AI is the future of business and AI driven chatbots are the first step in gaining a competitive edge for your company in that future. Attend BUSINESS OF BOTS in San Francisco, Oct 9-11, and save 15% with code KD15.
In this upcoming webinar, review the practical first steps an organization can take towards becoming an AI-driven enterprise, and learn how you cut through the hype of AI, apply it organization-wide, and quickly realize ROI.
Talks, tutorials and playlists – you could not get a more gentle introduction to Machine Learning (ML) in Finance. Got a quick 4 minutes or ready to study for hours on end? These videos cover all skill levels and time constraints!
Here is how we got one of the best results in a Kaggle challenge remarkable for a number of interesting findings and controversies among the participants.
Highlights and key takeaways from day 1 of AI Conference San Francisco 2017, including current state review, future trends, and top recommendations for AI initiatives.
Highlights from recent AI Conference include the inevitable merger of IQ and EQ in computing, Deep learning to fight cancer, AI as the new electricity and advice from Andrew Ng, Deep reinforcement learning advances and frontiers, and Tim O’Reilly analysis of concerns that AI is the single biggest threat to the survival of humanity.
To fully use machine learning, we first need to understand both the potential benefits and the techniques to create data-driven models. In this webinar series, we will show you how to easily and automatically apply complex algorithms to data in real world applications.
This tutorial will lay a solid foundation to your understanding of Tensorflow, the leading Deep Learning platform. The second part shows how to get started, install, and build a small test case.
This article walks you through a step by step process and comes with starter code for building your own chatbot. In the end we also provide some pointers for folks looking to take this proof of concept to production stage.
Only three weeks until the IAPA National Conference "Advancing Analytics", October 18, Melbourne - don't miss this one-day to get up-to-date, meet with peers and hear from global leaders.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape, targeted towards people who want to understand the emerging world of Big Data. Use code KDNUGGETS to save.
Perhaps most significant development in IT over the past few years, blockchain has the potential to change the way that the world approaches big data, with enhanced security and data quality.
Keras is a Python deep learning library for Theano and TensorFlow. The package is easy to use and powerful, as it provides users with a high-level neural networks API to develop and evaluate deep learning models.
Vast amounts of data are already overwhelming existing BI tools and analytics processes. To address these challenges, BI and analytics professionals are adopting user-friendly, automated machine learning solutions.
Looking for advice? Guidance? Stories? We’ve put a list of the top ten LinkedIn influencers of the last three months, follow them and stay up-to-date with the latest news in Big Data, Data Science, Analytics, Machine Learning and AI.
The author often finds himself explaining machine learning to non-experts. These are 10 things that he believes everyone should know, which he offers as a public service announcement.
When Data Scientists first get a data set, they oftne use a matrix of 2D scatter plots to quickly see the contents and relationships between pairs of attributes. But for data with lots of attributes, such analysis does not scale.
Get an exclusive preview of "Spark: The Definitive Guide" from Databricks! Learn how Spark runs on a cluster, see examples in SQL, Python and Scala, Learn about Structured Streaming and Machine Learning and more.
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets; How To Become a 10x Data Scientist; 5 Machine Learning Projects You Can No Longer Overlook – Episode VI; Ensemble Learning to Improve Machine Learning Results; Putting Machine Learning in Production
The second part in this series addresses group-based imputation for dealing with missing data values. Check out why finding group means can be a more formidable action than overall means, and see how to accomplish it in Python.
In machine learning, going from research to production environment requires a well designed architecture. This blog shows how to transfer a trained model to a prediction server.
Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).
This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.
Our comprehensive agenda covers the most important topics and success factors for high-impact data insights, with expert instructors whose only goal is to get you to the next level. Big savings when you register by Oct 13 with priority code KD20.
Everyone is talking about Tensorflow these days. In this multipart series, we explain Tensorflow in detail, including it’s architecture and industry applications.
Handling missing values is one of the worst nightmares a data analyst dreams of. In situations, a wise analyst ‘imputes’ the missing values instead of dropping them from the data.
It takes less effort to lie without numbers, but there are now more numbers and more ways to lie with them than ever before. Poor Reverend Bayes, who understood the true meaning of "evidence".
AI and machine learning have hit the mainstream, spanning consumer devices to enterprise initiatives. Experts say change is coming fast - if you don't have a plan, are you behind the curve?
Deep learning, data preparation, data visualization, oh my! Check out the latest installation of '5 Machine Learning Projects You Can No Longer Overlook' for insight on... well, what machine learning projects you can no longer overlook.
Schedule a time to see a demo of the CrowdFlower platform and see how we empower data scientists to train, test, and tune machine learning for a human world. We’ll hook you up with some of San Francisco's best ice cream!
Subscribe to The Predictive Analytics Times, the premier resource that delivers timely, and find a PAW event that will fit your calendar and specific needs. Sign up with code PATIMES17 for 15% off two day and combo passes.
A 10x developer is someone who is 10 times more productive than average. We adapt tips and tricks from the developer community to help you become a more proficient data scientist loved by team members, including code design and selecting right tools for the job.
