Search results for

Found 6292 documents, 6021 searched:

• 5 More Machine Learning Projects You Can No Longer Overlook

  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.

  https://www.kdnuggets.com/2016/06/five-more-machine-learning-projects-cant-overlook.html

• Mining Twitter Data with Python Part 4: Rugby and Term Co-occurrences

  Part 4 of this series employs some of the lessons learned thus far to analyze tweets related to rugby matches and term co-occurrences.

  https://www.kdnuggets.com/2016/06/mining-twitter-data-python-part-4.html

• Subscribe to KDnuggets News

  Get the FREE ebook 'The Great Big Natural Language Processing Primer' and 'The Complete Collection of Data Science Cheat Sheets' along with the leading newsletter Read more »

  https://www.kdnuggets.com/news/subscribe.html

• KDnuggets News Archive

  Past KDnuggets News issues for 2021 (48 issues), 2020 (48 issues), 2019 (49 issues), 2018 (48 issues), 2017 (48 issues), 2016 (46 issues), 2015 (42 Read more »

  https://www.kdnuggets.com/news/archive.html

• KDnuggets Privacy Policy

  We respect your privacy. We do not sell your information to advertisers. We only send our subscribers emails relevant to AI, Analytics, Big Data, Data Read more »

  https://www.kdnuggets.com/news/privacy-policy.html

• Submissions Guidelines

    Thinking about submitting to KDnuggets? We would love to have a look! Please take a minute to familiarize yourself with the guidelines below prior Read more »

  https://www.kdnuggets.com/news/submissions.html

• Gregory Piatetsky-Shapiro

  Gregory Piatetsky-Shapiro, Ph.D. is the Founder of KDnuggets, a leading site for Analytics, Big Data, Data Science, Data Mining, and Machine Learning. Gregory was a Read more »

  https://www.kdnuggets.com/gps.html

• Improving Nudity Detection and NSFW Image Recognition

  This post discussed improvements made in a tricky machine learning classification problem: nude and/or NSFW, or not?

  https://www.kdnuggets.com/2016/06/algorithmia-improving-nudity-detection-nsfw-image-recognition.html

• Regularization in Logistic Regression: Better Fit and Better Generalization?

  A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.

  https://www.kdnuggets.com/2016/06/regularization-logistic-regression.html

• Top Machine Learning Libraries for Javascript

  Javascript may not be the conventional choice for machine learning, but there is no reason it cannot be used for such tasks. Here are the top libraries to facilitate machine learning in Javascript.

  https://www.kdnuggets.com/2016/06/top-machine-learning-libraries-javascript.html

• Ten Simple Rules for Effective Statistical Practice: An Overview

  An overview of 10 simple rules to follow to ensure proper effective statistical data analysis.

  https://www.kdnuggets.com/2016/06/ten-simple-rules-effective-statistical-practice-overview.html

• 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

• History of Data Mining

  Data mining is a subfield of computer science which blends many techniques from statistics, data science, database theory and machine learning. Here are the major milestones and “firsts” in the history of data mining plus how it’s evolved and blended with data science and big data.

  https://www.kdnuggets.com/2016/06/rayli-history-data-mining.html

• New Andrew Ng Machine Learning Book Under Construction, Free Draft Chapters

  Check out the details on Andrew Ng's new book on building machine learning systems, and find out how to get your free copy of draft chapters as they are written.

  https://www.kdnuggets.com/2016/06/free-machine-learning-book-draft-chapters.html

• What is Your Data Worth? On LinkedIn, Microsoft, and the Value of User Data

  The recent announcement of Microsoft’s acquisition of LinkedIn has raised many questions about how Microsoft will monetize this data. We examine LinkedIn value per user and compare to Google, Facebook, Yahoo, and Twitter.

  https://www.kdnuggets.com/2016/06/walker-linkedin-microsoft-value-user-data.html

• Political Data Science: Analyzing Trump, Clinton, and Sanders Tweets and Sentiment

  This post shares some results of political text analytics performed on Twitter data. How negative are the US Presidential candidate tweets? How does the media mention the candidates in tweets? Read on to find out!

  https://www.kdnuggets.com/2016/06/politics-analytics-trump-clinton-sanders-twitter-sentiment.html

• A Visual Explanation of the Back Propagation Algorithm for Neural Networks

  A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.

