At Predictive Analytics World for Business in Chicago, June 19-22, many of the sessions and workshops cover advanced predictive modeling methods. Register for PAW Business Chicago now with super early bird rates.
Deep Learning is on everyone's list of top skills to learn in 2017. ODSC Masterclass Summit, San Francisco, March 1-2 offers 2 intense days of hands-on training in deep learning. Use discount code: KD25 for an extra 25% Off before it expires Jan 31 at 11 PM!
Learn about the latest trends in data science applications in technology from the top experts in the industry. Register by Feb 8 and save with code KDNuggetsVIP.
This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.
6 Areas of AI and Machine Learning to Watch Closely; Pandas Cheat Sheet; Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms; Chatbots on Steroids: 10 Key Machine Learning Capabilities to Fuel Your Chatbot; The Top Predictive Analytics Pitfalls to Avoid
IoT applications have to collect and analyze information from multiple heterogeneous objects. Dealing with multiple sensors and internet connected objects, at multiple levels, requires attention. Read on to find out more.
This post is a look at the factors  --  public fears and a loss of investor appetite  --  that could thwart AI progress... if we don’t pay them enough attention.
Read the keynote and tutorial presentations on important topics including Big Data and Privacy, Mining Unstructured Text Data, Database Decay and How to Avoid It, Trajectory Data Mining, and more.
The Pandas library can seem very elaborate and it might be hard to find a single point of entry to the material: with other learning materials focusing on different aspects of this library, you can definitely use a reference sheet to help you get the hang of it.
No matter how advanced is your Machine Learning algorithm, the results will be bad if the input data
is bad. We examine one popular IMDB dataset and discuss how an analyst can deal with such data.
Bot bots bots... Read this overview of how artificial intelligence and natural language processing are contributing to chatbot development, and where it all goes from here.
The Marketing Analytics and Data Science conference is happening April 3-5 in San Francisco. Exclusive KDnuggets offer saves you 20% with VIP Code MADS17KDN. Register today!
The Most Popular Language For #MachineLearning and #DataScience; Introduction to Geospatial Data with #Python; The #DataScience Puzzle, Revisited; Best #DataScience Books and Articles That Will Surge Your Career
Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.
This article covers the value of understanding the virtualization constructs for the data scientist and data engineer as they deploy their analysis onto all kinds of cloud platforms. Virtualization is a key enabling layer of software for these data workers to be aware of and to achieve optimal results from.
Interested in learning machine learning algorithms by implementing them from scratch? Need a good set of examples to work from? Check out this post with links to minimal and clean implementations of various algorithms.
Regis University is proud to announce a new on-campus option for our Master of Science in Data Science program, which started in January 2017 in Denver, CO.
Dataiku launched a survey a few months back to find out how companies handled going from designing to deploying a data product. Read the report and learn four ways that companies approach production.
Many analytic projects fail to understand the business problem they are trying to solve. Correctly applying decision modeling in the Business Understanding phase of CRISP-DM brings clarity to the business problem.
Researchers at Maluuba are developing ways to teach artificial agents how to seek information actively, by asking questions. This includes a deep neural agent that learns to accomplish these tasks through efficient information-seeking behaviour, a vital step towards Artificial General Intelligence.
The summit will offer you many opportunities to network with over 250 thought leaders and to dig into multiple aspects within big data. Save 20% with code KDN20.
Successful analytics in the big data era does not start with data and software, but with immersive, interactive training and goal-driven strategy. TMA’s live online and classroom training spans all skill levels and analytic team roles to build analytic leaders. Live Online in February; Dubai U.A.E in March; or Washington, DC in April.
Predictive modelling and machine learning are significantly contributing to business, but they can be very sensitive to data and changes in it, which makes it very important to use proper techniques and avoid pitfalls in building data science models.
As chatbots become a common practice, the need for smarter bots arises. Empowering your bot with machine learning capabilities can really differentiate it from the rest. Check out these 10 capabilities to help fuel your chatbot.
