Telecom Customer Churn Prediction in Apache Spark (ML)

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In this Data science Machine Learning project, we will create Telecom Customer Churn Prediction Project using Classification Model Logistic Regression, Naive Bayes and One-vs-Rest classifier few of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.


Spark Machine Learning Project (House Sale Price Prediction)

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Get your team access to 3,500 top Udemy courses anytime, anywhere. In this Data science Machine Learning project, we will predict the sales prices in the Housing data set using LinearRegression one of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.


Spark ML Runs 10x Faster on GPUs, Databricks Says

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Apache Spark machine learning workloads can run up to 10x faster by moving them to a deep learning paradigm on GPUs, according to Databricks, which today announced that its hosted Spark service on Amazon's new GPU cloud. Databricks, the primary commercial venture behind Apache Spark, today announced that it's now supporting TensorFrames, the new Spark library based on Google's (NASDAQ: GOOG) TensorFlow deep learning framework, on its hosted Spark service, which runs on Amazon Web Services (NASDAQ: AMZM). The deep learning service will be generally available within two weeks, the company says. TensorFrames, which was unveiled this March as a technical preview, lets Spark harness TensorFlow for the purpose of programing deep neural networks, the primary computational method powering so-called "deep learning" algorithms. TensorFrames is also available to on-prem Spark users as a GitHub project, but it's not yet available for download in the Apache Spark project, which limits its usefulness for the time being.


Apache Spark For Machine Learning & Data Science (Spark 301): 5 half-day Live-On... - RegOnline

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This hands-on, 5 half-day Live-Online Spark 301 training targets experienced Data Scientists wishing to perform data analysis at scale using Apache Spark. The class will be held on the following days and times: Monday, January 22 to Friday, January 26, 2018, from 7:00am to 11:00am PST each day. This course covers an overview of Apache Spark, hands-on projects utilizing extract- transform-load operations (ETL), employing exploratory data analysis (EDA), building machine learning models, evaluating models, and performing cross validation. This course covers the same material as our three-day, in-person Apache Spark For Machine Learning & Data Science (Spark 301) course. All hands-on labs are run on Databricks Community Edition, a free cloud based Spark environment.


Spark Summit 2018 Preview: Putting AI up front, and giving R and Python programmers more respect

ZDNet

It shouldn't be surprising given the media spotlight on artificial intelligence, but AI will be all over the keynote and session schedule for this year's Spark Summit. The irony, of course, is that while Spark has become known as a workhorse for data engineering workloads, its original claim to fame was that it put machine learning on the same engine as SQL, streaming, and graph. But Spark has also had its share of impedance mismatch issues, such as making R and Python programs first-class citizens, or adapting to more compute-intensive processing of AI models. Of course, that hasn't stopped adventurous souls from breaking new ground. Hold those thoughts for a moment.