  https://www.kdnuggets.com/2016/06/visual-explanation-backpropagation-algorithm-neural-networks.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

• Thinking About Analytics Readiness

  This article touches upon an important but under-discussed topic of analytics readiness, including whether and when organizations should engage in analytics.

  https://www.kdnuggets.com/2016/06/thinking-domain-readiness.html

• Nutrition & Principal Component Analysis: A Tutorial

  A great overview of Principal Component Analysis (PCA), with an example application in the field of nutrition.

  https://www.kdnuggets.com/2016/06/nutrition-principal-component-analysis-tutorial.html

• 7 Steps to Mastering SQL for Data Science

  Follow these 7 steps to go from SQL data science newbie to seasoned practitioner quickly. No nonsense, just the necessities.

  https://www.kdnuggets.com/2016/06/seven-steps-mastering-sql-data-science.html

• Bootcamps in Analytics, Big Data, Data Science, Machine Learning

  BaseCamp, an innovative data science bootcamp from Knoyd. The first cohort will start in Vienna, Austria in January 2017. Data Science Dojo, an in-person or Read more »

  https://www.kdnuggets.com/education/bootcamps.html

• Mining Twitter Data with Python Part 1: Collecting Data

  Part 1 of a 7 part series focusing on mining Twitter data for a variety of use cases. This first post lays the groundwork, and focuses on data collection.

  https://www.kdnuggets.com/2016/06/mining-twitter-data-python-part-1.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

• Machine Learning Classic: Parsimonious Binary Classification Trees

  Get your hands on a classic technical report outlining a three-step method of construction binary decision trees for multiple classification problems.

  https://www.kdnuggets.com/2016/06/breiman-stone-parsimonious-binary-classification-trees.html

• How to Select Support Vector Machine Kernels

  Support Vector Machine kernel selection can be tricky, and is dataset dependent. Here is some advice on how to proceed in the kernel selection process.

  https://www.kdnuggets.com/2016/06/select-support-vector-machine-kernels.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

• AIG & Zurich on Machine Learning in Insurance

  Where and how can machine learning be practically applied by insurers? And is it worth it? Read the white paper from insurance experts at AIG and Zurich.

  https://www.kdnuggets.com/2016/06/fcbi-aig-zurich-machine-learning-insurance.html

• Top NoSQL Database Engines

  An overview of the top 5 NoSQL database engines in use today, including examples of key-value, column-oriented, graph, and document paradigms.

  https://www.kdnuggets.com/2016/06/top-nosql-database-engines.html

• Cloud Computing Key Terms, Explained

  A concise overview of 20 core cloud computing ecosystem concepts. The focus here is on the terminology, not The Big Picture.

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

• 5 Best Practices for Big Data Security

  Lack of data security can not only result in financial losses, but may also damage the reputation of organizations. Take a look at some of the most important data security best practices that can reduce the risks associated with analyzing a massive amount of data.

  https://www.kdnuggets.com/2016/06/5-best-practices-big-data-security.html

• Where are the Opportunities for Machine Learning Startups?

  Machine learning has permeated data-driven businesses, which means almost all businesses. Here are a few areas where it’s possible that big corporations haven’t already eaten everybody’s lunch.

  https://www.kdnuggets.com/2016/06/opportunites-machine-learning-startups.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

• Big Data Business Model Maturity Index and the Internet of Things (IoT)

  This post explores how organizations could use the Big Data Business Model Maturity Index (BDBMMI) to exploit the Internet of Things (IoT).

  https://www.kdnuggets.com/2016/06/big-data-business-model-maturity-index-iot.html

• R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results

  R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science platform. Big Data tools used by almost 40%, and Deep Learning usage doubles.

  https://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.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

• Udacity Nanodegree Programs: Machine Learning, Data Analyst, and more

  Develop new skills. Be in demand. Accelerate your career with the credential that fast-tracks you to career success.

  https://www.kdnuggets.com/2016/06/udacity-nanodegree-programs-machine-learning-data-analyst.html

• Top 10 Open Dataset Resources on Github

  The top open dataset repositories on Github include a variety of data, freely available for use by researchers, practitioners, and students alike.

  https://www.kdnuggets.com/2016/05/top-10-datasets-github.html

• Predicting Popularity of Online Content

  A look at predicting what makes online content popular, with a particular focus on images, especially selfies.

