbelievably accurate insights into a neighborhood's inhabitants – from their income and level of education to their ethnic background and political beliefs – just by looking at images from Google Street View. If, for example, you wanted to see whether an area voted Republican or Democrat, the AI algorithm would be able to correctly tell you with over 80 percent accuracy, namely based on the types of vehicles riding on the road. The deep-learning algorithm was developed by a team of computer scientists based at Stanford University. Their study was published in the Proceedings of the National Academy of Sciences. Throughout this process, it used an object recognition algorithm to clock tens of millions of houses, landscape features like shrubberies, and – most importantly – vehicles.

Feature selection is a dimensionality reduction technique that selects a subset of representative features from high-dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature selection methods that ignores correlation between features. These works first map data onto a low-dimensional subspace and then select features by posing a sparsity constraint on the transformation matrix. However, they are restricted by design to linear data transformation, a potential drawback given that the underlying correlation structures of data are often non-linear. To leverage a more sophisticated embedding, we propose an autoencoder-based unsupervised feature selection approach that leverages a single-layer autoencoder for a joint framework of feature selection and manifold learning. More specifically, we enforce column sparsity on the weight matrix connecting the input layer and the hidden layer, as in previous work. Additionally, we include spectral graph analysis on the projected data into the learning process to achieve local data geometry preservation from the original data space to the low-dimensional feature space. Extensive experiments are conducted on image, audio, text, and biological data. The promising experimental results validate the superiority of the proposed method.

This video course will demonstrate the steps for analyzing unstructured data with the R/R Studio software. The approaches will be illustrated using practical applications for business, healthcare, and retail data, among others. At the end the video course you will have mastered obtaining and visualizing data with R. You will also be confident with data cleaning, preparation, and sentiment analysis with R. Dr. Bharatendra Rai is a professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne State University, Detroit.

Learn volatility trading analysis through a practical course with R statistical software using CBOE, S&P 500, VelocityShares volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing. It explores main concepts from advanced to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced sophisticated investor. Learning volatility trading analysis is indispensable for finance careers in areas such as derivatives research, derivatives development, and derivatives trading mainly within investment banks and hedge funds. It is also essential for academic careers in derivatives finance. And it is necessary for experienced sophisticated investors' volatility trading strategies research.

One clever way to pierce the PR swamp of my inbox is with an optimistic twist. If that optimism is backed by data? Topcoder PR recently won my inbox with this email subject header: "Coders aren't scared of losing work to AI – Topcoder community explains why." It helps that I've known about Topcoder for years. With 1,200,000 developers, all signed up to collaborate on crowdsourced projects and compete in online challenges, Topcoder know a thing or two about what makes developers tick – and how coders upskill against requirements.

Excited about using AI to improve your organization's operations? I want to warn you about bias and how it can appear in those types of projects, share some illustrative examples, and translate the latest academic research on "algorithmic bias." What we call things shapes our understanding of them. That's why I try to avoid the hype-driven term "artificial intelligence." Most projects called that are more usefully described as "machine learning."

With "Introduction to R", you will gain a solid grounding of the fundamentals of the R language! This course has about 90 videos and 140 exercise questions, over 10 chapters. To begin with, you will learn to Download and Install R (and R studio) on your computer. Then I show you some basic things in your first R session. From there, you will review topics in increasing order of difficulty, starting with Data/Object Types and Operations, Importing into R, and Loops and Conditions.

At health insurers, artificial intelligence can strengthen claims management by systematically identifying and correcting errors while avoiding ineffective interventions. Artificial intelligence (AI) is one of the current megatrends emerging from the broader digitization of society and the economy. So far, these "smart" AI technologies have mainly attracted attention in the e-business, automotive, and consumer goods sectors. Siri, the automated voice on Apple's iPhone, or Alexa, Amazon's electronic shopping assistant, are two examples shaping public perception. Automated image recognition systems and self-driving cars are making a mark as well. The private sector has long recognized the potential inherent in the new technologies.

One of the most amazing things about Python's scikit-learn library is that is has a 4-step modeling pattern that makes it easy to code a machine learning classifier. While this tutorial uses a classifier called Logistic Regression, the coding process in this tutorial applies to other classifiers in sklearn (Decision Tree, K-Nearest Neighbors etc). In this tutorial, we use Logistic Regression to predict digit labels based on images. The image above shows a bunch of training digits (observations) from the MNIST dataset whose category membership is known (labels 0–9). After training a model with logistic regression, it can be used to predict an image label (labels 0–9) given an image.

Are you interested in the field of Deep Learning? Here is a short and useful Review of Deep Learning Course A-Z Hands-On Artificial Neural Networks. If you are in the intermediate level people who know the basics of Deep Learning and Machine Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning. This is one of the bestseller courses on Udemy where students enrolled more than 68120 and with a 4.5 star ratings. With this top-selling Deep Learning tutorial you will learn how to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts.