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• Machine Learning Trends and the Future of Artificial Intelligence

  The confluence of data flywheels, the algorithm economy, and cloud-hosted intelligence means every company can now be a data company, every company can now access algorithmic intelligence, and every app can now be an intelligent app.

  https://www.kdnuggets.com/2016/06/machine-learning-trends-future-ai.html

• How open API economy accelerates the growth of big data and analytics

  An open API is available on the internet for free. We review the growth of API economy and how organizations have been realizing the potential of open APIs in transforming their business.

  https://www.kdnuggets.com/2016/06/open-api-economy-growth-big-data-analytics.html

• 10 Data Acquisition Strategies for Startups

  An interesting discussion of the myriad methods in which startups may choose to acquire data, often the most overlooked and important aspect of a startup's success (or failure).

  https://www.kdnuggets.com/2016/06/10-data-acquisition-strategies-startups.html

• Apache Spark Key Terms, Explained

  An overview of 13 core Apache Spark concepts, presented with focus and clarity in mind. A great beginner's overview of essential Spark terminology.

  https://www.kdnuggets.com/2016/06/spark-key-terms-explained.html

• Data Science of Variable Selection: A Review

  There are as many approaches to selecting features as there are statisticians since every statistician and their sibling has a POV or a paper on the subject. This is an overview of some of these approaches.

  https://www.kdnuggets.com/2016/06/data-science-variable-selection-review.html

• Ethics in Machine Learning – Summary

  Still worried about the AI apocalypse? Here we are discussion about the constraints and ethics for the machine learning algorithms to prevent it.

  https://www.kdnuggets.com/2016/06/ethics-machine-learning-mlconf.html

• What is the Difference Between Deep Learning and “Regular” Machine Learning?">What is the Difference Between Deep Learning and “Regular” Machine Learning?

  Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning.

  https://www.kdnuggets.com/2016/06/difference-between-deep-learning-regular-machine-learning.html

• 5 Reasons Machine Learning Applications Need a Better Lambda Architecture

  The Lambda Architecture enables a continuous processing of real-time data. It is a painful process that gets the job done, but at a great cost. Here is a simplified solution called as Lambda-R (ƛ-R) for the Relational Lambda.

  https://www.kdnuggets.com/2016/05/5-reasons-machine-learning-applications-lambda-architecture.html

• Embrace the Random: A Case for Randomizing Acceptance of Borderline Papers

  A case for using randomization in the selection of borderline academic papers, a particular use case which has parallels with many other possible scenarios.

  https://www.kdnuggets.com/2016/05/embrace-random-acceptance-borderline-papers.html

• Implementing Neural Networks in Javascript

  Javascript is one of the most prevalent and fastest growing languages in existence today. Get a quick introduction to implementing neural networks in the language, and direction on where to go from here.

  https://www.kdnuggets.com/2016/05/implementing-neural-networks-javascript.html

• Angoss 9.6 Data Science Software Suite

  Angoss software provides users with comprehensive scorecard building functionality that is fast, reliable, accurate, and business centric.

  https://www.kdnuggets.com/2016/04/angoss-9-6-data-science-software-suite.html

• Three Pitfalls to Avoid When Building Data Science Into Your Business

  An overview of pitfalls to avoid when building data science into your business, how to avoid them, and what to do instead.

  https://www.kdnuggets.com/2016/04/pitfalls-building-data-science-business.html

• When Does Deep Learning Work Better Than SVMs or Random Forests®?">When Does Deep Learning Work Better Than SVMs or Random Forests®?

  Some advice on when a deep neural network may or may not outperform Support Vector Machines or Random Forests.

  https://www.kdnuggets.com/2016/04/deep-learning-vs-svm-random-forest.html

• Does Your Company Need a Data Scientist?

  Your company needs a data scientist... doesn't it? It very well may not, but you need to know either way. Read on to determine whether or not your company could benefit from the skills of an on-board data scientist.

  https://www.kdnuggets.com/2016/04/your-company-need-data-scientist.html

• Deep Learning for Chatbots, Part 1 – Introduction

  The first in a series of tutorial posts on using Deep Learning for chatbots, this covers some of the techniques being used to build conversational agents, and goes from the current state of affairs through to what is and is not possible.

  https://www.kdnuggets.com/2016/04/deep-learning-chatbots-part-1.html

• Using Big Data Analytics To Prevent Crimes The “Minority Report” Way

  The idea of using artificial intelligence for the crime prevention has been around for more than a decade. In this post, we present four examples, including how using analytics, we can prevent a criminal from re-offending.

