All (100) | Courses, Education (6) | Meetings (7) | News, Features (22) | Opinions, Interviews (32) | Software (9) | Tutorials, Overviews (20) | Webcasts & Webinars (4)
- Top KDnuggets tweets, Nov 23-29: The Entire #Python Language in a Single Image ; Great list of Data Science, Machine Learning, AI Resources - Nov 30, 2016.
The Entire #Python Language in a Single Image; Cartoon: Thanksgiving, #BigData, and Turkey #DataScience; 50% of Data Scientists have under 10 GB databases, not #BigData; Machine Learning Algorithms: A Concise Technical Overview
- Measuring Topic Interpretability with Crowdsourcing - Nov 30, 2016.
Topic modelling is an important statistical modelling technique to discover abstract topics in collection of documents. This article talks about a new measure for assessing the semantic properties of statistical topics and how to use it.
- The Data Science Delusion - Nov 30, 2016.
Gleanings from observed technical misunderstandings between business leaders and data scientists (and among data scientists themselves) so dramatic that one could start wondering whether there is something wrong with data science as it is being practiced.
- Top 10 Amazon Books in Artificial Intelligence & Machine Learning, 2016 Edition - Nov 30, 2016.
Given the ongoing explosion in interest for all things Data Science, Artificial Intelligence, Machine Learning, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the AI & Machine Learning category.
- Industries / Fields where you applied Analytics, Data Mining, Data Science in 2016? - Nov 29, 2016.
New KDnuggets Poll is asking: What are the Industries/Fields where you applied Analytics, Data Science, Data Mining in 2016? Please vote and we will publish the analysis and trends.
- Predictive Analytics.
Max Results. Min Time. - Nov 29, 2016.Successful analytics in the big data era does not start with data and software, but with immersive hands-on training and goal-driven strategy. Get this training with TMA courseware, which spans all skill levels and analytic team roles. Live Online in January or in Wash-DC in April.
- 10 Tips to Improve your Data Science Interview - Nov 29, 2016.
Interviewing is a skill. Here are 10 tips and resources to improve your Data Science interviews.
- Ethical Implications Of Industrialized Analytics - Nov 29, 2016.
Analytics & Big Data will be involved in every aspect of our lives and we should handle the ethical dilemmas wisely to let innovation contribute more to our lives.
- Machine Learning vs Statistics - Nov 29, 2016.
Machine learning is all about predictions, supervised learning, and unsupervised learning, while statistics is about sample, population, and hypotheses. But are they actually that different?
- Top Stories, Nov 21-27: Top 20 Python Machine Learning Open Source Projects; Continuous Improvement for IoT - Nov 28, 2016.
Top 20 Python Machine Learning Open Source Projects, updated; Continuous improvement for IoT through AI; Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning; Linear Regression, Least Squares & Matrix Multiplication: A Concise Technical Overview
- RCloud – DevOps for Data Science - Nov 28, 2016.
After almost two decades of software development, term – DevOps was coined and officially given importance to collaboration between development and deployment of software systems. In this early stage of Data Science field, use of standardized and empirical practises like DevOps will definitely speed up its evolution.
- Introduction to Machine Learning for Developers - Nov 28, 2016.
Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning.
- Webinar: Predictive Analytics: Failure to Launch [Dec 14] - Nov 28, 2016.
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 Dec 14.
- arXiv Paper Spotlight: Stealing Machine Learning Models via Prediction APIs - Nov 28, 2016.
Despite their confidentiality, machine learning models which have public-facing APIs are vulnerable to model extraction attacks, which attempt to "steal the ingredients" and duplicate functionality. The paper at hand investigates.
- Cyber Monday Sale: Save on TDWI Austin, Las Vegas, and more - Nov 28, 2016.
TDWI Conferences are world leading training events for analytics and Big Data, with industry experts sharing their knowledge and experiences in half/full-day sessions on skills you need today. Here are 2 ways to save this Cyber Monday.