  https://www.kdnuggets.com/2016/05/predicting-popularity-online-content.html

• Free eBook: Healthcare Social Media Analytics and Marketing

  Get your free copy of a new ebook outlining social media marketing and analytics strategies (including code) for healthcare professionals.

  https://www.kdnuggets.com/2016/05/healthcare-social-media-analytics-marketing-ebook.html

• A Concise Overview of Standard Model-fitting Methods

  A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.

  https://www.kdnuggets.com/2016/05/concise-overview-model-fitting-methods.html

• 5 Ways in Which Big Data Can Help Leverage Customer Data

  Every business enterprise realizes the importance of big data but rarely puts the customer data that they possess to good use. Here are few ways enterprises can leverage customer data.

  https://www.kdnuggets.com/2016/05/5-ways-big-data-leverage-customer-data.html

• Let Me Hear Your Voice and I’ll Tell You How You Feel

  This post provides an overview of a voice tone analyzer implemented as part of a cohesive emotion detection system, directly from the researcher and architect.

  https://www.kdnuggets.com/2016/05/voice-tone-analysis-emotion-detection.html

• 10 Must Have Data Science Skills, Updated

  An updated look at the state of the data science landscape, and the skills - both technical and non-technical - that are absolutely required to make it as a data scientist.

  https://www.kdnuggets.com/2016/05/10-must-have-skills-data-scientist.html

• How to Explain Machine Learning to a Software Engineer

  How do you explain what machine learning is to the uninitiated software engineer? Read on for one perspective on doing so.

  https://www.kdnuggets.com/2016/05/explain-machine-learning-software-engineer.html

• 5 Machine Learning Projects You Can No Longer Overlook

  We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.

  https://www.kdnuggets.com/2016/05/five-machine-learning-projects-cant-overlook.html

• Top Posts

    Current Top Posts   Top Posts By Year Top Posts of 2023 5 Free Books to Master Data Science • 5 Free Courses to Read more »

  https://www.kdnuggets.com/news/top-stories.html

• Tips for Data Scientists: Think Like a Business Executive

  Thinking like a Data Scientist is important; it puts businesses and business leaders in an analytical frame of mind. But it is also important for Data Scientists to be able to think like business executives. Read on to find out why.

  https://www.kdnuggets.com/2016/05/tips-data-scientist-think-like-executive.html

• The Amazing Power of Word Vectors

  A fantastic overview of several now-classic papers on word2vec, the work of Mikolov et al. at Google on efficient vector representations of words, and what you can do with them.

  https://www.kdnuggets.com/2016/05/amazing-power-word-vectors.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

• Practical skills that practical data scientists need

  The long story short, data scientist needs to be capable of solving business analytics problems. Learn more about the skill-set you need to master to achieve so.

  https://www.kdnuggets.com/2016/05/practical-skills-practical-data-scientists-need.html

• Troubleshooting Neural Networks: What is Wrong When My Error Increases?

  An overview of some of the things that could lead to an increased error rate in neural network implementations.

  https://www.kdnuggets.com/2016/05/troubleshooting-neural-network-error-increase.html

• Are Deep Neural Networks Creative?

  Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?

  https://www.kdnuggets.com/2016/05/deep-neural-networks-creative-deep-learning-art.html

• Deep Learning and Neuromorphic Chips

  The 3 main ingredients to creating artificial intelligence are hardware, software, and data, and while we have focused historically on improving software and data, what if, instead, the hardware was drastically changed?

  https://www.kdnuggets.com/2016/05/deep-learning-neuromorphic-chips.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

• Meet the 11 Big Data & Data Science Leaders on LinkedIn

  In this post, we present a list of popular data science leaders on LinkedIn. Follow these leaders who will keep you in touch with the latest Data Science happenings!

  https://www.kdnuggets.com/2016/05/10-big-data-data-science-leaders-linkedin.html

• Why Implement Machine Learning Algorithms From Scratch?

  Even with machine learning libraries covering almost any algorithm implementation you could imagine, there are often still good reasons to write your own. Read on to find out what these reasons are.

  https://www.kdnuggets.com/2016/05/implement-machine-learning-algorithms-scratch.html

• How Much do Analytics Salaries Increase when Changing Jobs?

