Introduced by IAPA, the largest analytics association in Australia, the Credential recognises analytics skills and capabilities benchmarked to global industry standards. Learn how to apply and be certified.
In Data Science, Gradient Descent is one of the important and difficult concepts. Here we explain this concept with an example, in a very simple way. Check this out.
The frontend code of programming languages only needs to parse and translate source code to an intermediate representation (IR). Deep Learning frameworks will eventually need their own “IR.”
How to deal with data analysis and limited resources: Computational power, data distribution, energy or memory? Learn at TU Dortmund International Summer School. Apply by July 15.
MIT Sloan professor builds new meta-analysis method to help settle unresolved debates. New method more accurately estimates basal metabolic rates and other effects only using prior studies.
The author disagrees with a previous KDnuggets post on “Why Hadoop is Failing” and argues that the Darwinian Open Source Ecosystem ensures Hadoop is a robust and mature technology platform .
This blogpost gives a quick example using Dask.dataframe to do distributed Pandas data wrangling, then using a new dask-xgboost package to setup an XGBoost cluster inside the Dask cluster and perform the handoff.
Also Practical #DeepLearning For Coders-18 hours of free lessons; Different views of #Machinelearning #cartoon #humor; Scikit-learn #MachineLearning classification algorithms.
Successful analytics at the organizational-level starts 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 May and September, Seattle in July and Wash-DC in October.
Image/video data analysis is surging, JSON replacing XML, anonymized data usage is growing in US and Europe (but not in Asia), itemsets and Twitter analysis is declining - some of the highlights of KDnuggets Poll on data types used.
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 May 16.
Analytics can be used to provide a boost to the cure of depression. How analytics is being adopted by companies like Microsoft, Facebook to handle and detect vulnerable targets of depression.
This is a no-nonsense overview of implementing a recurrent neural network (RNN) in TensorFlow. Both theory and practice are covered concisely, and the end result is running TensorFlow RNN code.
Choose from over 175 sessions in 10 different tracks, including Developer, Data Science, Enterprise Applications, Machine Learning, Streaming and Spark Experience & Use Cases. Save 15% with code KDNUGGETS.
Applying Machine Learning to steel production is really hard! Here are some lessons from Yandex researchers on how to balance the need for findings to be accurate, useful, and understandable at the same time.
When something goes wrong, as it inevitably does, it can be a daunting task discovering the behavior that caused an event that is locked away inside a black box where discoverability is virtually impossible.
Efficient implementation is key to achieving the benefits of parallelization, even though parallelism is a good idea when the task can be divided into sub-tasks that can be executed independent of each other without communication or shared resources.
Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until April 29, 2017.
Awesome Deep Learning: Most Cited Deep Learning Papers; The Value of Exploratory Data Analysis; 10 Free Must-Read Books for Machine Learning and Data Science; Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions; Data Science for the Layman
Data not Constantly Maintained ->Data Becomes Irrelevant -> People Lose Trust -> Use Data Less. We examine 4 reasons for such wheel of death, and what can you do about it.
Learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors, on Tuesday, May 2nd at 1:00 pm ET.
This post outlines some very basic methods for performing financial data analysis using Python, Pandas, and Matplotlib, focusing mainly on stock price data. A good place for beginners to start.
Join us again this year in Chicago for Predictive Analytics World for Manufacturing, June 19-22, and receive 20% off current conference rates for two-day passes with the discount code MISSYOU.
This cartoon takes a vector space approach to your favorite drinks and examines the distance between Espresso and Cappuccino. Warning: this is only funny to Data Scientists and mathematicians.
AI Conference will focus on emerging technology with a specific focus around projects, teams and people who are working on Artificial General Intelligence and related topics. Use code KDnuggets to save on tickets.
If you cannot manage real-time streaming data and make real-time analytics and real-time decisions at the edge, then you are not doing IOT or IOT analytics, in my humble opinion. So what is required to support these IOT data management and analytic requirements?
We examine “citizen” data scientists and debate between Jeffersonians, who seek to empower everyday worker with data science tools, and Platonists who argue that democratizing data science leads to anarchy and overfitting.
This post introduces a curated list of the most cited deep learning papers (since 2012), provides the inclusion criteria, shares a few entry examples, and points to the full listing for those interested in investigating further.
Join Ramesh Johari, Associate Professor, Stanford Department of Management Science & Engineering, as he discusses some common practices and pitfalls in A/B testing, Apr 26, 2017.
AI is the hottest technology now. The organizers of Strata + Hadoop World Conferences are bring you new conference on AI in NYC in June. Use code PCKDNG to save.
Whether your every day tool is Scala, Python, R, or Excel, you can now use one tool - Dataiku - to transform raw data to predictions without the hassle. Discover the platform!
In this post, we will give a high level overview of what exploratory data analysis (EDA) typically entails and then describe three of the major ways EDA is critical to successfully model and interpret its results.
We expect data scientists to be objective, but intentionally or not, they can produce results that mislead. We examine three common types of “lies” that Data Scientists should be aware of.
This is an overview of recent research outlining the limitations of the capabilities of image recognition using deep neural networks. But should this really be considered a "limitation?"