- Why We Need Data Science - Nov 26, 2016.
A gentle reminder as to why we need Data Science, reasons for which even you may have been guilty of offending at some point. A basic topic, to be sure, making it all the more important.
- Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed - Nov 25, 2016.
Sebastian Raschka weighs in on how to battle stress as a beginner in the data science world. His insight is to-the-point, so reading it should be a stress-free endeavour.
- Continuous improvement for IoT through AI / Continuous learning - Nov 25, 2016.
In reality, especially for IoT, it is not like once an analytics model is built, it will give the results with same accuracy till the end of time. Data pattern changes over the time which makes it absolutely important to learn from new data and improve/recalibrate the models to get correct result. Below article explain this phenomenon of continuous improvement in analytics for IoT.
- Deep Learning Research Review: Reinforcement Learning - Nov 25, 2016.
This edition of Deep Learning Research Review explains recent research papers in Reinforcement Learning (RL). If you don't have the time to read the top papers yourself, or need an overview of RL in general, this post has you covered.
- Neighbors Know Best: (Re) Classifying an Underappreciated Beer - Nov 24, 2016.
A look at beer features to determine whether a specific brew might be better served (pun intended) by being classified under a different style. kNN analysis supported with in-post plots and linked iPython notebook.
- Linear Regression, Least Squares & Matrix Multiplication: A Concise Technical Overview - Nov 24, 2016.
Linear regression is a simple algebraic tool which attempts to find the “best” line fitting 2 or more attributes. Read here to discover the relationship between linear regression, the least squares method, and matrix multiplication.
- Top KDnuggets tweets, Nov 16-22: Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning - Nov 23, 2016.
Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning; What is the Difference Between #DeepLearning and Regular #MachineLearning?; Questions To Ask When Moving #MachineLearning From Practice to Production; How to Choose the Right #Database System
- Analytics help app developer conceive of new possibilities - Nov 23, 2016.
By now, we all have realised the power of IoT, Mobile Apps, Big Data and Analytics. Now it’s time to use this power in every possible way for complete well being of everyone in the world. Let’s read this interesting article on Women Health Care Mobile Apps and Data Analytics.
- Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning - Nov 23, 2016.
Social media now not only shares friendship connections or photos of “selfies” but also spreads from political media to science information. Social network members are tending to more eagerly learn about big data, data science and machine learning through groups. We review the ten largest Facebook groups in this area.
- Cartoon: Thanksgiving, Big Data, and Turkey Data Science. - Nov 23, 2016.
We revisit KDnuggets Thanksgiving cartoon, which examines the predicament of one group of fowl Data Scientists.
- Top Data Scientist Daniel Tunkelang on Data Recycling - Nov 22, 2016.
Respected Data Scientist Daniel Tunkelang shares some insight into data recycling, using data from other contexts to bootstrap your initial statistical models until you can collect live data.
- Predictive Science vs Data Science - Nov 22, 2016.
Is Predictive Science accurately represented by the term Data Science? As a matter of fact, are any of Data Science's constituent sciences well-represented by the umbrella term? This post discusses a few of these points at a high level.
- How to Make Your Database 200x Faster Without Having to Pay More - Nov 22, 2016.
Waiting long for a BI query to execute? I know it’s annoyingly frustrating… It’s a major bottle neck in day-to-day life of a Data Analyst or BI expert. Let’s learn some of the easy to use solutions and a very good explanation of why to use them, along with other advanced technological solutions.
- iSight Cloud – Lightning fast visualizations on large data sets - Nov 22, 2016.
SnappyData is launching a FREE cloud service called iSight-Cloud so anyone can try our engine and provide us some feedback. You can try our simple demos in a visual environment or even bring your own data sets to try.
- Beyond the Dashboard, MS in Applied Data Science - Nov 22, 2016.
Bay Path University’s MS in Applied Data Science will teach the fundamental principles, platforms, and tool-sets of the data science profession.
