Analytics Vidhya

MARCH 7, 2019

This article has been updated on Women’s Day, 2019. Introduction This women’s day, we at Analytics Vidhya are celebrating the power of women in. The post 29 Inspiring Women Blazing a Trail in the Data Science World appeared first on Analytics Vidhya.

Data Science 290

Data Science Analytics Analytics 290

Dataconomy

MARCH 7, 2019

Digital transformation dominates most CIO priority lists pertaining to questions such as: How will digital transformation affect IT infrastructure? Will technology live on-premise or in the cloud? Depending on where that data lives, an organization requires different skill sets. If you’re building these resources in-house, then you need an infrastructure.

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Big Data Big Data Analytics Analytics 174

Eugene Yan

MARCH 5, 2019

How we built an ML system to predict hospitalization costs at admission; sharing at DATAx Conference.

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Data Science 101

MARCH 7, 2019

Read to the end to learn more about a new study group I will be launching. In Late January 2019, Microsoft launched 3 new certifications aimed at Data Scientists/Engineers. For a while, Microsoft has been toying with different methods for training and credentials. They launched the Microsoft Professional Program in Data Science back in 2017. While it provides great content, it did not result in either a college diploma or an official Microsoft certification.

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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

AI

Analytics Vidhya

MARCH 5, 2019

Introduction The rapid rise of data science as a professional field has lured in people from all backgrounds. Engineers, computer scientists, marketing and finance. The post 11 Steps to Transition into Data Science (for Reporting / MIS / BI Professionals) appeared first on Analytics Vidhya.

Data Science 278

Data Science Analytics Analytics Business Intelligence 278

Dataconomy

MARCH 6, 2019

Patient privacy and safety have always been cornerstones of the U.S. healthcare system. But in today’s digital era, there are apps tracking the most sensitive information such as the female menstrual cycle and fertility window. The collection of this data might be valuable for the future of healthcare – the. The post Can Femtech deliver radically personalized care to women appeared first on Dataconomy.

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Analytics Analytics Machine Learning Machine Learning 156

Data Science 101

MARCH 6, 2019

University can be a great way to learn data science. However, many universities are very expensive, difficult to get admitted, or not geographically feasible. Luckily, a few of them are willing to share data science, machine learning and deep learning materials online for everyone. Here is just I small list I have come across lately. MIT Deep Learning – Lecture notes, slides and guest talks about deep learning and self driving cars Introduction to Artificial Intelligence from UC Berkeley &

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Data Science Deep Learning Deep Learning Machine Learning 80

Analytics Vidhya

MARCH 6, 2019

Introduction Machine learning is disrupting multiple and diverse industries right now. One of the biggest industries to be impacted – finance. Functions like fraud. The post Hands-On Introduction to creditR: An Amazing R Package to Enhance Credit Risk Scoring and Validation appeared first on Analytics Vidhya.

Machine Learning 246

Machine Learning Machine Learning Analytics Analytics 246

Victor Zhou

MARCH 3, 2019

Here’s something that might surprise you: neural networks aren’t that complicated! The term “neural network” gets used as a buzzword a lot, but in reality they’re often much simpler than people imagine. This post is intended for complete beginners and assumes ZERO prior knowledge of machine learning. We’ll understand how neural networks work while implementing one from scratch in Python.

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Machine Learning Machine Learning Natural Language Processing Python 59

DataRobot

MARCH 4, 2019

Can You Trust That Your AI Was Built Correctly? It has been more than three years since the last time I competed in a data science competition, and yet there’s one memory about that competition that remains vivid in my mind. I had spent a busy week at my computer coding up a cool-looking solution, and I was ready to submit my first entry to the competition.

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Artificial Intelligence Artificial Intelligence Data Science AI 68

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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Database

Data Science 101

MARCH 2, 2019

A new community for data visualization professionals has launched. It is called the Data Visualization Society. According to the website, The Data Visualization Society aims to collect and establish best practices and foster community to support its members as they develop their data visualization skills. Currently, membership is free and they are looking for help growing the community.

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Data Visualization Data Science 75

Analytics Vidhya

MARCH 6, 2019

Introduction “People underestimate how complex intelligence is.” How close are we to Artificial General Intelligence (AGI)? It seems we take a step closer to. The post DataHack Radio #19: The Path to Artificial General Intelligence with Professor Melanie Mitchell appeared first on Analytics Vidhya.

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Analytics Analytics Data Science Artificial Intelligence 124

Depends on the Definition

MARCH 7, 2019

Today we want to build a model, that can identify ingredients in cooking recipes. I use the “German Recipes Dataset”, I recently published on kaggle. We have more than 12000 German recipes and their ingredients list.

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DataRobot

MARCH 6, 2019

The DataRobot AI Partner Program is continuing to evolve and grow, so we’d like to highlight our partners at Ironside , a leading data and analytics consulting firm with two decades of applied experience.

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Data Scientist Analytics Analytics AI 67

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

Data Science 101

MARCH 4, 2019

Brandon Rohrer (along with others ) created an excellent resource for academic programs, Industry recommendations for academic data science programs. The resource is authored by a number of industry data scientists and university faculty. It is collection of useful information for college data science programs. Here are some of the topics: What do Industry data scientists do?

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ML Review

MARCH 5, 2019

Different lenses to see through AI; motivations and introduction to our web app At MTank , we work towards two goals. (1) Model and distil knowledge within AI. (2) Make progress towards creating truly intelligent machines. As part of these efforts we release pieces about our work for people to enjoy and learn from. If you like our work, then please show your support by following, sharing and clapping your asses off.

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AI AI Support Vector Machines Natural Language Processing 52

DataCentric podcast

MARCH 7, 2019

Steve McDowell and Matt Kimball, senior datacenter analysts at Moor Insights & Strategy, leave their impressions from IBM's recent Think event, where IBM demonstrated impressive infrastructure while delivering a message focused on enterprise class problems. IBM has long been a trendsetter and early promoter of new architectures such as cloud, capacity on-demand, and solution deliver.

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ML Review

MARCH 5, 2019

Word embeddings (word2vec, fastText), paper embeddings (LSA, doc2vec), embedding visualisation, paper search and charts! At MTank, we work towards two goals: (1) Model and distil knowledge within AI. (2) Make progress towards creating truly intelligent machines. As part of these efforts we release pieces about our work for people to enjoy and learn from.

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Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Data Science