Jun
2016 Jul
All (112) | Courses, Education (13) | Meetings (8) | News, Features (17) | Opinions, Interviews, Reports (31) | Software (8) | Tutorials, Overviews (31) | Webcasts & Webinars (4)
- Getting Started with Data Science – Python
- Aug 1, 2016.
A great introductory post from DataRobot on getting started with data science in the Python ecosystem, including cleaning data and performing predictive modeling.
- KDnuggets Free Bronze Pass to Strata + Hadoop World New York City, Sep 28-29, 2016
- Jul 30, 2016.
Strata + Hadoop World is the leading event on how big data and ubiquitous, real-time computing is shaping the course of business and society. Win KDnuggets free pass to Strata + Hadoop World New York City.
- Data Science of Visiting Famous Movie Locations in San Francisco
- Jul 30, 2016.
Using the Google Places API and IMDb API, we selected movie locations in The Golden City which every movie fan should visit while they are in town, and optimize sightseeing by solving the travelling salesman problem.
- Theoretical Data Discovery: Using Physics to Understand Data Science
- Jul 29, 2016.
Data science may be a relatively recent buzzword, but the collection of tools and techniques to which it refers come from a broad range of disciplines. Physics has a wealth of concepts to learn from, as evidenced in this piece.
- Deep Learning For Chatbots, Part 2 – Implementing A Retrieval-Based Model In TensorFlow
- Jul 29, 2016.
Check out part 2 of this tutorial on building chatbots with deep neural networks. This part gets practical, and using Python and TensorFlow to implement.
- Build vs Buy – Analytics Dashboards
- Jul 29, 2016.
Read this post on choosing between available analytics dashboard options, and designing your own. Get an informed opinion.
- Data Science Statistics 101
- Jul 28, 2016.
Statistics can often be the most intimidating aspect of data science for aspiring data scientists to learn. Gain some personal perspective from someone who has traveled the path.
- Data Science for Beginners 2: Is your data ready?
- Jul 28, 2016.
This second video and write-up in the Data Science for Beginners series discusses what is required of your data before it can be useful.
- The steps in the machine learning workflow
- Jul 28, 2016.
We outline preprocessing steps for finding, removing, and cleaning data to prepare it for machine learning and how tools like MATLAB can help with data exploration, identification of key traits, and communicating the findings.
- Database Key Terms, Explained
- Jul 28, 2016.
Interested in a survey of important database concepts and terminology? This post defines 16 essential database key terms concisely and accurately.
- Top KDnuggets tweets, Jul 20-26: Math-free simple explanation: #DeepLearning Demystified; Are #Humans Becoming More Machine-Like?
- Jul 27, 2016.
Finally, a #TensorFlow book for humans; Great math-free simple intro explanation video: Deep Learning Demystified; Does #sentiment analysis work? A tidy analysis of Yelp reviews; JupyterLab: the next generation of the #Jupyter Notebook
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 3
- Jul 27, 2016.
This is the final part of a 3 part introductory series on machine learning in Python, using the Titanic dataset.
- 7 Steps to Understanding NoSQL Databases
- Jul 27, 2016.
Are you a newcomer to NoSQL, interested in gaining a real understanding of the technologies and architectures it includes? This post is for you.
- Internet of Things Key Terms, Explained
- Jul 27, 2016.
This post will define 12 Key Terms for the Internet of Things, in straightforward manner.
- Analytical Approaches to Solving Problems in Communications and Media
- Jul 26, 2016.
Discover a set of techniques and methodologies to analyze and explore telecommunications data in order to improve business and operational performances. This new course debuts Sep 14 at Analytics Experience 2016 in Las Vegas.
- Boost your Business Analytics Skills
- Jul 26, 2016.
Learn the latest business practices, concepts, methodologies and techniques in advanced analytics, data mining, survival analysis, explaining analytics to decision makers, fraud detection, and more with the SAS Business Knowledge Series.
- Predictive Analytics World for Government, Washington, DC, Oct 17-20
- Jul 26, 2016.
PAW Government provides the best information on applying predictive analytics to government with a special track that includes technical training on most relevant tools and concepts. Get extra KDnuggets discount w. code KDN150.
- The Fallacy of Seeing Patterns
- Jul 26, 2016.
Analysts are often on the lookout for patterns, often relying on spurious patterns. This post looks at some spurious patterns in univariate, bivariate & multivariate analysis.
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 2
- Jul 26, 2016.