The Master in Business Analytics & Big Data from the IE School of Human Sciences and Technology is an innovative degree offered in a Full-Time (Madrid) and Part-Time (Madrid & Dubai) format to train the new generation of data professionals.
Time Series Analysis: A Primer; Deep Learning Can be Applied to Natural Language Processing; The Current State of Automated Machine Learning; Introduction to Forecasting with ARIMA in R; 90 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning
GBM is one the hottest machine learning methods. Learn how to create GBM using SciKit-Learn and Python and
understand the steps required to transform features, train, and deploy a GBM.
The data science puzzle is re-examined through the relationship between several key concepts in the realm, and incorporates important updates and observations from the past year. The result is a modified explanatory graphic and rationale.
Learn how to get started with predictive modeling and overcome strategic and tactical limitations that cause data mining projects to fall short of their potential. Next webinar is Feb 14.
How does AI help sales and marketing teams in the organisation? Let’s understand Dos and don’ts of sales calls with the help of analysis of over 70,000+ B2B SaaS sales calls.
The performance of automated machine learning tool MLJAR on Kaggle competition data is presented in comparison with those from other predictive APIs from Amazon, Google, PredicSis and BigML.
Diffract-and-destroy experiments to accurately determine three-dimensional structures of nano-scale systems can produce 150 TB of data per sample. We review how such Big Data is processed.
Whatever you want to learn about data, you’ll find it at Strata + Hadoop World. Take a look at the program and see for yourself, and register by midnight January 20 with code PCKDNG and save up to $670 on your pass.
CRISP-DM is the leading approach for managing data mining, predictive analytic and data science projects. CRISP-DM is effective but many analytic projects neglect key elements of the approach.
What is automated machine learning (AutoML)? Why do we need it? What are some of the AutoML tools that are available? What does its future hold? Read this article for answers to these and other AutoML questions.
Do you make any new year resolutions? Hit the gym more often? Lose that last 10 pounds? While personal resolutions often get a bad rap, setting professional goals at the start of the new year is not necessarily a bad idea. Check out one data scientist's new year resolutions for 2017.
Time series analysis is a complex subject but, in short, when we use our usual cross-sectional techniques such as regression on time series data, variables can appear "more significant" than they really are and we are not taking advantage of the information the serial correlation in the data provides.
The Modern Data Science Academy provides state-of-the-art workshops taught by San Diego Supercomputer Center experts. Coming workshops on Machine Learning (Feb 8-9) and NoSQL Databases (Mar 1-2).
Stay up-to-date in the data science with active blogs. This is a list of 90 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. In this tutorial, we walk through an example of examining time series for demand at a bike-sharing service, fitting an ARIMA model, and creating a basic forecast.
Here is a challenge to contribute to the world health, organised by Camelyon17 and IEEE. Come forward to build a healthy world. Submission deadline is April 1, 2017s.
This post is a rebuttal to a recent article suggesting that neural networks cannot be applied to natural language given that language is not a produced as a result of continuous function. The post delves into some additional points on deep learning as well.
There are only 10 days to go until the Las Vegas Data & Analytics Summits, and only a handful of passes left. If you haven't had a chance to register yet, do it now!
The Most Popular Language For Machine Learning and Data Science; Analytics and Data Science Make Business Smarter; Exclusive Interview with Jeremy Howard; 5 Machine Learning Projects You Can No Longer Overlook, January
My exclusive interview with rock star Data Scientist Jeremy Howard, on his latest Deep Learning course, what is needed for success in Kaggle, how Enlitic is transforming medical diagnostics, and what Data Scientists should do to create value for their organization.
An analysis of NYC Open Data health inspections showing that craft beer bar kitchens in Manhattan are cleaner than the average establishment by a statistically significant margin. An encouraging finding for Dry January.
The most important traits for a good data analyst or data miner are curiosity, creativity and intuition for how to answer important questions using data. Read this white paper to learn more.
2016 saw some big leaps in chatbot technologies (along with a few unforeseen embarrassments). Get a quick review of the big events in the space over the past year, complete with supporting videos.
Predictive Analytics World for Business is heading to San Francisco this Spring. The deadline to save with Super Early Bird rates is around the corner, but act today and save an additional $150 using discount code KDN150.