Written for the layman, this book is a practical yet gentle introduction to data science. Discover key concepts behind more than 10 classic algorithms, explained with real-world examples and intuitive visuals.
Also Modern NLP in Python, or What you can learn about food by analyzing a million Yelp reviews; The Periodic Table of #DataScience; What is #Blockchain Technology?
The rise of conversational UI signals exciting progress for the BI world but there are pitfalls to be avoided. This blog presents 3 considerations for guiding your conversational UI implementation to ensure success and maximize the value of your data analytics.
These online courses, developed by Prof. Bart Baesens and SAS, include videos, case studies, quizzes, and focus on focusses on the concepts and modeling methodologies and not on specific software.
We see the need for a new type of Engineer who will combine knowledge from Electronics & IoT with Machine learning, AI, Robotics, Cloud and Data management (devops).
In this tutorial, we will see an example of how a Generative Additive Model (GAM) is used, learn how functions in a GAM are identified through backfitting, and learn how to validate a time series model.
We're gathering 400+ leading experts and healthcare professionals at the Deep Learning Summit, and Deep Learning in Healthcare Summit in Boston on May 25-26. KDnuggets subscribers get 20% off pass prices for all RE•WORK events with discount code KDNUGGETS.
Open source dominates the data management conversation. Postgres Vision, June 26-28, Boston, explores the business value realized from innovative solutions and strategies. Use code KDPV17 to save.
Companies can no longer afford to have product rollbacks or have wastage because of replacement parts. This is where the need for “Predictive Maintenance” comes into play.
MeaningCloud, leader in SaaS semantic analytics, has new RapidMiner extension that offers very powerful and flexible text analytics and the ability to extract the meaning of any unstructured text. Learn more in April 27 webinar.
In 2017 there are many new and revamped data science tracks that are much more comprehensive for beginners than ever before. The tracks are designed to give you the skills you need to grab a job in data science, and some even have a job guarantee.
This is an introduction to recent research which presents an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.
10 Free Must-Read Books for Machine Learning and Data Science; 5 Machine Learning Projects You Can No Longer Overlook, April; Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions; Top mistakes data scientists make when dealing with business people
DataScience Trends, a new interactive tool from DataScience Inc., gives users the ability to explore and visualize data across 2.8 million open source repositories without writing code.
Without doubt, critical thinking is necessary in order to be a good analyst but particular skills and experience are also required. What are some of these skills?
Is blockchain the ultimate enabler of data and analytics monetization; creating marketplaces where companies, individuals and even smart entities (cars, trucks, building, airports, malls) can share/sell/trade/barter their data and analytic insights directly with others?
Who leads in Data Science, Machine Learning, and Predictive Analytics? We compare the latest Forrester and Gartner reports for this industry for 2017 Q1, identify gainers and losers, and strong leaders vs contenders.
ACM SIGKDD, the premier global professional organization for data science and data mining, is calling for nominations for top awards in the field: Doctoral Dissertation Award, Test-of-time Award, Innovation Award (the "Nobel" prize of Data Science), and Service Award.
WordStat is a flexible and easy-to-use text analysis software – whether you need text mining tools for fast extraction of themes and trends, or careful and precise measurement with state-of-the-art quantitative content analysis tools. Now works on Macs.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape targeted towards both technical and non-technical people who want to understand the emerging world of Big Data. Use code KDNUGGETS to save.
There are no cover articles praising the fails of the many data scientists that don’t live up to the hype. Here we examine 3 typical mistakes and how to avoid them.
In this article we will talk about basics of deep learning from the lens of Convolutional Neural Nets. We plan to use this knowledge to build CNNs in the next post and use Keras to develop a model to predict lung cancer.
Catch this live webinar from Open Data, which will explain both streaming and batch analytic types, typical use cases for each, as well as the best way to deploy these analytics in production. It happens April 19th, 2017 at 10am PST (1pm EST).
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out. Find tools for data exploration, topic modeling, high-level APIs, and feature selection herein.
Explore the latest advancements in deep learning and their applications in industry and healthcare at the Deep Learning Summit and Deep Learning in Healthcare Summit in Boston, 25-26 May. Use discount code KDNUGGETS to save 20% off all tickets.
10 Free Must-Read Books for Machine Learning and Data Science; How to make beautiful data visualizations in Python with matplotlib; #DeepLearning in 7 lines of code; Data Science of Variable Selection: A Review.
Submit your research to HF, the first and only interdisciplinary journal on high-frequency data questions, including HF data assimilation, analysis, and/or methods for decision-making.
In this post, the author assembles a dataset of fake and real news and employs a Naive Bayes classifier in order to create a model to classify an article as fake or real based on its words and phrases.
Learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors.
Find out how to expand R capabilities with RStudio + sparklyr on Apache Spark on a fast cloud platform and how simple to get started in the cloud with Cazena Data Science Sandbox as a Service.
This post walks the reader through a real-world example of a "linkage" attack to demonstrate the limits of data anonymization. New privacy regulation, most notably the GDPR, are making it increasingly difficult to maintain a balance between privacy and utility.