This is part 2 of a 3 part introductory series on machine learning in Python, using the Titanic dataset.
- Data Science for Beginners 1: The 5 questions data science answers
- Jul 26, 2016.
A series of videos and write-ups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.
- Online MS in data science and analytics | Deadline Aug. 6
- Jul 26, 2016.
Sharpen your edge with an online master’s degree in data science and analytics (MS) from the University of Missouri Informatics Institute. Apply today!
- Barley, Hops, and Bayes: Predicting The World Beer Cup
- Jul 26, 2016.
This post covers predicting award counts by the United States in an international beer competition. Exploratory data analysis and Bayes methods are also supported.
- Global Big Data Conference, Santa Clara, Aug 30 – Sep 1, 2016
- Jul 25, 2016.
Understand emerging big data trends, develop new technical skills through hands on workshops, analyze multiple industry case studies, learn emerging best practices in big data. Use code KDNUGGETS to save.
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 1
- Jul 25, 2016.
Check out the first of a 3 part introductory series on machine learning in Python, fueled by the Titanic dataset. This is a great place to start for a machine learning newcomer.
- Why Do Deep Learning Networks Scale?
- Jul 25, 2016.
A discussion of what about deep learning architectures allows them to scale, and addresses some assumptions that often inhibit an understanding of this topic.
- Data Analytics Bootcamp to make you irreplaceable
- Jul 25, 2016.
Become irreplaceable at Level Bootcamp by learning how to use data to solve real problems. Get 15% KDnuggets discount for upcoming programs in Boston, Seattle, Charlotte, Silicon Valley, and online.
- Top Stories, July 18-24: Why Big Data is in Trouble; In Deep Learning, Architecture Engineering is the New Feature Engineering
- Jul 25, 2016.
Why Big Data is in Trouble: They Forgot About Applied Statistics; In Deep Learning, Architecture Engineering is the New Feature Engineering; 5 Big Data Projects You Can No Longer Overlook; What Has Pokemon Got To Do With Big Data?
- What Has Pokemon Got To Do With Big Data?
- Jul 23, 2016.
For me, the millions of people around the world playing Pokémon last weekend (and crashing their servers on a regular basis) showed me a glimpse of the future. There may well be an opportunity for real-time Big Data - I will give you a glimpse.
- Building a Data Science Portfolio: Machine Learning Project Part 3
- Jul 22, 2016.
The final installment of this comprehensive overview on building an end-to-end data science portfolio project focuses on bringing it all together, and concludes the project quite nicely.
- Machine Learning: Separating Hype From Reality
- Jul 22, 2016.
When it comes to business value and ROI, does machine learning live up tot he claims? We’ll explore a pure machine learning approach through the lens of a typical enterprise use case.
- Webinar, July 28: How Open Data Science Can Help Analytics Leaders Survive & Thrive in an Era of Accelerating Technology Disruption
- Jul 22, 2016.
Continuum Analytics CTO Peter Wang will show how you, an analytics leader, and your team can continuously leverage the latest innovations in data, analytics and computation by joining the big data party in the Open Data Science tent.
- SAS vs R vs Python: Which Tool Do Analytics Pros Prefer?
- Jul 22, 2016.
There are lots of flame wars involving different data science and analytics tools... but this isn't one of them. Check out the quantitative results and analysis of a Burtch Works survey on the subject.
- Improve Your Regression with Modern Regression Analysis Techniques, July 27, Aug 10 Webinars
- Jul 22, 2016.
This two part webinar will help you improve your regression using modern regression analysis techniques. July 27 (part 1) and August 10 (part 2).
- Big Data Bootcamp, Boston, Aug 19-21
- Jul 21, 2016.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape. No prior knowledge of databases or programming is assumed. Use code KDNUGGETS to save - extra discount if you register by July 31.
- An education so adventurous, we wrote a field guide
- Jul 21, 2016.
Through our project-based graduate program, you'll get an ethical approach to data science, helping businesses untangle the complexities of data collection and analytics to build a better business and a more equitable society. Now that's a beautiful thing.
- Building a Data Science Portfolio: Machine Learning Project Part 2
- Jul 21, 2016.
The second part of this comprehensive overview on building an end-to-end data science portfolio project concentrates on data exploration and preparation.
- Interesting Things I Learned at SciPy 2016
- Jul 21, 2016.
Learn about some interesting projects featured at SciPy 2016, brought to you by an attendee who put in the work to bring you this great list of projects.
- Introducing Cloud Hosted Deep Learning Models
- Jul 21, 2016.
