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Data Science Central Thursday Digest, April 25

Here is our selection of featured articles and resources posted since Monday:

Resources

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Added by Vincent Granville on April 25, 2019 at 12:30pm — No Comments

Naive Bayes in One Picture

Naive Bayes is a deceptively simple way to find answers to probability questions that involve many inputs. For example, if you're a website owner, you might be interested to know the probability that a visitor will make a purchase. That question has a lot of "what-ifs", including time on page, pages visited, and prior visits. Naive Bayes essentially allows you to take the raw inputs (i.e. historical data), sort the data into more meaningful chunks, and input them into a formula. …

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Added by Stephanie Glen on April 25, 2019 at 10:00am — No Comments

Frequencies in Pandas Redux

 

A little less than a year ago, I posted a blog on generating multivariate frequencies with the Python Pandas data management library, at the same time showcasing Python/R graphics interoperability. For my…

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Added by steve miller on April 25, 2019 at 5:33am — No Comments

What is the Difference Between Hadoop and Spark?

Hadoop and Spark are software frameworks from Apache Software Foundation that are used to manage ‘Big Data’. There is no particular threshold size which classifies data as “big data”, but in simple terms, it is a data set that is too high in volume, velocity or variety such that it cannot be stored and processed by a single computing…

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Added by Divya Singh on April 24, 2019 at 8:30pm — No Comments

Some Fun with Gentle Chaos, the Golden Ratio, and Stochastic Number Theory

So many fascinating and deep results have been written about the number (1 + SQRT(5)) / 2 and its related sequence - the Fibonacci numbers - that it would take years to read all of them. This number has been studied both for its applications (population growth, architecture) and its mathematical properties, for over 2,000 years. It is still a topic of active research.…

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Added by Vincent Granville on April 24, 2019 at 4:00pm — No Comments

How a Data Catalog Connects the What and the Why

  

"Technology is nothing in itself. What's important is that you have faith in people, that they're basically good and smart, and if you give them tools, they'll do wonderful things with them."

                                                                Steve Jobs

The what and the…

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Added by Shilpi Agarwal on April 24, 2019 at 3:30pm — No Comments

What is Augmented Analytics and Can it Add Value to Business Intelligence?

The Merriam-Webster dictionary defines the word 'augment' this way: 'to make greater, more numerous, larger or more intense'. If you are wondering how this applies to the term 'augmented analytics', you are not alone. Let's take a closer look at Augmented Analytics and talk about why it has gotten so much attention in the business intelligence world.

What is Augmented…

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Added by Kartik Patel on April 24, 2019 at 3:00am — No Comments

Free book – learn azure in a month of lunches – covering Cloud, AI, devops etc

 

 

 

At the Data Science for IoT course at the University of Oxford – I have been working on a strategy implementing Artificial Intelligence holistically on the Cloud and Edge. This is a complex approach with many new concepts to learn.…

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Added by ajit jaokar on April 23, 2019 at 11:00am — No Comments

Causality – The Next Most Important Thing in AI/ML

Summary:  Finally there are tools that let us transcend ‘correlation is not causation’ and identify true causal factors and their relative strengths in our models.  This is what prescriptive analytics was meant to be.

 

Just when I thought we’d figured it all out, something comes along to make…

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Added by William Vorhies on April 22, 2019 at 8:47am — No Comments

How Industrial IoT is Shaping the Modern World?

The world has taken several leaps in the technical aspects in the past few years. This has resulted in tremendous growth for the entire globe. With proper modes of communication and travel, the world has discovered ways of becoming stronger by every passing day.

With the flow of time, technology evolved and discovered those…

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Added by Sanjeev Verma on April 22, 2019 at 12:24am — No Comments

Building machine learning models in Apache Spark using SCALA in 6 steps

Introduction:

When dealing with building machine learning models, Data scientists spend most of the time on 2 main tasks when building machine learning models

Pre-processing and Cleaning

The major portion of time goes in to collecting, understanding, and analysing, cleaning the data and then building features. All the above steps mentioned are very important and critical to build successful machine learning…

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Added by Rohit Walimbe on April 21, 2019 at 9:00pm — No Comments

Data Science Central Weekly Digest, April 22

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this…

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Added by Vincent Granville on April 21, 2019 at 10:00am — No Comments

Unsolved Problems in Machine Learning

Quora contribution written by Chomba Bupe.

I am actually not even aware of any machine learning (ML) problem that is considered to have been solved recently or in the past. This tells you a lot about how hard things really are in ML. Of course, if you read media outlets, it may seem like researchers are sweeping the floor clean with deep learning (DL), solving ML problems one…

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Added by Andrea Manero-Bastin on April 21, 2019 at 6:00am — No Comments

Data Curation: Weaving Raw Data into Business Gold

The Big Data craze caught fire with a provocative declaration that “Data is the New Oil”; that data will fuel the economic growth in the 21stcentury in much the same way that oil fueled the economic growth of the 20thcentury.  The “New Oil” analogy was a great way to contextualize the economic value of data; to give the Big Data conversation an easily recognizable face.  The Economist recently declared data “…

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Added by Bill Schmarzo on April 19, 2019 at 11:15am — No Comments

What my first Silver Medal taught me about Text Classification and Kaggle in general?

Kaggle is an excellent place for learning. And I learned a lot of things from the recently concluded competition on Quora Insincere questions classification in which I got a rank of 182/4037. In this post, I will try to provide a summary of the things I tried. I will also try to summarize the ideas which I missed but were a part of other winning solutions.



As a side note: if you want to know more about NLP, I would…

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Added by Rahul Agarwal on April 19, 2019 at 10:15am — No Comments

Data Science Central Thursday Digest, April 18

Here is our selection of featured articles and resources posted since Monday:

Resources

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Added by Vincent Granville on April 18, 2019 at 12:00pm — No Comments

Reinforcement Learning Explained: Overview, Comparisons and Applications in Business

Imagine you’re completing a mission in a computer game. Maybe you’re going through a military depot to find a secret weapon. You get points for the right actions (killing an enemy) and lose them for the wrong ones (falling into a pit or getting hit). If you’re playing on high difficulty, you might not conclude this task in just one attempt. Try after try, you learn which consecutive actions are needed to get out of a location safe, armed, and equipped with bonuses like extra health points or…

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Added by Kateryna Lytvynova on April 18, 2019 at 2:00am — No Comments

What is the Difference Between AI and Machine Learning

Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in.

In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI…

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Added by Divya Singh on April 17, 2019 at 9:00pm — No Comments

34 Great Articles and Tutorials on Clustering

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on…

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Added by Vincent Granville on April 17, 2019 at 8:30am — No Comments

Free Python Data Science coding Book series

In this post, I explain

  1. How you can participate further in the free book series which we are launching based on the early experiences and
  2. Useful resources we recommend based on our experience for learning coding for Data Science (using Python – tensorflow and keras)

 

To provide some context, I posted…

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Added by ajit jaokar on April 17, 2019 at 8:06am — No Comments

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