Successful data teams at companies of any size are able to produce results because they develop gradually through a series of stages and acquire skills along the way that help them stay efficient and effective.
We know various job profiles in data science – data engineer, data scientist, data analyst etc. Here we explain how these roles fits in a real world data science team and what they do.
Train AI comes to San Francisco on May 17, 2017, and focuses on everything except the algorithm - the training data and feature selection and deployment issues that end up being 90% of the work. KDnuggets readers get 30% off registration with code KDAI30.
Spring. Rejuvenation. Rebirth. Everything’s blooming. And, of course, people want free ebooks. With that in mind, here's a list of 10 free machine learning and data science titles to get your spring reading started right.
Postgres Vision, June 26-28, Boston, will be a forum for the sharpest minds in open source as organizations strive to harvest greater strategic value and actionable insight from their data. Use code KDPV17 to save.
Predictive Analytics World Workforce (May 14-18, San Francisco) is pleased to announce the Weird Science Keynote by Eric Siegel, "How to Know Your Predictive Discovery Is Not BS."
Top 20 Recent Research Papers on Machine Learning and Deep Learning; A Brief History of Artificial Intelligence; Medical Image Analysis with Deep Learning; Introduction to Anomaly Detection; The 42 V’s of Big Data and Data Science
It's 2017 now, and we now operate in an ever more sophisticated world of analytics. To keep up with the times, we present our updated 2017 list: The 42 V's of Big Data and Data Science.
Chief Data Officer, Financial Services, West is coming to San Francisco on April 26th. Corinium Global Intelligence and KDnuggets would like to offer you an exclusive 30% discount to join us. Save over $500 using discount code KD30 during registration.
With change comes opportunity! The pace of data science innovation continues to quicken. New tools and techniques are constantly emerging especially around deep learning and machine learning. In 2017 many more companies are embarking on data science projects.
Learn how to keep your audience from struggling to understand your work, why others should review your experimentation process, how to build your experimental muscle, and more.
From care delivery to care coordination, diagnostics to population health management - AI, machine learning, big data, and cognitive computing are revolutionizing healthcare systems worldwide. Don't get left behind! Use code KDN15 to save.
Why are some people struck by lightning multiple times or, more encouragingly, how could anyone possibly win the lottery more than once? The odds against these sorts of things are enormous.
Data scientists tend to think that their main job is to answer complex questions and gain in-depth insights, bu in reality it is all about solving problems – and the only way to solve a problem is to act on it.
The world is experiencing a digital revolution: Big Data and Cybersecurity are now playing a key role in a growing number of diverse industries. Get the jump on the digital revolution with a graduate education from IE School of Human Sciences & Technology.
Machine learning and Deep Learning research advances are transforming our technology. Here are the 20 most important (most-cited) scientific papers that have been published since 2014, starting with "Dropout: a simple way to prevent neural networks from overfitting".
Also Self-driving talent is fleeing Google and Uber to catch the autonomous-driving; Using Docker, CoreOS For #GPU Based #DeepLearning; A Short Guide to Navigating the Jupyter Ecosystem.
The largest gathering of insurance analytics, data and technology executives in Canada in back in 2017 – the Insurance Analytics Canada Summit (June 28-29, Toronto). KDnuggets readers use code 4819KD200 to save.
Successful analytics starts 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. Washington, DC in April, Live Online in May and Seattle in July.
Two Data Science challenges were launched by UK Government agencies, including MI5 and MI6. One challenge involves classifying vehicles from aerial images, and another analyzing crisis reports. Can you take part and be the next James Bond?
Here is a proposed “7A” model that is useful enough to capture of the core of what AI offers without falsely implying there is a static body of best practices in this area.
An obvious metric we can look at for how much harm terrorists from the banned countries do to America is looking at the number of people killed on American soil by terrorists from these countries.
Detecting anomalous cases in large datasets is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Learn how to do it online in "Anomaly Detection" course at Statistics.com.
A Super Harsh Guide to Machine Learning; Google is acquiring data science community Kaggle; Suggestion by Salesforce chief data scientist; Andrew Ng resigning from Baidu; Distill: An Interactive, Visual Journal for Machine Learning Research
This overview will cover several methods of detecting anomalies, as well as how to build a detector in Python using simple moving average (SMA) or low-pass filter.
This conference shows real-world applications that balance of high-level rigor and business know-how and elevating the role of analytics in an organization's strategic decision-making. Hilary Mason keynotes.
We examine 2 common tactics by data "skeptics": demanding more precision and demanding unanimity. These techniques are especially effective against data scientists, who should be aware of them, and able to counteract them.
Standardization and Specialization in Analytics, Data Science, and BI; What is Structural Equation Modeling?; The Best R Packages for Machine Learning; What makes a great data scientist?; Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Intelligence
The exciting announcement yesterday of Deep Stubborn Networks (StubNets) introduces an innovative refinement to GANs, taking their development in a new direction.
Cartoon: Machine Learning – What They Think I Do
Keep it simple! How to understand Gradient Descent algorithm
Awesome Deep Learning: Most Cited Deep Learning Papers
10 Free Must-Read Books for Machine Learning and Data Science
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