Algorithmia introduces a solution for hosting and distributing locally-trained deep learning models on Algorithmia using GPUs in the cloud, where they become smart API endpoints for other developers to use.
- 5 Big Data Projects You Can No Longer Overlook
- Jul 21, 2016.
Check out 5 Big Data projects that you are not likely to have seen before, but which may be useful to you, and perhaps even scratch an itch you didn't know you had.
- Building a Data Science Portfolio: Machine Learning Project Part 1
- Jul 20, 2016.
Dataquest's founder has put together a fantastic resource on building a data science portfolio. This first of three parts lays the groundwork, with subsequent posts over the following 2 days. Very comprehensive!
- Top KDnuggets tweets, Jul 13 – Jul 19: Bayesian #MachineLearning, Explained; Introducing JupyterLab
- Jul 20, 2016.
Bayesian #MachineLearning, Explained; JupyterLab: the next generation of the #Jupyter Notebook; On the importance of democratizing #ArtificialIntelligence
- Multi-Task Learning in Tensorflow: Part 1
- Jul 20, 2016.
A discussion and step-by-step tutorial on how to use Tensorflow graphs for multi-task learning.
- 10 Great Talks From SciPy 2016
- Jul 20, 2016.
Here's a curated short list of interesting and insightful talks to watch from SciPy 2016 to help guide your search through the volume of great video material emerging from the conference.
- In Deep Learning, Architecture Engineering is the New Feature Engineering
- Jul 19, 2016.
A discussion of architecture engineering in deep neural networks, and its relationship with feature engineering.
- What the Next Generation of IoT Sensors Have in Store
- Jul 19, 2016.
This post is an overview of some of the next-generation IoT sensors, and what they could mean for our future.
- MNIST Generative Adversarial Model in Keras
- Jul 19, 2016.
This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.
- Online Master of Science in Predictive Analytics
- Jul 19, 2016.
Build in-demand skills for the growing analytics field with the Northwestern University Master of Science in Predictive Analytics degree, completely online.
- Statistical Data Analysis in Python
- Jul 18, 2016.
This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.
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Why Big Data is in Trouble: They Forgot About Applied Statistics
- Jul 18, 2016.
This "classic" (but very topical and certainly relevant) post discusses issues that Big Data can face when it forgets, or ignores, applied statistics. As great of a discussion today as it was 2 years ago. - Predictive Analytics Introductory Key Terms, Explained
- Jul 18, 2016.
Here is a collection of introductory predictive analytics terms and concepts, presented for the newcomer in a straight-forward, no frills definition style.
- O’Reilly AI: Last chance to get Best Price
- Jul 18, 2016.
This week is your last chance to get the Best Price for the O'Reilly Artificial Intelligence Conference happening in New York September 26-27. Register with your KDnuggets discount code now!
- Top Stories, July 11–17: Top Machine Learning MOOCs and Online Lectures; Bayesian Machine Learning, Explained
- Jul 18, 2016.
Top Machine Learning MOOCs and Online Lectures; Bayesian Machine Learning, Explained; 10 Algorithm Categories for A.I., Big Data, and Data Science; 5 Deep Learning Projects You Can No Longer Overlook; The Hard Problems AI Can't (Yet) Touch
- KDnuggets Interview: Inderpal Bhandari, IBM Global Chief Data Officer on 4 key ideas of Cognitive Computing
- Jul 17, 2016.
In this wide-ranging interview, we discuss the role of IBM global chief data officer, 4 key ideas of cognitive computing, risks of AI, IBM Data Science Experience, healthcare, basketball, sports analytics, and more.
- America’s Next Topic Model
- Jul 15, 2016.
Topic modeling is a a great way to get a bird's eye view on a large document collection using machine learning. Here are 3 ways to use open source Python tool Gensim to choose the best topic model.
- Data Mining Most Vexing Problem Solved, or is this drug REALLY working?
- Jul 15, 2016.
This is a summary of the basic principle behind a new paper on multiple test correction for streams and cascades of statistical hypothesis tests, showing how to strictly control the risk of making a mistake over a series of tests and draw appropriate conclusions.
- 4 Major Trends Disrupting the Data Science Market
- Jul 15, 2016.
An interesting excerpt from Burtch Works' recently published Burtch Works Study: Salaries of Data Scientists 2016, focusing on trends disrupting the data science market.
- 2016’s Best Places for Data Scientist Jobs
- Jul 15, 2016.
