Also: How To Grow As A Data Scientist; Training and Visualising Word Vectors; Using Genetic Algorithm for Optimizing RNNs; Top 10 Machine Learning Algorithms for Beginners; Comparing Machine Learning as a Service
Join Insurance Nexus as we talk to MetLife, Chubb and Nationwide about how to prioritize investments and internal resources. Learn which innovations will have the biggest impact on customer experience and improved profitability.
This post will take a different approach to constructing pipelines. Certainly the title gives away this difference: instead of hand-crafting pipelines and hyperparameter optimization, and performing model selection ourselves, we will instead automate these processes.
KDD-2018 invites submission of papers describing innovative research on all aspects of data science, and of applied papers describing designs and implementations for practical tasks in data science. Submissions due Feb 11.
This workshop will bring together experts in bio-acoustics with mathematicians and computer scientists with expertise in classification, clustering, and information theory to develop a unified approach. Apply by March 5.
We discuss 3Vs of Big Data; Infonomics and many aspects of monetizing information including promising analytics methods, successful companies, main challenges; Information marketplaces and why data ownership concept is misguided, and more.
This live online training is geared towards one single goal – to prepare developers and operations specialists who need to interact with GraphDB in their daily routine. For a limited time get 20% Early Bird discount.
In order for a data scientist to grow, they need to be challenged beyond the technical aspects of their jobs. They need to question their data sources, be concise in their insights, know their business and help guide their leaders.
Also: Advice For New and Junior #DataScientists; 5 EBooks to Read Before Getting into A #MachineLearning Career; #Blockchain or Bullshit; 30 Essential #DataScience, #MachineLearning & #DeepLearning Cheat Sheets
Join Team Anaconda for a live webinar, Jan 30, 2pm CT, as we tackle the four main concerns we hear from our customers and show you best practices for managing enterprise data science: scalability, security, integration, and governance.
In this post, we will be using grid search to optimize models built from a number of different types estimators, which we will then compare and properly evaluate the best hyperparameters that each model has to offer.
In this article we explore how to calculate machine learning model metrics, using the example of fraud detection. We'll see lots of different ways that we can try to understand just how good our learned model is.
The agenda is live for Predictive Analytics World for Business and Predictive Analytics World for Financial Services Las Vegas — June 3-7, 2018 at Caesars Palace — and we wanted to make sure that you are the first to know.
The MSc in Digital Marketing & Data Science is a 16-month programme designed to grow a new generation of leading marketing specialists – digital savvy professionals. Get 10% tuition fee waiver by submitting online application by Feb 1, 2018.
This article is about implementing Deep Learning (DL) using the H2O package in R. We start with a background on DL, followed by some features of H2O's DL framework, followed by an implementation using R.
Also: Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2: Integrating Grid Search; Generative Adversarial Networks, an overview; Learning Curves for Machine Learning; Top 10 TED Talks for Data Scientists and Machine Learning Engineers
But how do you learn data science? Let’s take a look at some of the steps you can take to begin your journey into data science without needing a degree, including Springboard’s Data Science Career Track.
This post shows you how to label hundreds of thousands of images in an afternoon. You can use the same approach whether you are labeling images or labeling traditional tabular data (e.g, identifying cyber security atacks or potential part failures).
But how do we diagnose bias and variance in the first place? And what actions should we take once we've detected something? In this post, we'll learn how to answer both these questions using learning curves.
This newly revised book presents two topics which are in most cases separated: machine learning (the design of flexible models from data) and intelligent optimization (the automated creation and selection of improving solutions). Free download!
How can insurance carriers gather and integrate data, and more importantly, effectively generate actionable insights to turn data into value? Get a whitepaper with best practices, including data collection, analytics, and value-add services.
We review recent developments and tools in topological data analysis, including applications of persistent homology to psychometrics and a recent extension of piecewise regression, called Morse-Smale regression.
Governance roles for data science and analytics teams are becoming more common... One of the key functions of this role is to perform analysis and validation of data sets in order to build confidence in the underlying data sets.
This is the narrative of a typical AI Sunday, where I decided to look at building a sequence to sequence (seq2seq) model based chatbot using some already available sample code and data from the Cornell movie database.
In this article, we’ll explain GANs by applying them to the task of generating images. One of the few successful techniques in unsupervised machine learning, and are quickly revolutionizing our ability to perform generative tasks.