- The Experience of Being a High-Performing Data Scientist - Nov 21, 2016.
Now in open beta, IBM Data Science Experience (DSX) delivers Machine Learning, Collaboration, and Creative capabilities in an open and integrated environment for team data science, including many productivity features for next-generation data science,
- Top Stories, Nov 14-20: How Bayesian Inference Works; Data Science and Big Data, Explained; Advanced Time Series Prediction - Nov 21, 2016.
How Bayesian Inference Works; Data Science and Big Data, Explained; Trump, Failure of Prediction, and Lessons for Data Scientist; Combining Different Methods to Create Advanced Time Series Prediction; Questions To Ask When Moving Machine Learning From Practice to Production
- Top 20 Python Machine Learning Open Source Projects, updated - Nov 21, 2016.
Open Source is the heart of innovation and rapid evolution of technologies, these days. This article presents you Top 20 Python Machine Learning Open Source Projects of 2016 along with very interesting insights and trends found during the analysis.
- Implementing a CNN for Human Activity Recognition in Tensorflow - Nov 21, 2016.
In this post, we will see how to employ Convolutional Neural Network (CNN) for HAR, that will learn complex features automatically from the raw accelerometer signal to differentiate between different activities of daily life.
- Data Avengers… Assemble! - Nov 19, 2016.
The Avengers are perfectly capable of defending the Earth from our worst enemies. But are they up to the task of taking care of our data? Read this terribly punny "opinion" piece to find out.
- NEW: 6 Hot Career Prospects in Data Science Industry Today - Nov 18, 2016.
You read that Data Scientist is “The Sexiest Job of The 21st Century”, but there are other jobs profiles and opportunities in Data Science – read about these roles, responsibilities, skills, salary prospects and market demand (also pretty sexy!).
- Questions To Ask When Moving Machine Learning From Practice to Production - Nov 18, 2016.
An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.
- Process Mining: Where Data Science and Process Science Meet - Nov 17, 2016.
A data scientist without Process Mining training is ill-equipped to uncover the organization’s real processes, analyze compliance, diagnose bottlenecks and improve processes, so improve your skills with a new version of the free Coursera course "Process Mining: Data Science in Action" will start on November 28, 2016.
- KDD 2016: Watch Talks by Top Data Science Researchers - Nov 17, 2016.
Watch the innovative talks and researches from top researchers in Data Science, presented at KDD 2016, San Francisco conference.
- Deep Learning Reading Group: Skip-Thought Vectors - Nov 17, 2016.
Skip-thought vectors take inspiration from Word2Vec skip-gram and attempt to extend it to sentences, and are created using an encoder-decoder model. Read on for an overview of the paper.
- Why Fluid Intelligence Makes You a Better Data Scientist - Nov 17, 2016.
How do you harness the power of insight on a regular basis? Check out these tips for increasing your fluid intelligence to do so, courtesy of Saint Mary's College.
- Top KDnuggets tweets, Nov 9-15: #Trump, limits of #prediction; #TensorFlow French-to-English machine translation - Nov 16, 2016.
#Trump, limits of #prediction, and lessons for #DataScience of #polls; A #TensorFlow implementation of French-to-English machine translation using @DeepMindAI ByteNet; 18 top women in #DataScience to follow on Twitter; A complete daily plan for studying to become a #MachineLearning #Engineer
- The Foundations of Algorithmic Bias - Nov 16, 2016.
We might hope that algorithmic decision making would be free of biases. But increasingly, the public is starting to realize that machine learning systems can exhibit these same biases and more. In this post, we look at precisely how that happens.
- Combining Different Methods to Create Advanced Time Series Prediction - Nov 16, 2016.
The results from combining methods for time series prediction have been quite promising. However, the degree of error for long-term predictions is still quite high. Sounds like a challenge, so some new experiments are forthcoming!