Data scientists may not be as educated or experienced in computer science, programming concepts, devops, site reliability engineering, non-functional requirements, software solution infrastructure, or general software architecture as compared to well-trained or experienced software architects and engineers.
It’s not necessary to understand the inner workings of a machine learning project, but you should understand whether the right things have been measured and whether the results are suited to the business problem. You need to know whether to believe what data scientists are telling you.
Learn how to add Apache SystemML to an existing Hortonworks Data Platform (HDP) 2.6.1 cluster for Apache Spark. Users interested in Python, Scala, Spark, or Zeppelin can run Apache SystemML as described here.
See Dr. John Elder, CEO & Founder, Elder Research, Inc. at Predictive Analytics World for Financial Services – October in New York – at his special featured session: How to Tell if Your Market Timing System Will Work: A New Measure of Model Quality.
A 10x developer is someone who is 10 times more productive than average. We adapt tips and tricks from the developer community to help you become a more proficient data scientist loved by team members and stakeholders.
Also: New-Age Machine Learning Algorithms in Retail Lending; Top 10 Machine Learning Use Cases: Part 2; Python vs R for Artificial Intelligence, Machine Learning, and Data Science; Python overtakes R, becomes the leader in Data Science, Machine Learning platforms
In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Introductory neural network concerns are covered.
TEXATA is the annual Big Data Analytics World Championships - business case-study competition with two online qualification rounds (4 hours each) and Top 12 Live World Finals held in Austin. Use TEXATA37856C for free entry for KDnuggets readers.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape, targeted towards people who want to understand the emerging world of Big Data. Use code KDNUGGETS to save.
Want to see the new Anaconda Enterprise 5 features in action? Register now for our new webinar, Unveiling Anaconda Enterprise 5—The Enterprise-Ready Data Science Platform.
We are now in the middle of an AI hype wave which will decline. This is why I think that AI will take 100 or more years to become sentient, only after completely different AI systems will be created.
You are not the only one who wonders how much longer they can get away with pretending to be a data scientist. You are not the only one who has nightmares about being laughed out of your next interview.
Back-to-school sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $12 until Sep 20, 2017.
This program will ground you in both statistical concepts and practical application and help you develop the skills needed for a rewarding career in Analytics and Data Science.
In response to the violence in Charlottesville, the Data Science Institute at the U. of Virginia is undertaking a unique project to help understand the ways people use social media to physically, and politically engage in the world around them.
There are many projects using computer vision systems, machine learning and large data sets to hopefully make a difference to our oceans and gain the knowledge to have a real impact on future sustainability.
This is the first of 3 posts to cover imputing missing values in Python using Pandas. The slowest-moving of the series (out of necessity), this first installment lays out the task and data at the risk of boring you. The next 2 posts cover group- and regression-based imputation.
IAPA National Conference in Melbourne on 18 October will be a fantastic day with another five speakers just announced. Early bird rates have been extended to Sep 20 or become IAPA member and save even more.
Also: WTF #Python - A collection of interesting and tricky Python examples; Thoughts after taking @AndrewYNg #Deeplearning #ai course; Another #Keras Tutorial For #NeuralNetwork Beginners.
On Sep 27, watch 24 free, LIVE data science talks from industry-leading speakers who will collectively demystify the training, tools, and career paths associated with the exciting field of data science.
For the next 48 hours only, get a Safari membership for only $199 for one year. Use code SAVEPNR for full, unlimited access to everything the Safari learning platform offers, including live training, books, videos, and more!
This is a very basic overview of activation functions in neural networks, intended to provide a very high level overview which can be read in a couple of minutes. This won't make you an expert, but it will give you a starting point toward actual understanding.
Like many other computer vision problems, there still isn’t an obvious or even “best” way to approach the problem of object recognition, meaning there’s still much room for improvement.
Bridging the gap between data science and strategy, the NYU Stern MS in Business Analytics program provides experienced professionals with a unique data-driven business perspective. Next application deadline is Oct 15.
K-Nearest Neighbors (K-NN) is one of the simplest machine learning algorithms. When a new situation occurs, it scans through all past experiences and looks up the k closest experiences. Those experiences (or: data points) are what we call the k nearest neighbors.
The first ICDIS conference will be held on April 8-10, 2018, in South Padre Island, Texas. Come experience this unique forum where data management, data intelligence, and data security are all involved.
We examine Google Trends, job trends, and more and note that while Python has only a small advantage among current Data Science and Machine Learning related jobs, this advantage is likely to increase in the future.
Such relational intelligence separates artificial intelligence systems with human cognition. DeepMind, the creators of AlphaGo, quietly published two groundbreaking research papers into this area, demonstrating a way to train relational reasoning using deep neural networks.
This post is the second in a series whose aim is to shake up our intuitions about what machine learning is making possible in specific sectors — to look beyond the set of use cases that always come to mind.
Public data has tremendous potential and different people can use it to solve variety of problems. Enigma relaunches Enigma Public — the platform connecting people to data.