Also: How Docker Can Help You Become A More Effective Data Scientist; Regularization in Machine Learning; Democratizing Artificial Intelligence, Deep Learning, Machine Learning with Dell EMC Ready Solutions; Quantum Machine Learning: An Overview
This article will help you understand why we need the learning rate and whether it is useful or not for training an artificial neural network. Using a very simple Python code for a single layer perceptron, the learning rate value will get changed to catch its idea.
Edge-based inferencing will become a foundation of all AI-infused applications in the Internet of Things and People and the majority of new IoT&P application-development projects will involve building the AI-driven smarts for deployment to edge devices for various levels of local sensor-driven inferencing.
If you are a data scientist who wants to capture data from such web pages then you wouldn’t want to be the one to open all these pages manually and scrape the web pages one by one. To push away the boundaries limiting data scientists from accessing such data from web pages, there are packages available in R.
OpenMinTED invites researchers, service providers and SMEs to submit proposals related to the development and integration of existing text mining/NLP applications or software components. Apply by Jan 26, 2018.
This year, RE-WORK will be continuing the Global Healthcare Series, focusing on the AI and deep learning tools and techniques set to revolutionise healthcare applications, medicine & diagnostics. Save an additional 20% on already discounted passes with the code: KDNUGGETS
What’s data science going to look like in 2018? How are job roles in the field going to change? Will AI find new ways to capture the public imagination? Learn more from Packt $5 books - on sale till Jan 16.
Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.
Artificial General Intelligence (AGI) in less than 50 years; Top KDnuggets tweets: 10 Free Must-Read Books for #MachineLearning and #DataScience; The Art of Learning #DataScience; Supercharging Visualization with Apache Arrow; Docker for #DataScience
MADS Can Help You Achieve Your 2018 Goals in San Francisco, April 11-13, 2018. Hear from speakers like DJ Patil, Former U.S. Chief Data Scientist, as he reveals the secrets to navigating the digital transformation. Save 20% with VIP Code MADS18KDN.
This article contains a lot of links to resources that I think are very helpful in getting you started to "think like a data scientist" which in my opinion is the most important step of the transition. I hope that you find this useful.
More generally, in evaluating any data mining algorithm, if our test set is a subset of our training data the results will be optimistic and often overly optimistic. So that doesn’t seem like a great idea.
Artificial General Intelligence (AGI) will likely be achieved in less than 50 years, according to latest KDnuggets Poll. The median estimate from all regions was 21-50 years, except in Asia where AGI is expected in 11-20 years.
Quantum Machine Learning (Quantum ML) is the interdisciplinary area combining Quantum Physics and Machine Learning(ML). It is a symbiotic association- leveraging the power of Quantum Computing to produce quantum versions of ML algorithms, and applying classical ML algorithms to analyze quantum systems. Read this article for an introduction to Quantum ML.
This article wants to give a flavor of the potentialities realized at the intersection of AI and Blockchain and discuss standard definitions, challenges, and benefits of this alliance, as well as about some interesting player in this space.
This article presents our opinions and suggestions on how an Advanced Analytics department should operate. We hope this will be useful for those who want to implement analytics work in their company, as well as for existing departments.
Also: 70 Amazing Free Data Sources You Should Know; Industry Predictions: Main AI, Big Data, Data Science Developments in 2017 and Trends for 2018; Can I Become a Data Scientist: Research into 1,001 Data Scientist Profiles; Yet Another Day in the Life of a Data Scientist
The Technically Speaking webcast series provides real-word case studies with key insights on overcoming the challenges in data collection, preparation, and analysis - find the webcast that fits your current challenge.
In this webinar, Jan 11, DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.
KDnuggets founder, Gregory Piatetsky-Shapiro, joins Michael Li, CEO and founder of The Data Incubator, Jan 11 at 2:30 pm PT/ 5:30 pm ET for their monthly webinar series, Data Science in 30 Minutes. Gregory will discuss his career, from AI to Data Mining to KDD to Data Science and back to AI, and examine current trends in the field.
It’s really hard to find predictions about the future made in the 1950’s. I decided to review the most popular sci-fi movies from 1950’s, and provide my perspective as to what these movies might tell us about 2018.
Coming from a statistics background I used to care very little about how to install software and would occasionally spend a few days trying to resolve system configuration issues. Enter the god-send Docker almighty.