- Data Exploration in Preparation for Modeling - Nov 16, 2016.
What you don't know can hurt you, especially in predictive modeling. Read great examples how exploring your data before creating models will help you spot problems before your build incorrect models.
- Data & Analytics Festival, Las Vegas – Catch The Early Bird - Nov 15, 2016.
2 great Las Vegas Summits: Big Data Innovation - Learn how to build scalable architecture for an effective data strategy; Business Analytics Innovation - learn how the most innovative companies communicate insight, and much more. Early Bird rates end Nov 25.
- Shortcomings of Deep Learning - Nov 15, 2016.
Current Deep Learning successes such as AlphaGo rely on massive amount of labeled data, which is easy to get in games, but often hard in other contexts. You can't play 20 questions with nature and win!
- 13 Forecasts on Artificial Intelligence - Nov 15, 2016.
Once upon a time, Artificial Intelligence (AI) was the future. But today, human wants to see even beyond this future. This article try to explain how everyone is thinking about the future of AI in next five years, based on today’s emerging trends and developments in IoT, robotics, nanotech and machine learning.
- Webinar, Nov 29: How Encore and Open English Gain Award Winning Marketing Insights with Speech Analytics - Nov 15, 2016.
Forward-thinking organizations are leveraging customer interaction analytics (a/k/a speech analytics) to gain a better understanding of the true “Voice of the Customer”. Join 2016 Speech Tech award winners to learn how they use analytics to gain actionable marketing insights that drive real revenue results.
- How Bayesian Inference Works - Nov 15, 2016.
Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Read an in-depth overview here.
- Data Science Hackathon, Metis / Invisible Institute, Nov 19, San Francisco - Nov 14, 2016.
Join hackathon to explore data science models and D3 visualizations to help the efforts of the Citizens Police Data Project.
- Ten Take-Aways from IBM World of Watson - Nov 14, 2016.
“Enterprise applications, Cloud, Cognitive computing and IBM Watson”, Yes, you guessed it right. This article talks about highlights of 2016 World of Watson conference organised at Las Vegas,NV.
- Data Science and Big Data, Explained - Nov 14, 2016.
This article is meant to give the non-data scientist a solid overview of the many concepts and terms behind data science and big data. While related terms will be mentioned at a very high level, the reader is encouraged to explore the references and other resources for additional detail.
- Top Stories, Nov 7-13: Trump, Failure of Prediction, and Lessons for Data Scientists; Top 10 Amazon Books in Data Mining - Nov 14, 2016.
Trump, Failure of Prediction, and Lessons for Data Scientists; Top 10 Amazon Books in Data Mining; Data Science Basics: An Introduction to Ensemble Learners; Parallelism in Machine Learning: GPUs, CUDA, and Practical Applications; 5 Free Machine Learning EBooks
- Discover the Future of Data at TDWI Austin- Plus a Special Gift! - Nov 14, 2016.
TDWI Austin takes place Dec 4-9. Register by November 18 and save $200. Use KDnuggets code KDFUN to get a $25 AMEX gift card to discover the weird and wonderful sights all around you in the capital of Texas!
- Trump, The Statistics of Polling, and Forecasting Home Prices - Nov 12, 2016.
Why polling has failed in US Presidential election? The home price index offers an apt comparison inasmuch as sample selection is problematic, equally snagging both election predictions and home price futures.
- An Intuitive Explanation of Convolutional Neural Networks - Nov 11, 2016.
This article provides a easy to understand introduction to what convolutional neural networks are and how they work.
- Data Scientists vs Data Analysts – Part 1 - Nov 11, 2016.
With employers trying to keep up with current data science trends, are data scientists just renamed data analysts? Part 1 of an investigation focuses on the top level numbers and pretty visualisations to highlight key differences.
- Join Us for the first AnacondaCON in February 2017 - Nov 11, 2016.
We're excited to announce that registration for AnacondaCON 2017, the first conference for Open Data Science leaders around the world, is now OPEN and limited to 500!
