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
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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 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 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
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.
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.
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.
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.
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.
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,
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
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.
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.
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!).
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.
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.
#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
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.
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!
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.
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!
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.
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.
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.
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.
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
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!
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.
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.
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.
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.
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.
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.
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.
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.
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.
“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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
#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?
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.
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.
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.
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.