About this course: This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today's NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection.

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.

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.

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.

One of the most intuitive and popular methods of data mining that provides explicit rules for classification and copes well with heterogeneous data, missing data, and nonlinear effects is decision tree. It predicts the target value of an item by mapping observations about the item. You can perform either classification or regression tasks here. For example, identifying fraudulent transactions using credit cards would be a classification task while forecasting prices of stock would be regression task. Decision tree technique is used to detect the criteria for dividing individual items of a group into n predetermined classes (Often, n 2 represents a balanced tree, which means a largest of two child nodes for each parent node.) Firstly, a variable is taken as the root node.

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.

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