  https://www.kdnuggets.com/2016/04/using-big-data-analytics-prevent-crimes-minority-report-way.html

• What Developers Actually Need to Know About Machine Learning

  Some guidance on what, exactly, it is that developers need to know to get up to speed with machine learning.

  https://www.kdnuggets.com/2016/04/developers-need-know-about-machine-learning.html

• Basics of GPU Computing for Data Scientists

  With the rise of neural network in data science, the demand for computationally extensive machines lead to GPUs. Learn how you can get started with GPUs & algorithms which could leverage them.

  https://www.kdnuggets.com/2016/04/basics-gpu-computing-data-scientists.html

• What is the influence of Big Data in Medicine?

  The 360-degree customer view is the idea, that companies can get a complete view of customers by aggregating data from the various touch points that a user. And, big data is helping to materialize this idea, which will revolutionize the healthcare.

  https://www.kdnuggets.com/2016/03/influence-big-data-medicine.html

• Data Science and Disability

  Data Science and Artificial Intelligence has come to the forefront of technology in the last few years. Learn, how practitioners are taking a more philanthropic outlook on life, supporting people suffering with both physical and mental disabilities.

  https://www.kdnuggets.com/2016/03/data-science-disability.html

• 21 Must-Know Data Science Interview Questions and Answers, part 2

  Second part of the answers to 20 Questions to Detect Fake Data Scientists, including controlling overfitting, experimental design, tall and wide data, understanding the validity of statistics in the media, and more.

  https://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers-part2.html

• How IBM Watson is Taking on The World

  We have made tremendous progress in the field of data analysis and on the other, our technology is getting smart. IBM has taken a solid stride in the direction of Artificial Intelligence by unveiling its supercomputer IBM Watson, learn what it can do, its adopters and what it holds for the future.

  https://www.kdnuggets.com/2016/02/dezyre-ibm-watson-taking-world.html

• Amazon Machine Learning: Nice and Easy or Overly Simple?

  Amazon Machine Learning is a predictive analytics service with binary/multiclass classification and linear regression features. The service is fast, offers a simple workflow but lacks model selection features and has slow execution times.

  https://www.kdnuggets.com/2016/02/amazon-machine-learning-nice-easy-simple.html

• Gartner 2016 Magic Quadrant for Advanced Analytics Platforms: gainers and losers">Gartner 2016 Magic Quadrant for Advanced Analytics Platforms: gainers and losers

  We compare Gartner 2016 Magic Quadrant Advanced Analytics Platforms vs its 2015 version and identify notable changes for leaders and challengers: SAS, IBM, RapidMiner, KNIME, Dell, Angoss, and Microsoft.

  https://www.kdnuggets.com/2016/02/gartner-2016-mq-analytics-platforms-gainers-losers.html

• The ICLR Experiment: Deep Learning Pioneers Take on Scientific Publishing

  Deep learning pioneers Yann LeCun and Yoshua Bengio have undertaken a grand experiment in academic publishing. Embracing a radical level of transparency and unprecedented public participation, they've created an opportunity not only to find and vet the best papers, but also to gather data about the publication process itself.

  https://www.kdnuggets.com/2016/02/iclr-deep-learning-scientific-publishing-experiment.html

• Learning from Hurricanes: Big Data Analytics, Risk, & Data Visualization

  This year, Florida has experienced its 10th consecutive year without a hurricane, which is longest period without a hurricane strike in modern times. Exploring this is worthy of some examination, as it offers us many lessons in Big Data Analytics, Risk, and Data Visualization.

  https://www.kdnuggets.com/2015/12/walker-hurricanes-big-data-analytics-risk-visualization.html

• Detecting In-App Purchase Fraud with Machine Learning

  Hacking applications allow users to make in-app purchases for free. With help from a few big games in the GROW data network we were able to build a model that classifies each purchase as real or fraud, with a very high level of accuracy.

  https://www.kdnuggets.com/2015/11/detecting-app-purchase-fraud-machine-learning.html

• What is the importance of Dark Data in Big Data world?

