Real-time face detection and emotion/gender classification; Top 20 #Python #MachineLearning Open Source Projects, updated; Stanford CS231n lecture slides: #DeepLearning software; #DataScience platforms are on the rise
This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.
For those analytics professionals who want to prove their worth to the business, an IAPA-Certified via Credential not only recognises your skills but also helps you be the outlier.
Customer Analytics from Wharton Executive Education gives you a deeper, actionable understanding of your data by delving into specific collection methodologies and patterns for predictive behavior, empowering you to make impactful decisions that drive success throughout your company.
Nuts-ml is a new data pre-processing library in Python for GPU-based deep learning in vision. It provides common pre-processing functions as independent, reusable units. These so called ‘nuts’ can be freely arranged to build data flows that are efficient, easy to read and modify.
The influence of a Twitter user goes beyond the simple number of followers. We also want to examine how effective are tweets - how likely they are to be retweeted, favorited, or the links inside clicked upon. What exactly is an influential user depends on the definition.
Attend the leading event for predictive analytics case studies, expertise, and resources. With conferences strategically scheduled around the globe, you are sure to 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.
With an explosive growth in the number of transactions, detecting fraud cannot be done manually and Machine Learning-based methods are required. We examine what are the main challenges for using Machine Learning for Trust.
New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll; Machine Learning Crash Course: Part 1; Text Mining 101: Mining Information From A Resume; Data science platforms are on the rise and IBM is leading the way; An Introduction to the MXNet Python API
This post is the first in a series of tutorials for implementing machine learning workflows in Python from scratch, covering the coding of algorithms and related tools from the ground up. The end result will be a handcrafted ML toolkit. This post starts things off with data preparation.
News, sentiment, and emotion drive markets - consumer markets and financial markets, making text and sentiment analysis essential tools for research and insights. Using code KDNUGGETS to save - early reg by May 31.
This post takes the concept of an ontology and presents it in a clear and simple manner, devoid of the complexities that often surround such explanations.
This post outlines an entire 6-part tutorial series on the MXNet deep learning library and its Python API. In-depth and descriptive, this is a great guide for anyone looking to start leveraging this powerful neural network library.
Download the 2017 Gartner Magic Quadrant for Data Science Platforms today to learn why IBM is named a leader in data science and to find out why data science, analytics, and machine learning are the engines of the future.
Data Science projects involve iterative processes and may need changes in data at every iteration. But Data versioning, data pipelines and data workflows make Data Scientist’s life easy, let’s see how.
A meticulously compiled list as extensive as possible of every accelerator, incubator or program the author has read or bumped into over the past months. It looks like there are at least 29 of them. An interesting read for a wide variety of potentially interested parties - far beyond only the investor.
There are elements of what we do which are AI complete. Eventually, Artificial General Intelligence will eliminate the data scientist, but it’s not around the corner.
#BigData 2017: Top Influencers and Brands; #ICYMI 10 Free Must-Read Books for #MachineLearning and #DataScience; Good Test for #DeepLearning #ImageRecognition? #Chihuahua or #Muffin
Successful analytics at the organizational-level starts with immersive, interactive training and goal-driven strategy. TMA’s live online and classroom training spans all skill levels and analytic team roles to build analytic leaders. Live Online in June, Seattle in July and Wash-DC in October.
DataScience.com new Python library, Skater, uses a combination of model interpretation algorithms to identify how models leverage data to make predictions.
This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.
The Saint Mary's College Master of Science in Data Science program will prepare you to enter into the data analysis process at any stage, from the initial formulation of the question, to visualizing data, to interpreting the results and drawing conclusions.
NLG tools automate the analysis and enhance traditional BI platforms by explaining in plain English the significance of visualizations and findings – here is an overview of the market.
Check the new “AI, Analytics and GDPR Survey 2017”, where Insurance Nexus quizzed 250 of the brightest minds in insurance, and learn the latest trends in analytics, AI and GDPR to help you adjust your strategy.
Python caught up with R and (barely) overtook it; Deep Learning usage surges to 32%; RapidMiner remains top general Data Science platform; Five languages of Data Science.
In this live webinar, on May 24th at 11AM Central, learn how Anaconda empowers data scientists to encapsulate and deploy their data science projects as live applications with a single click.
Getting Into Data Science: What You Need to Know; The Best Python Packages for Data Science; HDFS vs. HBase : All you need to know; What are common data quality issues for Big Data and how to handle them?; Teaching the Data Science Process
Learn about Big Data technologies and trends, Democratizing Big Data analytics, Big Data and the Cloud, and more in this webcast with top experts Dean Abbott and Mamdouh Refaat.
This report is the second in a series analyzing data science related topics. This time around, specifically, we rank 15 top Python data science packages, hopefully with results of use to the data science community.
This upcoming 45-minute webinar explores efficient methods to explore and organize complex data, how to marry multiple datasets for feature engineering, and optimal target selection and how to address information leakage.
Ready to embark on an exciting and in-demand career? Here’s what you need to know about what a data scientist does—and how you can become competitive in this in-demand field.
