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!
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.
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.
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
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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%.
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!
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.
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.
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.
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
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.
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.
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.
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.
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.