Big data craze inspires firms to save every possible bit of data, with the misconception that the more data you have, the better. Firms must keep data (for compliance purposes) or often aren’t sure what information they need to keep. This post looks at alternative data sources.
Big Data does not convert data into actionable information. Big Data does not create value. But Data Science does, and it does not have to be complex or expensive, or even big.
A lot happened in IoT during 2016. Read this post for a briefing on some of the most important events, how they unfolded, and what they mean moving forward, complete with select videos to reinforce and elaborate.
Data preparation is usually the most time consuming part of a data analysis project. To get good results, follow the six steps here, starting with Understand the Business Needs, Get to Know the Data, and Wrangle, Munge, and Mash Up.
Online courses at Statistics.com are small, with rich and engaging content that includes readings, videos, quizzes, homework, projects, and practical work with software. Use promo code deepkdn17 to save.
When it comes to choosing programming language for Data Analytics projects or job prospects, people have different opinions depending on their career backgrounds and domains they worked in. Here is the analysis of data from indeed.com with respect to choice of programming language for machine learning and data science.
2016 was a big year for electric and driverless cars. Get a quick review with relevant videos on some of the events of interest in the field during the past year.
Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment; Huggable Image Classifier; xkcd: Linear Regression; AlphaGO WINS!; TensorFlow Fizzbuzz
Discover advances in deep learning tools and techniques from the world's leading innovators across industry, academia and the healthcare sector at the Deep Learning in Healthcare Summit in London, 28 February – 1 March. Use discount code KDNUGGETS to save 20%.
Merrimack College online MS in Data Science and MS in Business Analytics degrees provide a personalized and convenient curriculum that teaches the skills employers demand.
We analyzed more than 400 thousand reviews of unlocked mobile phones sold on Amazon.com to find out insights with respect to reviews, ratings, price and their relationships.
A top statistics professor and statistical researcher reflects on a number of awesome accomplishments by individuals in, and related to, the fields of statistics and data science, with a focus on the world of academia but with resonance far beyond.
We recognize our top blogs and bloggers in December 2016, who wrote about Data Science and Machine Learning Cheat Sheets, Machine Learning & AI Main Developments in 2016 and Key Trends in 2017, and more.
Harrisburg University of Science and Technology invites you to attend the FREE 2017 Data Analytics Summit, March 9 & 10, 2017, where the theme will be Analytics Applied: Case Studies, Measuring Impact, and Communicating Results. RSVP now!
2017 is here. Check out an encore installation in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.
Learn to expert presentations from leading companies (including IBM Watson, Microsoft, and Nokia Bell Labs) with a strong focus on real-world examples of deployed predictive analytics techniques.
5 Machine Learning Projects You Can No Longer Overlook, January; Machine Learning and Cyber Security Resources; Generative Adversarial Networks - Hot Topic in Machine Learning; Ten Myths About Machine Learning
Social media like twitter, facebook are very important sources of big data on the internet and using text mining, valuable insights about a product or service can be found to help marketing teams. Lets see, how healthcare companies are using big data and text mining to improve their marketing strategies.
This course will examine how firms can take big data to big profits through data monetization strategies and the best use of data science for growth and innovation across your organization.
The surprising finding is that people are much more willing to ride in a self-driving car that might kill them to save several pedestrians than in a car that would save them but kill pedestrians. Asian respondents had significantly different preferences from US and Europe.
Image tagging with user generated content in the wild, without the use of curated image datasets? Read more about this paper and its promising research.
Digital transformation that allows insurers to become more agile and customer-facing is fuelling growth globally. But what are Insurers in Asia doing about their digital transformation? Read this exclusive white paper.
Data scientists at Foodpairing help brands cut down on the fuzzy front end of product development. The so-called Consumer Flavor Intelligence combines internet data and food science to create timely flavor line extensions.
Report from an important IEEE workshop on Human Use of Machine Learning, covering trust, responsibility, the value of explanation, safety of machine learning, discrimination in human vs. machine decision making, and more.