  A data-informed analysis of analytics career salaries and their increase when changing jobs.

  https://www.kdnuggets.com/2016/05/burtchworks-analytics-salaries-increase-changing-jobs.html

• A Data Science Approach to Writing a Good GitHub README

  Readme is the first file every user will look for, whenever they are checking out the code repository. Learn, what you should write inside your readme files and analyze your existing files effectiveness.

  https://www.kdnuggets.com/2016/05/algorithmia-data-science-approach-good-github-readme.html

• Datasets Over Algorithms

  The average elapsed time between key algorithm proposals and corresponding advances is about 18 years; the average elapsed time between key dataset availabilities and corresponding advances is less than 3 years, 6 times faster.

  https://www.kdnuggets.com/2016/05/datasets-over-algorithms.html

• How to Network and Build a Personal Brand in Data Science

  SpringBoard shares some ideas on how to network and build a data career, as taken from a new guide they have put together on the topic.

  https://www.kdnuggets.com/2016/05/how-network-build-personal-brand-data-science.html

• How to Use Cohort Analysis to Improve Customer Retention

  Cohort analysis is a subset of behavioral analytics that takes the user data and breaks them into related groups for analysis. Let’s understand using cohort analysis with an example of daily cohort of app users.

  https://www.kdnuggets.com/2016/05/clevertap-use-cohort-analysis-improve-customer-retention.html

• Cartoon: When Automation Goes Too Far

  KDnuggets Cartoon looks into the future of Automated Data Science and Marketing - when will automation go too far?

  https://www.kdnuggets.com/2016/04/cartoon-when-automation-goes-too-far.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

• Data Scientist Survey: What Is An Interesting Result?

  A survey requesting feedback from data scientists on their opinion of what an interesting result is. The survey is anonymous, has only a single mandatory question, and takes only 5 minutes.

  https://www.kdnuggets.com/2016/04/irisa-data-scientist-survey-interesting-result.html

• Machine Learning for Artists – Video lectures and notes

  Art has always been deep for those who appreciate it... but now, more than ever, deep learning is making a real impact on the art world. Check out this graduate course, and its freely-available resources, focusing on this very topic.

  https://www.kdnuggets.com/2016/04/machine-learning-artists-video-lectures-notes.html

• Eugenics – journey to the dark side at the dawn of statistics

  Today is the 80th anniversary of the death of Karl Pearson, one of the founding father of statistics (correlation coefficient, principal components, the p-value, and much more). He was also deeply involved with eugenics, a jarring reminder that truth often comes bundled with a measure of darkness.

  https://www.kdnuggets.com/2016/04/eugenics-journey-dark-side-statistics.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

• How to Remove Duplicates in Large Datasets

  Dealing with huge datasets can be tricky, especially the data cleaning process. One of such processing is de-duplication, find out how you can solve this using the statistical techniques.

  https://www.kdnuggets.com/2016/04/clevertap-remove-duplicates-large-datasets.html

• Microsoft is Becoming M(ai)crosoft

  This post is an overview and discussion of Microsoft's increasing investment in, and approach to, artificial intelligence, and how their philosophy differs from their competitors.

  https://www.kdnuggets.com/2016/04/microsoft-becoming-m-ai-crosoft.html

• Advantages of a Career in Data Science

  As the rampant growth of data science continues across industries, the opportunities are plenty for both aspiring and expert data scientists. Here is an overview of data science industries, opportunities and work locations.

  https://www.kdnuggets.com/2016/04/advantages-career-data-science.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

• Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

  A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.

  https://www.kdnuggets.com/2016/04/top-10-ipython-nb-tutorials.html

• Comprehensive Guide to Learning Python for Data Analysis and Data Science

  Want to make a career change to Data Science using python? Well learning anything on your own can be a challenge & a little guidance could be a great help, that is exactly what this article will provide you with.

  https://www.kdnuggets.com/2016/04/datacamp-learning-python-data-analysis-data-science.html

• KDnuggets News Future Schedule

  KDnuggets News blog is published every weekday. KDnuggets email digest is emailed on Wednesdays, except during holiday periods. KDnuggets welcomes guest blogs. Submissions should be Read more »

  https://www.kdnuggets.com/news/schedule.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

• Top 15 Frameworks for Machine Learning Experts

  Either you are a researcher, start-up or big organization who wants to use machine learning, you will need the right tools to make it happen. Here is a list of the most popular frameworks for machine learning.

  https://www.kdnuggets.com/2016/04/top-15-frameworks-machine-learning-experts.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