Moving to Hadoop is not without its challenges—there are so many options, from tools to approaches, that can have a significant impact on the future success of a business’ strategy. Data management and data pipelining can be particularly difficult.
Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until May 27, 2017.
Understanding the process requires not only wide technical background in machine learning but also basic notions of businesses administration; here I will share my experience on teaching the data science process.
AI is the hottest technology now. You can win KDnuggets free pass to the new AI conference in NYC in from the organizers of Strata + Hadoop World Conferences.
More than 400 of the sharpest minds in the industry will meet at Postgres Vision June 26-28 in Boston. The goal is to envision the future for enterprises striving to harvest greater strategic value and actionable insight from their data.
DataCamp is celebrating 1 millions learners on its platform and is offering 50% off for unlimited access until May 23. Learn R and Python for data science interactively at your own pace.
Data science projects may often fail due to a lack of clear definition of the business goal and not because data scientists technical abilities. We examine the connection between data science and research design to help address this issue.
Propensity scores are used in quasi-experimental and non-experimental research when the researcher must make causal inferences, for example, that exposure to a chemical increases the risk of cancer.
Let's have a look at common quality issues facing Big Data in terms of the key characteristics of Big Data – Volume, Velocity, Variety, Veracity, and Value.
It's an easy-to-read, practical primer for C-level to mid-level executives about how to harness the power of analytics to increase organizational effectiveness.
TDWI Anaheim SoCal conference at Disneyland combines business with pleasure and will be the one work event your family begs you to book. Register by June 2 and save up to 30% with code KD30.
Hadoop Distributed File System (HDFS), and Hbase (Hadoop database) are key components of Big Data ecosystem. This blog explains the difference between HDFS and HBase with real-life use cases where they are best fit.
New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?; Using Deep Learning To Extract Knowledge From Job Descriptions; Deep Learning Past, Present, and Future; The Guerrilla Guide to Machine Learning with R
Onalytica's Big Data Influencer report for 2017 is here. Check out the names and brands that have made the list this year, and get up to speed on the latest happenings in Big Data.
The Data Science Career Track is the first online bootcamp to guarantee you a data science job or your money back. The application process is selective - start it know.
The courses cover topics such as Neural Networks and Deep Learning, Bayesian Networks, Big Data with Apache Spark, Bayesian Inference, Text Mining and Time Series, and each has theoretical as well as practical classes, done with R or Python. Early bird till June 5.
In short, you reach different resting placing with different SGD algorithms. That is, different SGDs just give you differing convergence rates due to different strategies, but we do expect that they all end up at the same results!
We introduce a new library for doing distributed hyperparameter optimization with Scikit-Learn estimators. We compare it to the existing Scikit-Learn implementations, and discuss when it may be useful compared to other approaches.
Stanford Data Mining Courses and Certificates are designed to give you the skills you need to gather and analyze massive amounts of information, and to translate that information into actionable business strategies. Enroll until June 18.
Explore the latest advancements in deep learning and their applications in industry at the Deep Learning in Finance Summit and Deep Learning in Retail Summit in London, 1-2 June. Use discount code KDNUGGETS to save 20% off all tickets.
ML modeling is an iterative process and it is extremely important to keep track of all the steps and dependencies between code and data. New open-source tool helps you do that.
Cloud computing is the next evolutionary step in Internet-based computing, which provides the means for delivering ICT resources as a service. Internet-of-Things can benefit from the scalability, performance and pay-as-you-go nature of cloud computing infrastructures.
This post is a lean look at learning machine learning with R. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.
Also Is #MachineLearning overtaking #BigData? What Do Frameworks Offer Data Scientists that #Programming Languages Lack?; Seeing Theory - A Brown University visual intro to probability and stats.
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 webinars are May 16 and June 14.
This report, created by analyzing millions of job postings using advanced technology, divides Data Science and Analytics roles into 6 broad categories, and answers many questions, including cities, industries, job roles with most growth.
In this month's installment of Machine Learning Projects You Can No Longer Overlook, we find some data preparation and exploration tools, a (the?) reinforcement learning "framework," a new automated machine learning library, and yet another distributed deep learning library.
Learn how to master Machine Learning by understanding the theory behind. MLTrain also teaches the concepts and helpful tricks of key systems like TensorFlow and how to code machine learning algorithms using it.
A/B testing is key to improving results in any marketing campaign. We examine the issues involved in its 3 main components: message variants, user group selection, and choosing the winning version.
We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.
Without knowing the ground truth of a dataset, then, how do we know what the optimal number of data clusters are? We will have a look at 2 particular popular methods for attempting to answer this question: the elbow method and the silhouette method.
Predictive Analytics World for Business focuses on concrete examples of deployed predictive analytics. Join us May 14-18 in San Francisco to learn how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling and machine learning, and the kind of business results they achieve.
SpringML inviting business and sales leaders to its Man vs Machine Forecasting Duel - give them a day with your data and they will provide an algorithm based, unbiased forecast.