- Top 10 Amazon Books in Data Mining, 2016 Edition - Nov 11, 2016.
Given the ongoing explosion in interest for all things Data Mining, Data Science, Analytics, Big Data, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the Data Mining category.
- A Reference Architecture for Self-Service Analytics - Nov 10, 2016.
The keys to self-service analytics success are organizational. In addition to a governed self-service architecture, companies need to establish governance committees and gateways, create federated organizations with co-located BI developers, and provide continuous education, training, and support. Learn how to do this in this report.
- Data Is the Language of Modern Marketing, Toronto, Nov 28-29 - Nov 10, 2016.
This event will focus on the use of data and analytics for the customer and help marketers to master customer intelligence and the use of analytics. Data Marketing will offer the attendees Master Classes, Panel Discussions, and Keynotes led by 80+ leading experts.
- Reasons Why Data Projects Fail - Nov 10, 2016.
Many companies seem to go through a pattern of hiring a data science team only for the entire team to quit or be fired around 12 months later. Why is the failure rate so high?
- Transform the Future with Predictive Analytics - Nov 10, 2016.
Visit SAP resource center to learn how to accelerate decisions with automated predictive techniques and results, deploy and manage thousands of predictive data sets and test-drive a fully functional copy of SAP BusinessObjects Predictive Analytics software.
- Parallelism in Machine Learning: GPUs, CUDA, and Practical Applications - Nov 10, 2016.
The lack of parallel processing in machine learning tasks inhibits economy of performance, yet it may very well be worth the trouble. Read on for an introductory overview to GPU-based parallelism, the CUDA framework, and some thoughts on practical implementation.
- Top KDnuggets tweets, Nov 2-8: 35 #OpenSource tools for Internet of Things; An Introduction to Ensemble Learners - Nov 9, 2016.
21 Must-Know #DataScience Interview Questions with Answers; Big Data Science: Expectation vs. Reality; Big #DataScience: Expectation vs. Reality; The 10 Algorithms #MachineLearning Engineers Need to Know.
- A Quick Introduction to Neural Networks - Nov 9, 2016.
This article provides a beginner level introduction to multilayer perceptron and backpropagation.
- How to Rank 10% in Your First Kaggle Competition - Nov 9, 2016.
This post presents a pathway to achieving success in Kaggle competitions as a beginner. The path generalizes beyond competitions, however. Read on for insight into succeeding while approaching any data science project.
- Trump, Failure of Prediction, and Lessons for Data Scientists - Nov 9, 2016.
The shocking and unexpected win of Donald Trump of presidency of the United States has once again showed the limits of Data Science and prediction when dealing with human behavior.
- Easy Access to Full-Text Articles for Text Mining, Nov 15 Webinar - Nov 8, 2016.
Learn about the benefits of text mining full-text articles compared to abstracts,~ How to streamline the text mining process, how the new CCC and Linguamatics text mining solution works, and more.
- How Hadoop, Spark, and Data Science are evolving – Nov 10 Webinar - Nov 8, 2016.
Find out how Hadoop and Spark are evolving for Data Science in this Nov 10 webinar and live Q&A with guest speaker, Forrester VP and Principal Analyst Mike Gualtieri.
- Deep Learning cleans podcast episodes from ‘ahem’ sounds - Nov 8, 2016.
“3.5 mm audio jack… Ahem!!” where did you hear that? ;) Well, this post is not about Google Pixel vs iPhone 7, but how to remove ugly “Ahem” sound from a speech using deep convolutional neural network. I must say, very interesting read.
- Practical Data Science: Building Minimum Viable Models - Nov 8, 2016.
Data Science for startups based on data: Minimum Valuable Model, a new concept to avoid a full scale 95% accurate data science model. Want to know more about MVM? Have a look at this interesting article.
- Data Science Basics: An Introduction to Ensemble Learners - Nov 8, 2016.
