Machine learning developers need to model a growing range of multi-partner scenarios where many learning agents and data sources interact under varying degrees of trustworthiness. This IBM site helps to take next step towards continuous intelligence.
In this post, the author implements a machine learning algorithm from scratch, without the use of a library such as scikit-learn, and instead writes all of the code in order to have a working binary classifier algorithm.
"Data scientist" continues to be recognized as a top career, but does this mean unending spoils for the data scientist? With large scale mass automation on the horizon for numerous professions, what can we do to safeguard our positions?
The Anatomy of Deep Learning Frameworks; Gartner 2017 Magic Quadrant for Data Science Platforms; 17 More Must-Know Data Science Interview Questions and Answers; The Gentlest Introduction to Tensorflow - Part 3; The Origins of Big Data
To leverage the potential of Big Data the manufacturing firms should intelligently integrate and connect their data sources on a unified platform and use machine learning to extract insights, analyze them, and derive results.
We compare Gartner 2017 Magic Quadrant for Data Science Platforms vs its 2016 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, MathWorks, Microsoft, and Quest.
Cyber Security is always a hot topic in IT industry and machine learning is making security systems more stronger. Here, a particular use case of machine learning in cyber security is explained in detail.
On March 9, Stanford’s Dr. Gregory Valiant discusses the difficulties of and solutions for making accurate inferences in this challenging regime, in which the empirical distribution of the available data is misleading.
Sir Austin Bradford Hill for the #DataScientist: An xkcd Story; Attacking #machinelearning with adversarial examples; Hans Rosling: An Appreciation - Great Data Scientist, Great Human #RIP; The Most Popular Language For #MachineLearning and #DataScience Is ...
Correlation is one of the most widely used (and widely misunderstood) statistical concepts. We provide the definitions and intuition behind several types of correlation and illustrate how to calculate correlation using the Python pandas library.
The second part of 17 new must-know Data Science Interview questions and answers covers overfitting, ensemble methods, feature selection, ground truth in unsupervised learning, the curse of dimensionality, and parallel algorithms.
This new two-day course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference, with emphasis on tools useful for tackling modern-day data analysis problems.
Latest poll of nearly 1000 analytics professionals and data scientists who read KDnuggets shows that 75% worldwide and 77% in the US oppose Trump Immigration Ban. The poll results reveal sharp polarization, with strong views prevailing on both sides.
Join your peers at Predictive Analytics World for Business and tap the potential of predictive analytics to optimize business. You will grasp it, own it, and put it to use by learning from the best of the best.
This is an attempt to explain Hill’s criteria using xkcd comics, both because it seemed fun, and also to motivate causal inference instructures to have some variety in which xkcd comic they include in lectures.
Deep Learning systems exhibit behavior that appears biological despite not being based on biological material. It so happens that humanity has luckily stumbled upon Artificial Intuition in the form of Deep Learning.
17 More Must-Know Data Science Interview Questions and Answers • Removing Outliers Using Standard Deviation in Python • Natural Language Processing Key Terms, Explained • KDnuggets Top Blogger: An Interview with Brandon Rohrer
With a new Snowflake data warehouse and Looker data platform on top, data analysts at athenahealth are delivering data to more people, and improving patient experience in the US healthcare system. Register and learn how.
Explore the new smart machines and self-controlled vehicles from the world's leading innovators across all industries at the Machine Intelligence and Autonomous Vehicles summits. Use code KDNUGGETS to save.
We are now at the right place and time for AI to be the set of technology advancements that can help us solve challenges where answers reside in data. While we have already seen a few AI bull and bear markets since the 50’s, this time it’s different. If I and others are right, the implications are immensely valuable for all.
5 Free Courses for Getting Started in #AI; Python #DataScience tutorial: Making #Python Speak #SQL with pandasql; Course materials: #DeepLearning for Natural Language Processing at Oxford; Resources for Learning AI, courtesy of McGill #AI Society.
17 new must-know Data Science Interview questions and answers include lessons from failure to predict 2016 US Presidential election and Super Bowl LI comeback, understanding bias and variance, why fewer predictors might be better, and how to make a model more robust to outliers.
This is an overview of a recent proposed method for solving the crowd wisdom problem: select the answer that is more popular than people predict. Research shows that this principle yields the best answer under reasonable assumptions about voter behavior.
This post attempts to provide some insights on the differences between IoT and the related technologies of M2M, CPS, and WoT, based on literature texts, but also the author's experience from projects and application deployments.
Be a member of an on-campus graduate class, watch lectures and complete assignments online, and digitally interact with your classmates. Stanford data mining courses: Flexibility. World-Class Teaching and Research. Stanford Credential.