• 12 Inspiring Women In Data Science, Big Data

  It’s been well documented that women don’t come close to parity in STEM fields with their counterparts. Could the rise of big data and data science offer women a clearer path to success in technology? Here’s a list of 12 inspiring women who work in big data and data

  https://www.kdnuggets.com/2016/04/12-inspiring-women-in-data-science-big-data.html

• Recommender Systems: New Comprehensive Textbook by Charu Aggarwal

  Covers recommender systems comprehensively, both fundamentals and advanced topics, organized into: Algorithms and evaluation, recommendations in specific domains and contexts, and advanced topics and applications.

  https://www.kdnuggets.com/2016/04/recommender-systems-textbook.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

• Association Rules and the Apriori Algorithm: A Tutorial

  A great and clearly-presented tutorial on the concepts of association rules and the Apriori algorithm, and their roles in market basket analysis.

  https://www.kdnuggets.com/2016/04/association-rules-apriori-algorithm-tutorial.html

• Regression & Correlation for Military Promotion: A Tutorial

  A clear and well-written tutorial covering the concepts of regression and correlation, focusing on military commander promotion as a use case.

  https://www.kdnuggets.com/2016/04/regression-correlation-military-tutorial.html

• Advantages and Risks of Self-Service Analytics

  Self-service analytics is likely to spread in all the business layers, and with proper care to avoid certain risks, the culture of self-service analytics will help all organizations.

  https://www.kdnuggets.com/2016/04/advantages-risks-self-service-analytics.html

• New Deep Learning Book Finished, Finalized Online Version Available

  What will likely become known as the seminal book on deep learning is finally finished, with the online version finalized and freely-accessible to all those interested in mastering deep neural networks.

  https://www.kdnuggets.com/2016/04/deep-learning-book-finished.html

• CrowdFlower 2016 Data Science Report

  A new data science report with survey results related to the success and challenges of data scientists, and where data science is going as a discipline.

  https://www.kdnuggets.com/2016/04/crowdflower-2016-data-science-repost.html

• JSU Computational and Data-Enabled Science and Engineering Program

  JSU is among the first minority serving institutions to create a Big Data focused doctoral and graduate program for MS and PhD in Computational and Data-Enabled Science and Engineering - apply now.

  https://www.kdnuggets.com/2016/04/jsu-computational-data-enabled-science-engineering.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

• Deep Learning for Internet of Things Using H2O

  H2O is feature-rich open source machine learning platform known for its R and Spark integration and it’s ease of use. This is an overview of using H2O deep learning for data science with the Internet of Things.

  https://www.kdnuggets.com/2016/04/deep-learning-iot-h2o.html

• 10 Signs Of A Bad Data Scientist

  With the number of people claiming to be a data scientist growing, the “true” data scientists are becoming hard to find. Here your guide identify the clues to catch a bad data scientists.

  https://www.kdnuggets.com/2016/04/10-signs-bad-data-scientist.html

• Salford Predictive Modeler 8: Faster. More Machine Learning. Better results

  Take a giant step forward with SPM 8: Download and try it for yourself just released version 8 and get better results.

  https://www.kdnuggets.com/2016/04/salford-predictive-modeler-8-software.html

• The Secret to a Perfect Data Science Interview

  How to interview a Data Scientist, in 5 steps. The secret to answering every question perfectly :).

  https://www.kdnuggets.com/2016/04/cartoon-interview-data-scientist.html

• How to Compute the Statistical Significance of Two Classifiers Performance Difference

  To determine whether a result is statistically significant, a researcher would have to calculate a p-value, which is the probability of observing an effect given that the null hypothesis is true. Here we are demonstrating how you can compute difference between two models using it.

  https://www.kdnuggets.com/2016/03/statistical-significance-two-classifiers-performance-difference.html

• 100 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning

  Stay on top of your data science skills game! Here’s a list of about 100 most active and interesting blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  https://www.kdnuggets.com/2016/03/100-active-blogs-analytics-big-data-science-machine-learning.html

• Don’t Buy Machine Learning

  In many projects, the amount of effort spent on R&D on Machine Learning is usually a small fraction of the total effort, or it’s not even there because we plan it for a future phase after building the application first.

  https://www.kdnuggets.com/2016/03/dont-buy-machine-learning.html

• Cartoon: Citizen Data Scientist At Work

  KDnuggets Cartoon examines Citizen Data Scientist at work and his previous career as a citizen dentist and a citizen pilot.

  https://www.kdnuggets.com/2016/03/cartoon-citizen-data-scientist.html

• How to combat financial fraud by using big data?

