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.

Python is a popular programming language, widely used in many scenarios and easy to use to use. Data Science is an interdisciplinary field that employs techniques to extract knowledge from data. As one of the fast growing fields in technology, the interest for Data Science is booming, and the demand for specialized talent is on the rise. Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. To start off with your learning journey, you can learn some of the fundamental tools of the trade and apply them to real data problems.

With its popularity as a statistical programming language rapidly increasing with each passing day, R is increasingly becoming the preferred tool of choice for data analysts and data scientists who want to make sense of large amounts of data as quickly as possible. R has a rich set of libraries that can be used for basic as well as advanced data analysis tasks. If you have a basic understanding of data analysis concepts and want to take your skills to the next level, this video is for you. Spanning over four hours, it contains carefully selected advanced data analysis concepts such as: cluster analysis; time-series analysis; Association mining; PCA (Principal Component Analysis); handling missing data; sentiment analysis; spatial data analysis with R and QGIS; advanced data visualization with R and ggplot2. Throughout the video, readers will use the various topics they've learned about to analyze real-world datasets from various industry sectors.