We recognize our top blogs and bloggers in January, who wrote about Machine Learning, CyberSecurity, IoT, Pandas Cheat Sheet, Data Scientist - best job in America, Time Series, Deep Learning, and more.
In our experience working with many quantitative professionals over the years, the two main areas that contribute to long-term career growth are networking and continuous learning. Here is specific advice on how to do this and tips for Continuous Learning.
5 Career Paths in Big Data and Data Science, Explained • So What is Big Data? • Making Python Speak SQL with pandasql • 52 Useful Machine Learning & Prediction APIs, updated • Deep Learning Research Review: NLP
This post is the first in a 2 part series on scraping and cleaning data from the web using Python. This first part is concerned with the scraping aspect, while the second part while focus on the cleaning. A concrete example is presented.
In this post, I’ll look at the practical ingredients of managing agile data science. By using agile data science methods, we help data teams do fast and directed work, and manage the inherent uncertainty of data science and application development.
AI is a hard problem and will take much longer to solve in any scope. The sudden uptick in interest may revert back to normal, but the cycle of work will be longer, much more diverse, and interesting than Mobile/Cloud/SaaS.
A consumer’s complete digital footprint is a messy, fuzzy, dynamic picture. But data science is helping make digital identity as stable as physical identity – the last hurdle in the quest for the "holy grail" of marketing.
Companies that regularly exceed shareholder expectations have something in common: 88% of them use a fully functional platform to do data science work. Get the white paper from Forrester to learn more.
Apply to Springboard Data Science Career Track - the first online bootcamp to guarantee you a job in data science or your money back. Hundreds of graduates have mastered data science skills, and have been hired at top companies.
Manufacturing contributes to 16% of the global GDP and the Internet of Things (IoT ) is on track to connect >28 billion things. What happens when these massive forces collide? We review 5 game-changing technology catalysts.
TensorFlow 1.0.0-alpha; Unknown bot repeatedly beats top Go players online - so far it's undefeated; TensorKart: self-driving MarioKart with TensorFlow; GTA V integration into Universe is now open-source; Keras will be added to core TensorFlow at Google
Learn how to identify complex and dynamic patterns within your multilevel data and how to build multilevel linear models (MLM) and multilevel generalized linear models (MGLM). NYC in March, Online in May, SF in July.
Deep Learning Research Review: Natural Language Processing; Data Scientist – best job in America, again; 5 Free Courses for Getting Started in Artificial Intelligence; Top R Packages for Machine Learning
Despite the popularity of Regression, it is also misunderstood. Why? The answer might surprise you: There is no such thing as Regression. Rather, there are a large number of statistical methods that are called Regression, all of which are based on a shared statistical foundation.
Sexiest job... massive shortage... blah blah blah. Are you looking to get a real handle on the career paths available in "Data Science" and "Big Data?" Read this article for insight on where to look to sharpen the required entry-level skills.
The letter, signed by many leading computer experts, calls for 5 ethical principles when using data: Do no harm, help create peaceful coexistence, help vulnerable people, preserve and improve natural environment, and help create a fair world without discrimination.
Why does Deep Learning perform better than other machine learning methods? We offer 3 reasons: integration of integration of feature extraction within the training process, collection of very large data sets, and technology development.
TDWI will present a 3-day Accelerate Conference on April 3–5, 2017 in Boston, with sessions on core data science skills including R, Python, and Spark. KDnuggets members save 20% through Feb 10, 2017 with priority code KD20.
What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!
Analytics is not one time job. It needs to be automated, deployed and improved for future business analytics requirements. Here an IBM expert discusses about development & deployment of analytics assets and capabilities of it.
Designed to be at the intersection of marketing, data science, and analytics, this summit will discuss common challenges and pain points, discover new cutting-edge technology tools and solutions, and to connect and network. Use discount code KDN15 to save.
#Python implementations of Andrew Ng #MachineLearning MOOC exercises; This repository contains the entire #Python #DataScience Handbook; What are the best #visualizations of #MachineLearning algorithms? Learn #TensorFlow and #DeepLearning, without a PhD.
Many analytic models are not deployed effectively into production while others are not maintained or updated. Applying decision modeling and decision management technology within CRISP-DM addresses this.
With nearly every every smart young computer scientist planning to work on deep learning, are there really still artificial intelligence researchers working on other techniques? Is deep learning the AI silver bullet?
A carefully-curated list of 5 free collections of university course material to help you better understand the various aspects of what artificial intelligence and skills necessary for moving forward in the field.
The Insurance Nexus USA Summit (March 14-15, Chicago) is the world’s only venue helping insurers to build a resilient inner core. Check out the attendee list, agenda topics and speakers and get special KDnuggets discount.