WSN and RFID are key to understanding more complex IoT concepts and technologies, but also the structure of non-trivial IoT systems, which are very likely to comprise RFID or WSN components.
Let TDWI Las Vegas give you a head start with the latest skills in analytics, data science and data management this February 12-17. Save 10% now with priority code VEGAS24!
Discover advances in Deep Learning, NLP, speech recognition, image retrieval, virtual assistants, and more from leading researchers and industry at the Deep Learning Summit and Virtual Assistant Summit in San Francisco, 26-27 January. Use code KDNUGGETS to save 20%.
Data science and predictive analytics can provide huge value, but they can mislead and backfire if not used with fail-safe measures. The author gives examples of such problems and provides guidelines to avoid them.
Matplotlib is the most widely used data visualization library for Python; it's very powerful, but with a steep learning curve. This overview covers a selection of plots useful for a wide range of data analysis problems and discusses how to best deploy each one so you can tell your data story.
OpenAI Universe; Deep Learning For Coders—18 hours of lessons for free; Elon Musk on Twitter: Tesla Autopilot vision neural net now working well; Apple to Start Publishing AI Research; Duolingo's "half-life regression" method for modeling human memory
R vs Python: A Comparison and Free Books to Learn; The Five Capability Levels of Deep Learning - Yann Lecun view; The Future Of Machine Learning, McKinsey 2016 Analytics Study; #BigData: Main Developments in 2016 and Key Trends in 2017
What can happen in the not too distant future when advanced technologies like a Self-Driving car and Machine Learning Recommendations Engine are combined ...
To stay competitive in machine learning business, you have to be superior than your rivals and not the best possible – says one of the leading machine learning expert. Simple rules are defined here to make that happen. Let’s see how.
This post summarizes some tidying examples Hadley Wickham used in his 2014 paper on Tidy Data in R, but will demonstrate how to do so using the Python pandas library.
Springboard, a leader in data science education, is launching the first data science bootcamp to guarantee you a data science career -- or your money back. The program is very selective, so apply quickly to see if you qualify.
Based on the trainers book, this course presents a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
There is growing support for revenue per employee as one of the most underrated metrics available for assessing business performance in a crowded marketplace.
Coming soon: Global AI Santa Clara, Big Data Innovation Las Vegas, CAO Miami, Deep Learning Summit San Francisco, PAw Manufacturing Dusseldorf, WSDM 2017 Cambridge UK, AnacondaCON Austin, TDWI Las Vegas, and more.
AnacondaCON '17 will help you conquer your biggest data science challenges. Learn from industry experts sharing what #OpenDataScienceMeans and their best practices. Get 2 for 1 ticket price thru Jan 16, 2017.
What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.
In both statistics and machine learning, outlier detection is important for building an accurate model to get good results. Here three methods are discussed to detect outliers or anomalous data instances.
Myths on artificial intelligence and machine learning abound. Noted expert Pedro Domingos identifies and refutes a number of these myths, of both the pessimistic and optimistic variety.
We examine what Uber has done that drives success in many markets across the globe and why so many businesses are seeking an Uber-style solution to their business. We present a listing of lessons on what to do if you are seeking to Uber-ize your business model.
Game Theory Reveals the Future of Deep Learning; A Funny Look at Big Data and Data Science; Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017; How I Detect Fake News, by Tim O’Reilly
The forum is a cross industry, vendor-neutral event where global senior speakers will present case studies, share their expertise in predictive analytics, data management, and digital transformation of their companies. Register by Jan 20 to get 20% off.
An overview of useful resources about applications of machine learning and data mining in cyber security, including important websites, papers, books, tutorials, courses, and more.
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
Academic Director Data Science Notre Dame, Postdocs in ML at Oxford, PhD in Q/A at Bonn, Faculty at Washington Jefferson College, Faculty in Machine Learning at Edinburgh, and more.
Deep Learning Research Review: Natural Language Processing
Data Scientist – best job in America, again
6 areas of AI and Machine Learning to watch closely
Big Data and the Internet of Things don’t make business smarter, Analytics and Data Science do
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