  Dark data is a subset of big data, but it constitutes the biggest portion of the total volume of big data collected by organizations in a year. We will discuss about what opportunities this holds for an organization.

  https://www.kdnuggets.com/2015/11/importance-dark-data-big-data-world.html

• 5 Warning Signs that Turn Off Data Science Hiring Managers

  Here are some warning signs that will prevent managers from hiring you for a Data Science position. If your resume has one or more of them, make an effort to remove the risk factors.

  https://www.kdnuggets.com/2015/11/warning-signs-data-science-hiring-managers.html

• We need a statistically rigorous and scientifically meaningful definition of replication

  Replication and confirmation are indispensable concepts that help define scientific facts. It seems that before continuing the debate over replication, we need a statistically meaningful definition of replication.

  https://www.kdnuggets.com/2015/10/statistically-rigorous-scientifically-meaningful-definition-replication.html

• Data Lake vs Data Warehouse: Key Differences

  We hear lot about the data lakes these days, and many are arguing that a data lake is same as a data warehouse. But in reality, they are both optimized for different purposes, and the goal is to use each one for what they were designed to do.

  https://www.kdnuggets.com/2015/09/data-lake-vs-data-warehouse-key-differences.html

• Paradoxes of Data Science

  There are many paradoxes, ironies and disconnects in today’s world of data science: pain points, things ignored, shoved under the rug, denied or paid lip.

  https://www.kdnuggets.com/2015/08/paradoxes-data-science.html

• Big Idea To Avoid Overfitting: Reusable Holdout to Preserve Validity in Adaptive Data Analysis

  Big Data makes it all too easy find spurious "patterns" in data. A new approach helps avoid overfitting by using 2 key ideas: validation should not reveal any information about the holdout data, and adding of a small amount of noise to any validation result.

  https://www.kdnuggets.com/2015/08/feldman-avoid-overfitting-holdout-adaptive-data-analysis.html

• New Standard Methodology for Analytical Models

  Traditional methods for the analytical modelling like CRISP-DM have several shortcomings. Here we describe these friction points in CRISP-DM and introduce a new approach of Standard Methodology for Analytics Models which overcomes them.

  https://www.kdnuggets.com/2015/08/new-standard-methodology-analytical-models.html

• Top 30 Social Network Analysis and Visualization Tools

  We review major tools and packages for Social Network Analysis and visualization, which have wide applications including biology, finance, sociology, network theory, and many other domains.

  https://www.kdnuggets.com/2015/06/top-30-social-network-analysis-visualization-tools.html

• R vs Python for Data Science: The Winner is …

  In the battle of "best" data science tools, python and R both have their pros and cons. Selecting one over the other will depend on the use-cases, the cost of learning, and other common tools required.

  https://www.kdnuggets.com/2015/05/r-vs-python-data-science.html

• R leads RapidMiner, Python catches up, Big Data tools grow, Spark ignites

  R is the most popular overall tool among data miners, although Python usage is growing faster. RapidMiner continues to be most popular suite for data mining/data science. Hadoop/Big Data tools usage grew to 29%, propelled by 3x growth in Spark. Other tools with strong growth include H2O (0xdata), Actian, MLlib, and Alteryx.

  https://www.kdnuggets.com/2015/05/poll-r-rapidminer-python-big-data-spark.html

• Top 10 Data Mining Algorithms, Explained

  Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.

  https://www.kdnuggets.com/2015/05/top-10-data-mining-algorithms-explained.html

• Machine Learning Wars: Amazon vs Google vs BigML vs PredicSis

  Comparing 4 Machine Learning APIs: Amazon Machine Learning, BigML, Google Prediction API and PredicSis on a real data from Kaggle, we find the most accurate, the fastest, the best tradeoff, and a surprise last place.

  https://www.kdnuggets.com/2015/05/machine-learning-wars-amazon-google-bigml-predicsis.html

• 7 common mistakes when doing Machine Learning

  In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly.

  https://www.kdnuggets.com/2015/03/machine-learning-data-science-common-mistakes.html

• 10 things statistics taught us about big data analysis

  There are 10 ideas in applied statistics are relevant for big data analysis, focusing on prediction accuracy, interactive analysis and more.

  https://www.kdnuggets.com/2015/02/10-things-statistics-big-data-analysis.html

• Deep Learning in a Nutshell – what it is, how it works, why care?