New to classifiers and a bit uncertain of what ensemble learners are, or how different ones work? This post examines 3 of the most popular ensemble methods in an approach designed for newcomers.
- Largest Dataset Analyzed Poll shows surprising stability, more junior Data Scientists - Nov 8, 2016.
The majority (57%) of respondents only worked with Gigabyte range data. More junior Data Scientists enter the market, but Petabyte Big Data Scientists still stand apart.
- Agilience Top Artificial Intelligence, Machine Learning Authorities - Nov 7, 2016.
Agilience developed a new way to find authorities in social media across many fields of interest. In previous post we reviewed the top authorities in Data Mining and Data science; in this post we review top authorities in Artificial Intelligence and Machine Learning which includes Vineet Vashishta, Kirk D. Borne, KDnuggets, James Kobielus, Kaggle and more.
- For AI Engineers/Data Scientists: Implementing Enterprise AI course - Nov 7, 2016.
This unique course that is focussed on AI Engineering / AI for the Enterprise. Created in partnership with H2O.ai , the course uses Open Source technology to work with AI use cases. It is offered online and also in London and Berlin, starting January 2017.
- KDnuggets Top Blogs and Bloggers in October 2016 - Nov 7, 2016.
We recognize KDnuggets Bloggers who had the most popular blogs by views or shares in October 2016. They wrote about ebooks to read for Machine Learning, Data Science Venn Diagrams, 10 Data Science Videos on Youtube, and more.
- Top Stories, Oct 31-Nov 6: Machine Learning: A Complete and Detailed Overview; Learn Data Science for Excellence - Nov 7, 2016.
Machine Learning: A Complete and Detailed Overview; Learn Data Science for Excellence; 5 EBooks to Read Before Getting into A Machine Learning Career; Eight Things an R user Will Find Frustrating When Trying to Learn Python
- Top October Stories: 5 EBooks to Read Before Getting into A Machine Learning Career; Top 10 Data Science Videos on Youtube - Nov 6, 2016.
Also Battle of the Data Science Venn Diagrams; Deep Learning Key Terms, Explained.
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in October 2016 - Nov 5, 2016.
PhD/Postdoc at KU Leuven, Postdoc at Northeastern, Data Science Fellowship program at NYU, Asst. Prof. in ML at Cal State Long Beach, Data Science Faculty at UMBC, Faculty Business Analytics at USF, and more.
- Using Predictive Algorithms to Track Real Time Health Trends - Nov 4, 2016.
How to build a real-time health dashboard for tracking a person blood pressure readings, do time series analysis, and then graph the trends over time using predictive algorithms.
- Agilience Top Data Mining, Data Science Authorities - Nov 4, 2016.
Agilience developed a new way to find authorities in social media across many fields of interest. We review the top authorities in Data Mining and Data science, which include KDnuggets, Kirk. D. Borne, Kaggle, Vincent Granville, and more.
- Provalis Research QDA Miner 5 to Improve Advanced Qualitative Data Analysis of Unstructured Text - Nov 4, 2016.
QDA Miner 5 provides enhanced data portability, sharpened analysis of unstructured text and increased visualization capabilities.
- Chief Data Officer Toolkit: Leading the Digital Business Transformation – Part 2 - Nov 4, 2016.
Read the second and final part of this overview of the CDO Toolkit, which integrates the disciplines of economics and analytics to help the CDO to ascertain the economic value of the organization’s data and data sources.
- Top /r/MachineLearning Posts, October: NSFW Image Recognition, Differentiable Neural Computers, Hinton on Coursera - Nov 4, 2016.
NSFW Image Recognition, Differentiable Neural Computers, Hinton's Neural Networks for Machine Learning Coursera course; Introducing the AI Open Network; Making a Self-driving RC Car
- An NLP Approach to Analyzing Twitter, Trump, and Profanity - Nov 3, 2016.