This is a summary (with links) of a three-part article series that's intended to be an in-depth overview of the considerations, tradeoffs, and recommendations associated with selecting between Python and R for programmatic data science tasks.
Also: Python overtakes R, becomes the leader in Data Science, Machine Learning platforms; I built a chatbot in 2 hours and this is what I learned; Closing the Insights-to-Action Gap; Are Data Lakes Fake News?; Visualizing Cross-validation Code
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape, targeted towards people who want to understand the emerging world of Big Data. Use code KDNUGGETS to save.
We will provide tips for data scientists to speed up Python algorithms, including a discussion on algorithm choice, and how effective package tool can make large differences in performance.
It seems Isaac Asimov didn’t envision needing a law to govern robots in these sorts of life-and-death situations where it isn’t the life of the robot versus the life of a human in debate, but it’s a choice between the lives of multiple humans!
Andrew Ng announces new Deep Learning specialization on Coursera; DeepMind and Blizzard open StarCraft II as an AI research environment; OpenAI bot beat best Dota 2 players in 1v1 at The International 2017; My Neural Network isn't working! What should I do?; Deep Learning Neural Networks Play Path of Exile
Data is driving business transformation. Come to Strata Data Conference and learn how to turn algorithms into business advantage, build modern data strategies, and spend quality time with experts. Use code KDNU to save.
Discover how to use a platform to organize unstructured data to see the linkages between word usage and document of origin, see the themes in a word cloud, and use topic extraction and document clustering.
The question is no longer ‘can we get machines to do this or that’ (the answer is yes for most things you can think of), question now is ‘where all do we want to do it?’
Data and analysis of data have, in some form, been used to aid decision making since ancient times. So why, after all these centuries are data and analytics not more embedded in corporate decision making?
Data Science Camp is SF Bay ACM annual event combining sessions, keynote, and optional tutorial - an excellent opportunity to learn and connect with others, at very low cost.
Lukas Vermeer will speak at the upcoming Predictive Analytics World for Business, 11-12 October in London. His Keynote will focus on new perspectives on the Data Science challenges we face today.
Also 42 Steps to Mastering Data Science; Deep Learning is not the AI future; Data Science Primer; The Rise of GPU Databases; What AI Can and cannot do.
The quick answer is yes, and the biggest problem is that the term “Data Lakes” has been overloaded by vendors and analysts with different meanings, resulting in an ill-defined and blurry concept.
The scientific method to approach a problem, in my point of view, is the best way to tackle a problem and offer the best solution. If you start your data analysis by simply stating hypotheses and applying Machine Learning algorithms, this is the wrong way.
New books on Data Science and Analytics with Python, Large-Scale Machine Learning in the Earth Sciences, and Social Networks with Rich Edge Semantics - save 20% with code JWR38.
January 25 & 26 in San Francisco will see the sixteenth global Deep Learning Summit and the fifth global AI Assistant Summit joined by the first ever Deep Learning for Enterprise Summit. Use code KDNUGGETS to save 20% on Early Bird passes!
There are many types of analytics for getting insight out of data, but the bigger and more difficult challenge is turning that insight into action. What should we do differently based on your findings?
Cross-validation helps to improve your prediction using the K-Fold strategy. What is K-Fold you asked? Check out this post for a visualized explanation.
This post introduces Deep Learning Pipelines from Databricks, a new open-source library aimed at enabling everyone to easily integrate scalable deep learning into their workflows, from machine learning practitioners to business analysts.
Budapest is calling Data Scientists and Data engineers to CRUNCH Conference, Oct 18-20. CRUNCH will feature talks from Google, Airbnb, Tesla, LinkedIn, Netflix, Uber, and more. Use code KDnuggetsAtCrunch to save.
The term Data Science should describe the “Science OF Data”, while doing Science WITH Data could be called “Data-Driven Science”. Whatever your preferred term, reinforcing the distinction will help establish the Science OF Data and doing Science WITH Data as bona-fide disciplines.
A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now “magically” solved by deep learning and any talented teenager can do it in a few hours.
Also: How to Become a Data Scientist: The Definitive Guide; Top 10 Machine Learning Use Cases; Vital Statistics You Never Learned… Because They’re Never Taught; Deep Learning is not the AI future; 42 Steps to Mastering Data Science
50+ leading experts in Artificial Intelligence area will present on state-of-the-art topics including Cognitive Computing, Chatbots, Machine Learning, Deep Learning, and IoT and cover key industry verticals. Use code KDNUGGETS to save.
Wharton Customer Analytics Initiative is looking to provide implementable solutions to companies most pressing marketing and analytics problems. Apply by Sep 25 to get a team of top Penn students work on your problem.
This is a collection of 277 data science key terms, explained with a no-nonsense, concise approach. Read on to find terminology related to Big Data, machine learning, natural language processing, descriptive statistics, and much more.
Top 10 Videos on Machine Learning in Finance
Introduction to Blockchains & What It Means to Big Data
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
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