Get the info on the Best Places in the U.S. for Data Scientist Jobs with GoodCall's new data-driven report.
- Data Mining/Data Science “Nobel Prize”: 2016 SIGKDD Innovation Award to Philip S. Yu
- Jul 15, 2016.
Dr. Philip S. Yu wins ACM KDD Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data.
- 10 Algorithm Categories for AI, Big Data, and Data Science
- Jul 14, 2016.
With a focus on leveraging algorithms and balancing human and AI capital, here are the top 10 algorithm categories used to implement A.I., Big Data, and Data Science.
- How to Start Learning Deep Learning
- Jul 14, 2016.
Want to get started learning deep learning? Sure you do! Check out this great overview, advice, and list of resources.
- What Data Scientists Can Learn From Qualitative Research
- Jul 14, 2016.
Learn what data scientists can learn from qualitative researchers when it comes to analysing text, and how this relates to writing quality code.
- Online Courses: Big Data Projects and Data Science Pipelines
- Jul 14, 2016.
Check out these online courses from O'Reilly Media on managing big data projects and building distributed data pipelines.
- 2016 SIGKDD Service Award to Wei Wang
- Jul 14, 2016.
Prof. Wei Wang wins ACM SIGKDD 2016 Service Award for her significant technical contributions to the principles, practice and application of data mining and for her outstanding services to society and the data mining community.
- Take a Risk Free Hadoop Ride. Save up to 80% cost and offload time.
- Jul 14, 2016.
The Impetus Data Warehouse Workload Migration product is a proven, cost-effective, and low-risk solution to offload traditional data warehouse to Big Data warehouse. Contact us for a proof-of-concept.
- Top KDnuggets tweets, Jul 6 – Jul 12: Statistical Data Analysis #Python #Jupyter Notebooks; Modern Pandas Notebooks
- Jul 13, 2016.
Statistical Data Analysis in #Python (#Jupyter Notebooks); Modern Pandas: idiomatic Pandas notebook collection; New (free) book by @rdpeng: #rstats Programming for #DataScience
- Metis Data Science Open Houses: San Francisco and New York City
- Jul 13, 2016.
Visit Metis in San Francisco (July 14) and New York City (July 20) to learn about their 12-week data science bootcamps.
- What do Postgres, Kafka, and Bitcoin Have in Common?
- Jul 13, 2016.
These three technologies on the surface couldn't look any more different, but under the hood they have one interesting thing in common.
- Bayesian Machine Learning, Explained
- Jul 13, 2016.
Want to know about Bayesian machine learning? Sure you do! Get a great introductory explanation here, as well as suggestions where to go for further study.
- Explore your unstructured text data
- Jul 13, 2016.
Learn examples of success with text exploration, what engineers and scientists can (and should) do with text data, and the consequences of collecting data and doing nothing with it.
- A Survey of Available Corpora for Building Data-driven Dialogue Systems
- Jul 12, 2016.
This post is a summary of Serban, et al. "A Survey of Available Corpora for Building Data-Driven Dialogue Systems," which is of increasing relevance given the recent state of conversational AI.
- TMA Predictive Analytics and Data Mining Training – Live Online, August
- Jul 12, 2016.
Successful analytics in the big data era does not start with data and software, but with hands-on, immersive training and goal-driven strategy - get it from The Modeling Agency online in August.
- Semi-supervised Feature Transfer: The Practical Benefit of Deep Learning Today?
- Jul 12, 2016.
This post evaluates four different strategies for solving a problem with machine learning, where customized models built from semi-supervised "deep" features using transfer learning outperform models built from scratch, and rival state-of-the-art methods.
- Webinar: Predictive Analytics: Failure to Launch [July 14]
- Jul 12, 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 July 14.
- Using Big Data and Predictive Analytics to Reach 63% Growth – case study
- Jul 12, 2016.
Join The Big Data Channel and Innovation Enterprise for three summits September 8 & 9 in Boston, where KDnuggets readers get a 10% discount. Register now!
- TalkingData Data Science Competition: understand mobile users
- Jul 12, 2016.
Unique opportunity to solve complex real world big data challenges for the China mobile market - predict users demographic characteristics based on their app usage, geolocation, and mobile device properties.
- 5 Deep Learning Projects You Can No Longer Overlook
- Jul 12, 2016.
There are a number of "mainstream" deep learning projects out there, but many more niche projects flying under the radar. Have a look at 5 such projects worth checking out.