Who swears more? Do Twitter users who mention Donald Trump swear more than those who mention Hillary Clinton? Let’s find out by taking a natural language processing approach (or, NLP for short) to analyzing tweets.
- How to Choose a Data Format - Nov 3, 2016.
In any data analytics project, after business understanding phase, data understanding and selection of right data format as well as ETL tools is very important task. In this article, a very useful and practical set of guidelines is explained covering data format selection and ETL phases of project lifecycle.
- Artificial Intelligence Classification Matrix - Nov 3, 2016.
There might be several different ways to think around machine intelligence startups; too narrow of a framework might be counterproductive given the flexibility of the sector and the facility of transitioning from one group to another. Check out this categorization matrix.
- Ready to take on 2017’s data mining challenges? - Nov 3, 2016.
With Stanford Graduate Certificates in Data Mining, learn about the applications of mining data within large sets of complex data and how to leverage them into tactical information for your company.
- Upcoming Meetings in Analytics, Big Data, Data Mining, Data Science: November and Beyond - Nov 2, 2016.
Coming soon: ODSC West Santa Clara, PAW Business Berlin, Apache Big Data Europe Seville, Chief Data Scientist Forum San Francisco, Big Data/Analytics Summit London, IEEE Big Data DC, and many more.
- Eight Things an R user Will Find Frustrating When Trying to Learn Python - Nov 2, 2016.
Are you an R user considering learning Python? Here's some insight into what you may be up against, and what, specifically, you may find frustrating. But don't worry, it's not all terrible.
- Evaluating HTAP Databases for Machine Learning Applications - Nov 2, 2016.
Businesses are producing a greater number of intelligent applications; which traditional databases are unable to support. A new class of databases, Hybrid Transactional and Analytical Processing (HTAP) databases, offers a variety of capabilities with specific strengths and weaknesses to consider. This article aims to give application developers and data scientists a better understanding of the HTAP database ecosystem so they can make the right choice for their intelligent application.
- Do You Suffer From Analytic Personality Disorder (APD)? - Nov 2, 2016.
Read this lighthearted take on Analytics Personality Disorder, a (nonexistent) syndrome for those obsessed with analytics.
- Top KDnuggets tweets, Oct 26-Nov 1: #BigData Science: Expectation vs. Reality; @AndrewNg #MachineLearning Course material - Nov 2, 2016.
#BigData Science: Expectation vs. Reality; Stanford CS 229: #MachineLearning Course material; Google - Decoding the micro-moments of #baseball via #BigData; Is your Code Good Enough to call Yourself a #DataScientist?
- Introduction to Trainspotting: Computer Vision, Caltrain, and Predictive Analytics - Nov 1, 2016.
We previously analyzed delays using Caltrain’s real-time API to improve arrival predictions, and we have modeled the sounds of passing trains to tell them apart. In this post we’ll start looking at the nuts and bolts of making our Caltrain work possible.
- Data Science 101: How to get good at R - Nov 1, 2016.
Everybody talks about R programming, how to learn, how to be good at it. But in this article, Ari Lamstein tells us his story about why and how he started with R along with how to publish, market and monetise R projects.
- How Can Lean Six Sigma Help Machine Learning? - Nov 1, 2016.
The data cleansing phase alone is not sufficient to ensure the accuracy of the machine learning, when noise / bias exists in input data. The lean six sigma variance reduction can improve the accuracy of machine learning results.
- Understanding the Chief Data Officer - Nov 1, 2016.
In this report you will find a concise look at how CDOs view their nascent role in high-profile organizations, focusing on guidelines and best practices for organizations looking to add their own CDO.
- Automated Machine Learning: An Interview with Randy Olson, TPOT Lead Developer - Oct 28, 2016.
Read an insightful interview with Randy Olson, Senior Data Scientist at University of Pennsylvania Institute for Biomedical Informatics, and lead developer of TPOT, an open source Python tool that intelligently automates the entire machine learning process.