Data Insight Leaders Summit is 2 value packed days with the most senior speaker faculty of strictly Head’s of Data Science, Advanced Analytics and Business Intelligence, 18-19 October 2017 in Barcelona.
Datakind, in collaboration with Microsoft, completed significant data-driven projects to improve traffic safety and help save lives in New York City, Seattle, and New Orleans.
How to Learn Machine Learning in 10 Days; Deep Learning – Past, Present, and Future; Keep it simple! How to understand Gradient Descent algorithm; New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?
A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.
Vote in KDnuggets 18th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will clean, analyze, visualize, and publish the results.
Check out this Python deep learning virtual machine image, built on top of Ubuntu, which includes a number of machine learning tools and libraries, along with several projects to get up and running with right away.
Learn how to implement AI in real-world projects today and explore what the future holds for intelligence engineering at O'Reilly's AI Conference, NYC, June 26-29. Save extra 20% with code PCKDNG.
Taking place June 20-21 at the Metro Toronto Convention Centre, Big Data Toronto 2017 will host three co-located conferences that are free to attend for data and AI professionals.
Why Momentum Really Works; O'Reilly's Hands-On Machine Learning with Scikit-Learn and TensorFlow; Implemented BEGAN and saw a cute face at iteration 168k; Self-driving car course; Exploring the mysteries of Go; DeepMind Solves AGI
DataScience.com’s enterprise data science platform can now be deployed on-premises or in the cloud. New features include scalable infrastructure, intuitive project organization, and task automation.
RE•WORK would like to update KDnuggets readers on their upcoming European events, as discounted tickets end next week, and share their on-demand content and expert interviews! For 20% off pass prices for all RE•WORK events, use discount code KDNUGGETS.
Is Machine Learning is overtaking Big Data?! We also examine trends for several more related and popular buzzwords, and see how BD, ML. Artificial Intelligence, Data Science, and Deep Learning rank.
42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.
Resampling is a solution which is very popular in dealing with class imbalance. Our research on churn prediction shows that balanced sampling is unnecessary.
This post summarizes nine creative ways to condemn almost any AI startup to bankruptcy. I focus on data science and machine learning startups, but the lessons on what to avoid can easily be applied to other industries.
Face Recognition with Python, in under 25 lines of code; Try #DeepLearning in #Python w. a fully pre-configured VM; Homo Bayesians #MachineLearning #humor #cartoon; The Most Popular Language For #MachineLearning, #DataScience Is ...
Are you and your data "having issues?" JMP real-world case studies help you solve them with key insights on overcoming the challenges with data collection, preparation, and analysis.
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
We know Big Data & Analytics are new & cutting edge technologies; but actually, human started using data & analytics techniques 5000 years ago. Let’s take a look.
Coming soon: TDWI Chicago Conference, PAW San Francisco, #BAChicago, Apache: Big Data Miami, Train AI SanFran, Strata + Hadoop World London, Deep Learning Summit Boston, Spark Summit San Francisco, Postgres Vision Boston, and more.
The courses offered in the Penn State World Campus 30-credit online Master of Professional Studies in Data Analytics – Business Analytics Option could enhance your potential in this growing field.
Use code KDPV17 to save on Postgres Vision, June 26-28, 2017, at the Royal Sonesta Boston. Co-hosted by EnterpriseDB and MIT, the event sponsors include Amazon Web Services, Avnet, credativ, EnterpriseDB, IBM, Microsoft, MIT, NEC, Palisade Compliance, Quest, TechData, and The Executive Council.
Built for speed and scalability, DataRobot radically reduces the time of data science projects - from data to deployment, enabling organizations to bring products to market and react to changing conditions faster. Learn more in June 6 webinar and live demo.
There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.
While programming languages will never be completely obsolete, a growing number of programmers (and data scientists) prefer working with frameworks and view them as the more modern and cutting-edge option for a number of reasons.
Predictive Analytics World for Business is coming to Chicago, June 19-22, 2017. NOW is the time to save $400! Register before early bird pricing ends May 5, and learn from industry leading firms.
The Guerrilla Guide to Machine Learning with Python; How to understand Gradient Descent algorithm; Cartoon: Machine Learning – What They Think I Do; AI & Machine Learning Black Boxes: The Need for Transparency and Accountability; How to Build a Recurrent Neural Network in TensorFlow
For the third year in a row, CrowdFlower surveyed data scientists (nearly 200 this year) from all manner of organizations, which they have compiled into one free report which you can be downloaded now. This year, lots of insights into the word of AI are included.
Using TensorFlow from Python is like using Python to program another computer. Being thoughtful about the graphs you construct can help you avoid confusion and costly performance problems.
10 days may not seem like a lot of time, but with proper self-discipline and time-management, 10 days can provide enough time to gain a survey of the basic of machine learning, and even allow a new practitioner to apply some of these skills to their own project.
Machine Learning Workflows in Python from Scratch Part 1: Data Preparation
Text Mining 101: Mining Information From A Resume
New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll
New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?
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