- Top Stories, July 4–10: The Invention of Support Vector Machines; Storytelling: The Power to Influence in Data Science
- Jul 11, 2016.
Data Mining History: The Invention of Support Vector Machines; Storytelling: The Power to Influence in Data Science; Support Vector Machines: A Simple Explanation; Big Data, Bible Codes, and Bonferroni
- The Hard Problems AI Can’t (Yet) Touch
- Jul 11, 2016.
It's tempting to consider the progress of AI as though it were a single monolithic entity, advancing towards human intelligence on all fronts. But today's machine learning only addresses problems with simple, easily quantified objectives
- Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey
- Jul 11, 2016.
This post reviews Machine Learning MOOCs and online lectures for both the novice and expert audience.
- NYU Stern – Master of Science in Business Analytics
- Jul 11, 2016.
Learn about the NYU Stern MS in Business Analytics, the only premier global degree program of its kind designed for senior level professionals focused on the intersection of business strategy and data science.
- U. Delaware Certificate in Analytics: Optimizing Big Data
- Jul 11, 2016.
This certificate program brings together the computational, analytical, communication skills, and the tools needed to analyze big data to make better business decisions. Classes run Sep 8 - Dec 15 in Wilmington, DE.
- New Book: Effective CRM using Predictive Analytics – get 20% discount
- Jul 11, 2016.
A comprehensive step-by-step guide to designing, setting up, executing and deploying data mining techniques in marketing. Use code VBM93 for 20% discount.
- Why You Should Attend the Data Science Summit 2016 and 9 Talks To Be Excited About
- Jul 9, 2016.
Here is a preview of the Data Science Summit, July 12-13 in San Francisco, where you can meet quality people hear exciting talks like 9 described here. Get get 20% with the code SFDATASCIENCE.
- Big Data, Bible Codes, and Bonferroni
- Jul 8, 2016.
This discussion will focus on 2 particular statistical issues to be on the look out for in your own work and in the work of others mining and learning from Big Data, with real world examples emphasizing the importance of statistical processes in practice.
- Glimpses & Impressions: Strata Silicon Valley AI + ML Review – Part Two
- Jul 8, 2016.
Read some impressions from onsite visits to 2 companies during Strata Silicon Valley in March: Novumind and Numenta.
- Streamlining Analytic Deployment: Inside the FICO Decision Management Suite 2.0
- Jul 8, 2016.
This post explains what’s new in the 2.0 version of the FICO Decision Management Suite, and how it can be used by data scientists and others to create stronger customer relationships and provide strategic competitive advantage.
- AI for Fun & Profit: Using the new Genie Cognitive Computing Platform for P2P Lending
- Jul 8, 2016.
This tutorials uses the recently-released Genie (an acronym for General Evolving Networked Intelligence Engine) platform to learn from P2P (peer-to-peer) loan data. Experts and non-experts alike can leverage Genie to analyze Big Data, recognize objects, events, and patterns, and more.
- Support Vector Machines: A Simple Explanation
- Jul 7, 2016.
A no-nonsense, 30,000 foot overview of Support Vector Machines, concisely explained with some great diagrams.
- Interview: Florian Douetteau, Dataiku Founder, on Empowering Data Scientists
- Jul 7, 2016.
Here is an interview with Florian Douetteau, founder of Dataiku, on how their tools empower data scientists, and how data science itself is evolving.
- Deep Residual Networks for Image Classification with Python + NumPy
- Jul 7, 2016.
This post outlines the results of an innovative Deep Residual Network implementation for Image Classification using Python and NumPy.
- GE Intelligent World Hackathon: Insights from Street Lights
- Jul 7, 2016.
In the Intelligent World Hackathon, running now through August 2, you can be one of the first developers to access GE smart LED streetlight network data and build urban apps on Predix, GE’s new IIoT data analytics platform.
- Glimpses & Impressions: Strata Silicon Valley AI + ML Review – Part One
- Jul 7, 2016.
Read some impressions from a visit to Strata Silicon Valley in March. The focus is on integration of data science and machine learning tools, as well as the simplification of related processes.
- Logit Data Science Academy fall session in LA – Apply now
- Jul 7, 2016.
Logit Academy full-time 12-week hands-on program is taught by data scientists from top institutions in Southern California, including UCLA, USC and Caltech. Register now for Next session which begins Sep 19.
- Top KDnuggets tweets, Jun 29 – Jul 5: Big Data Ecosystem is Too Damn Big!; Deep Learning Intro with Caffe and Python
- Jul 6, 2016.