  Financial fraud methods are becoming more sophisticated and the techniques to combat such attacks also need to evolve. Big data has brought with it novel fraud detection and prevention techniques such as behavioral analysis and real-time detection to give fraud fighting techniques a new perspective.

  https://www.kdnuggets.com/2016/03/combat-financial-fraud-using-big-data.html

• XGBoost: Implementing the Winningest Kaggle Algorithm in Spark and Flink

  An overview of XGBoost4J, a JVM-based implementation of XGBoost, one of the most successful recent machine learning algorithms in Kaggle competitions, with distributed support for Spark and Flink.

  https://www.kdnuggets.com/2016/03/xgboost-implementing-winningest-kaggle-algorithm-spark-flink.html

• Top 10 Data Science Resources on Github

  The top 10 data science projects on Github are chiefly composed of a number of tutorials and educational resources for learning and doing data science. Have a look at the resources others are using and learning from.

  https://www.kdnuggets.com/2016/03/top-10-data-science-github.html

• Doing Data Science: A Kaggle Walkthrough – Cleaning Data

  Gain insight into the process of cleaning data for a specific Kaggle competition, including a step by step overview.

  https://www.kdnuggets.com/2016/03/doing-data-science-kaggle-walkthrough-cleaning-data.html

• R Learning Path: From beginner to expert in R in 7 steps

  This learning path is mainly for novice R users that are just getting started but it will also cover some of the latest changes in the language that might appeal to more advanced R users.

  https://www.kdnuggets.com/2016/03/datacamp-r-learning-path-7-steps.html

• Lift Analysis – A Data Scientist’s Secret Weapon

  Gain insight into using lift analysis as a metric for doing data science. Understand how to use it for evaluating the performance and quality of a machine learning model.

  https://www.kdnuggets.com/2016/03/lift-analysis-data-scientist-secret-weapon.html

• Must Know Tips for Deep Learning Neural Networks

  Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.

  https://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-1.html

• Netflix Prize Analyzed: Movie Ratings and Recommender Systems

  A 195-page monograph by a top-1% Netflix Prize contestant. Learn about the famous machine learning competition. Improve your machine learning skills. Learn how to build recommender systems.

  https://www.kdnuggets.com/2016/03/netflix-prize-analyzed-movie-ratings-recommender-systems.html

• The Data Science Game – Student Competition

  The Data Science Game returns this year, with university students competing for dominance. Details for this iteration and further information is provided here.

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

• New KDnuggets Tutorials Page: Learn R, Python, Data Visualization, Data Science, and more

  Introducing new KDnuggets Tutorials page with useful resources for learning about Business Analytics, Big Data, Data Science, Data Mining, R, Python, Data Visualization, Spark, Deep Learning and more.

  https://www.kdnuggets.com/2016/03/new-tutorials-section-r-python-data-visualization-data-science.html

• The Evolution of the Data Scientist

  We trace the evolution of Data Science from ancient mathematics to statistics and early neural networks, to present successes like AlphaGo and self-driving car, and look into the future.

  https://www.kdnuggets.com/2016/03/evolution-data-scientist.html

• How to tell a great analyst from a good analyst

  Good analyst help businesses to stay in the competition, but great analyst sets the business apart from its competition. Learn more about how to be a great analyst by walking that extra mile.

  https://www.kdnuggets.com/2016/03/quandl-great-analyst-vs-good-analyst.html

• What Should Data Scientists Know About Psychology?

  Due to training in the scientific method, data management, statistics/data analysis, subject matter expertise, and communicating results into substantive knowledge psychology researchers must have a solid understanding of data science and vice-versa.

  https://www.kdnuggets.com/2016/03/data-scientists-know-about-psychology.html

• Jobs Posting Information

  To add a free short entry on KDnuggets Jobs page for an industry or academic job related to AI, Big Data, Data Science, or Machine Read more »

  https://www.kdnuggets.com/jobs/details.html

• KDnuggets Jobs Testimonials

  For rates, and to post your job, email to Accenture, Amazon, Apple, Bank of America, Boeing, Citibank, Deloitte & Touche, eBay, Fair Isaac, GE, Google, Read more »

  https://www.kdnuggets.com/jobs/testimonials.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

• Tutorials, Overviews

  https://www.kdnuggets.com/tutorials/index.html

• 3 Viable Ways to Extract Data from the Open Web

  We look at 3 main ways to handle data extraction from the open web, along with some tips on when each one makes the most sense as a solution.

  https://www.kdnuggets.com/2016/03/webhose-3-ways-extract-data-open-web.html

• The Data Science Puzzle, Explained

  The puzzle of data science is examined through the relationship between several key concepts in the data science realm. As we will see, far from being concrete concepts etched in stone, divergent opinions are inevitable; this is but another opinion to consider.