  Deep learning and neural networks are increasingly important concepts in computer science with great strides being made by large companies like Google and startups like DeepMind.

  https://www.kdnuggets.com/2015/01/deep-learning-explanation-what-how-why.html

• 11 Clever Methods of Overfitting and how to avoid them

  Overfitting is the bane of Data Science in the age of Big Data. John Langford reviews "clever" methods of overfitting, including traditional, parameter tweak, brittle measures, bad statistics, human-loop overfitting, and gives suggestions and directions for avoiding overfitting.

  https://www.kdnuggets.com/2015/01/clever-methods-overfitting-avoid.html

• CRISP-DM, still the top methodology for analytics, data mining, or data science projects

  CRISP-DM remains the most popular methodology for analytics, data mining, and data science projects, with 43% share in latest KDnuggets Poll, but a replacement for unmaintained CRISP-DM is long overdue.

  https://www.kdnuggets.com/2014/10/crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html

• The Impact Cycle – how to think of actionable insights

  The IMPACT Cycle provides a guiding framework for thinking about the steps for being effective analytical consultant, and can be a tool to help you drive effectiveness through your analytical teams.

  https://www.kdnuggets.com/2014/06/impact-cycle-actionable-insights.html

• Do you need a Masters Degree to become a Data Scientist?

  Leading analytics experts answer the question: "Do you need a Masters Degree to become a Data Scientist?" Read practical tips and interesting commentary.

  https://www.kdnuggets.com/2014/06/masters-degree-become-data-scientist.html

• Data Science Skills and Business Problems

  Discover what skills a data scientist benefits from learning and how the concept of a data scientist, and what businesses expect of them, has developed over time.

  https://www.kdnuggets.com/2014/06/data-science-skills-business-problems.html

• KDnuggets™ News 14:n06, Mar 19

  Features (11) | News (3) | Software (6) | Webcasts (3) | Courses (5) | Competitions (3) | Meetings (6) | Jobs (3) | Academic Read more »

  https://www.kdnuggets.com/2014/n06.html

• KDnuggets™ News 14:n05, Mar 5

  Features (10) | News (6) | Software (5) | Webcasts (1) | Courses (6) | Meetings (5) | Jobs (7) | Academic (1) | Publications Read more »

  https://www.kdnuggets.com/2014/n05.html

• Viewpoint: Why your company should NOT use “Big Data”

  Hardcore analytics (and Big Data) can add value, but only marginally and only for companies that have already mastered using the data they already have. The ‘obvious’ information from your own data can get you 90%+ of the total impact, so start there. The hard part is executing the basic insights across the organization.

  https://www.kdnuggets.com/2014/01/viewpoint-why-your-company-should-not-use-big-data.html

• 2013 Dec: Analytics, Big Data, Data Mining and Data Science News

  All (95) | News, Software (27) | Courses, Events (12) | Jobs | Academic | Publications (38) Unicorn Data Scientists vs Data Science Teams - Read more »

  https://www.kdnuggets.com/2013/12/index.html

• Statistics Software

  commercial | free Analyse-it!, accurate low-cost statistical software for Microsoft Excel. Appricon's Analysis Studio, a statistical analysis and modeling software with advanced logistic regression modeling, Read more »

  https://www.kdnuggets.com/software/statistics.html

• CRM (Customer Relationship Management)

  A B C D E F G H I J K L M N O PQ R S T U V W XYZ 11Ants Customer Read more »

  https://www.kdnuggets.com/solutions/crm.html

• 2013 Nov: Analytics, Big Data, Data Mining and Data Science Posts

  All (116) | News, Software (28) | Courses, Events (23) | Jobs | Academic | Publications (36) Yahoo SAMOA, Open Source Platform for Mining Big Read more »

  https://www.kdnuggets.com/2013/11/index.html

• KDnuggets™ News 13:n28, Nov 20

  Features (7) | Software (1) | Webcasts (1) | Jobs (6) | Academic (2) | Competitions (2) | Publications (5) | Tweets (3) | NewsBriefs Read more »

  https://www.kdnuggets.com/2013/n28.html

• KDnuggets™ News 13:n27, Nov 13

  Features (10) | Software (4) | Webcasts (3) | Courses, Events (5) | Meetings (3) | Jobs (7) | Academic (4) | Competitions (2) | Publications Read more »

  https://www.kdnuggets.com/2013/n27.html

• 2013 Nov Publications: Analytics, Big Data, Data Mining and Data Science

  All (116) | News, Software (28) | Courses, Events (23) | Jobs | Academic | Publications (36) LIONbook Chapter 15: Dimensionality reduction - Nov 28, Read more »

  https://www.kdnuggets.com/2013/11/publications.html

• KDnuggets™ News 13:n18, Jul 31

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• KDnuggets™ News 13:n16, Jul 3

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  https://www.kdnuggets.com/2013/n16.html

• KDnuggets™ News 13:n01, Jan 15

  Features (11) | Software (2) | Courses, Events (2) | Webcasts (3) | Jobs (11) | Competitions (3) | Publications (10) | NewsBriefs (4) | CFP Read more »

  https://www.kdnuggets.com/2013/n01.html