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Introduction to Spark NLP: Foundations and Basic Components

#artificialintelligence

Natural language processing (NLP) is a key component in many data science systems that must understand or reason about a text. Common use cases include question answering, paraphrasing or summarizing, sentiment analysis, natural language BI, language modeling, and disambiguation. NLP is essential in a growing number of AI applications. Extracting accurate information from free text is a must if you are building a chatbot, searching through a patent database, matching patients to clinical trials, grading customer service or sales calls, extracting facts from financial reports or solving for any of these 44 use cases across 17 industries. Due to the popularity of NLP and hype in Data Science in recent years, there are many great NLP libraries developed and even the newbie data science enthusiasts started to play with various NLP techniques using these open source libraries.


Implement an ARIMA model using statsmodels (Python)

@machinelearnbot

In this article was written by Michael Grogan. Michael is a data scientist and statistician, with a profound passion for statistics and programming. In a previous tutorial, I elaborated on how an ARIMA model can be implemented using R. The model was fitted on a stock price dataset, with a (0,1,0) configuration being used for ARIMA. Here, I detail how to implement an ARIMA model in Python using the pandas and statsmodels libraries.


Germany seeks 'big flip in publishing model

Science

Over the last 2 years more than 150 German libraries, universities, and research institutes have formed a united front trying to force academic publishers into a new way of doing business. Instead of buying subscriptions to specific journals, consortium members want to pay publishers an annual lump sum that covers publication costs of all papers whose first authors are at German institutions. Those papers would be freely available around the world; meanwhile, German institutions would receive access to all of the publishers' online content. Libraries and universities in other countries have pushed for similar agreements, but have had to settle for less than they wanted. But Germany's consortium, named Projekt DEAL, plans to hold firm, and it thinks a successful outcome could help trigger what some call a "big flip," a global transition toward open access.


Implement an ARIMA model using statsmodels (Python)

@machinelearnbot

In this article was written by Michael Grogan. Michael is a data scientist and statistician, with a profound passion for statistics and programming. In a previous tutorial, I elaborated on how an ARIMA model can be implemented using R. The model was fitted on a stock price dataset, with a (0,1,0) configuration being used for ARIMA. Here, I detail how to implement an ARIMA model in Python using the pandas and statsmodels libraries.


Implement an ARIMA model using statsmodels (Python) Michael Grogan

@machinelearnbot

As previously mentioned, our data is in logarithmic format. Since we are analysing stock price, this format is necessary to account for compounding returns.