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

• The Data Science Process, Rediscovered

  The Data Science Process is a relatively new framework for doing data science. It is compared to previous similar frameworks, and a discussion on process innovation versus repetition is then undertaken.

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

• Deriving Better Insights from Time Series Data with Cycle Plots

  Visualization plays key role in analysis of time series data, to understand underlying trends. Here we are demonstrating the cycle plot which shows both the cycle or trend and the day-of-the-week or the month-of-the-year effect.

  https://www.kdnuggets.com/2016/03/better-insights-time-series-cycle-plots.html

• Top February stories: 21 Must-Know Data Science Interview Q&A; Gartner 2016 MQ for Advanced Analytics: gainers and losers

  21 Must-Know Data Science Interview Questions and Answers; Top 10 TED Talks for the Data Scientists; Gartner 2016 Magic Quadrant for Advanced Analytics Platforms: gainers and losers.

  https://www.kdnuggets.com/2016/03/top-news-2016-feb.html

• AI and Machine Learning: Top Influencers and Brands

  Onalytica gives us a new list of the top 100 Artifical Intelligence and Machine Learning influencers and brands, and provides some insight into the relationships between them.

  https://www.kdnuggets.com/2016/03/onalytica-ai-machine-learning-top-influencers-brands.html

• Watch the Geek Rap Video – Predictive Analytics Song

  “PREDICT THIS!” is the first pop song to present analytics content with Gangnam Style humor, and media-blending 80’s throwback visuals. The rapper, formerly known as Dr. Eric Siegel (co-founder of Predictive Analytics World) said, “I only answer to ‘Dr. Data’ now.”

  https://www.kdnuggets.com/2016/03/eric-siegel-rap-video-predictive-analytics-song.html

• Self-Paced E-Learning course: Credit Risk Modeling

  The course covers basic and advanced modeling, including stress testing Probability of Default (PD), Loss Given Default (LGD ) and Exposure At Default (EAD) models.

  https://www.kdnuggets.com/2016/03/baesens-elearning-course-credit-risk-modeling.html

• Introducing GraphFrames, a Graph Processing Library for Apache Spark

  An overview of Spark's new GraphFrames, a graph processing library based on DataFrames, built in a collaboration between Databricks, UC Berkeley's AMPLab, and MIT.

  https://www.kdnuggets.com/2016/03/introducing-graphframes-apache-spark.html

• Fastest Growing Programming Languages and Computing Frameworks

  A new model for ranking programming languages and predicting the growth of user adoption. Includes current language rankings and predictions.

  https://www.kdnuggets.com/2016/03/ranking-growth-programming-languages.html

• The Data Science Process

  What does a day in the data science life look like? Here is a very helpful framework that is both a way to understand what data scientists do, and a cheat sheet to break down any data science problem.

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

• AutoML: Automated Data Science and Machine Learning

  For recent posts and more recent lists of AutoML and Automated Data Science, see Tag: AutoML. ABM: Automatic Business Modeler, automatically builds accurate and interpretable Read more »

  https://www.kdnuggets.com/software/automated-data-science.html

• scikit-feature: Open-Source Feature Selection Repository in Python

  scikit-feature is an open-source feature selection repository in python, with around 40 popular algorithms in feature selection research. It is developed by Data Mining and Machine Learning Lab at Arizona State University.

  https://www.kdnuggets.com/2016/03/scikit-feature-open-source-feature-selection-python.html

• Top Big Data Processing Frameworks

  A discussion of 5 Big Data processing frameworks: Hadoop, Spark, Flink, Storm, and Samza. An overview of each is given and comparative insights are provided, along with links to external resources on particular related topics.

  https://www.kdnuggets.com/2016/03/top-big-data-processing-frameworks.html