The #BigData Ecosystem is Too Damn Big!; A Practical Introduction to #DeepLearning with Caffe and #Python; What do Postgres, Kafka, and Bitcoin have in common?
- Storytelling: The Power to Influence in Data Science
- Jul 6, 2016.
Data scientists need to share results, which is different than talking shop with other data scientists. Read about influencing people and telling stories as a data scientist.
- Success Criteria for Process Mining
- Jul 6, 2016.
This article provides tips about the pitfalls and advice that will help you to make your first process mining project as successful as it can be.
- Mining Twitter Data with Python Part 7: Geolocation and Interactive Maps
- Jul 6, 2016.
The final part of this 7 part series explores using geolocation and interactive maps with Twitter data.
- 3 Key Ethics Principles for Big Data and Data Science
- Jul 6, 2016.
If ethics in general are important, should ethics training be a crucial element of the data science field?
- How to Compare Apples and Oranges ? : Part III
- Jul 6, 2016.
In the previous article, look at techniques to compare categorical variables with the help of an example. In this article, we shall look at techniques to compare mixed type of variables i.e. numerical and categorical variables together.
- Getting Started with Analytics: What’s the Upfront Investment?
- Jul 5, 2016.
Everyone wants to leverage analytics, but should everyone dive into the deep end right away? Heed some sensible advice on getting started with analytics, and assessing the true upfront investment.
- A Brief Primer on Linear Regression – Part III
- Jul 5, 2016.
This third part of an introduction to linear regression moves past the topics covered in the first to discuss linearity, normality, outliers, and other topics of interest.
- Top June stories: The Difference Deep Learning and “Regular” Machine Learning? R, Python duel as top Data Science tools.
- Jul 5, 2016.
Also Data Science of Variable Selection; The Big Data Ecosystem is Too Damn Big.
- Mining Twitter Data with Python Part 6: Sentiment Analysis Basics
- Jul 5, 2016.
Part 6 of this series builds on the previous installments by exploring the basics of sentiment analysis on Twitter data.
- NLP, Sentiment Analysis, Consumer and Market Insights at SAS16
- Jul 5, 2016.
The next Sentiment Analysis Symposium (the premier industry event) takes place July 12 in New York. Register today with your 10% KDnuggets discount!
- Top /r/MachineLearning Posts, June: Microsoft Videos, Machine Learning Training Pathway, Free Books!
- Jul 5, 2016.
Microsoft Research Machine Learning Videos; Free Machine Learning Training Pathway; Andrew Ng's New Book; Coursera Removing Free Online Courses; Free Books!
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in June 2016
- Jul 5, 2016.
Positions at Center for Data Science and Public Policy at U. Chicago; Business Analytics Lecturer at U. Iowa; IBM Social Good Fellow; Data quality postdoc at McMaster U; Asst. Prof. of Marketing at Yale, and more.
- Top Stories, June 27 – July 3: Big Data Ecosystem is Too Damn Big; 5 More Machine Learning Projects You Can’t Overlook
- Jul 4, 2016.
The Big Data Ecosystem is Too Damn Big; 5 More Machine Learning Projects You Can No Longer Overlook; 7 Steps to Mastering Machine Learning With Python; Machine Learning Trends and the Future of Artificial Intelligence
- Data Mining History: The Invention of Support Vector Machines
- Jul 4, 2016.
The story starts in Paris in 1989, when I benchmarked neural networks against kernel methods, but the real invention of SVMs happened when Bernhard decided to implement Vladimir Vapnik algorithm.
- Upcoming Meetings in Analytics, Big Data, Data Mining, Data Science, Machine Learning: July and Beyond
- Jul 1, 2016.
Coming soon: Sentiment Analysis Symposium, Data Science Summit, TDWI Accelerate, CDAO Singapore, KDD 2016, HPE Big Data Boston, and many more.
- What is Softmax Regression and How is it Related to Logistic Regression?
- Jul 1, 2016.
An informative exploration of softmax regression and its relationship with logistic regression, and situations in which each would be applicable.
- Three Impactful Machine Learning Topics at ICML 2016
- Jul 1, 2016.
This post discusses 3 particular tutorial sessions of impact from the recent ICML 2016 conference held in New York. Check out some innovative ideas on Deep Residual Networks, Memory Networks for Language Understanding, and Non-Convex Optimization.
- Text Mining 101: Topic Modeling
- Jul 1, 2016.
We introduce the concept of topic modelling and explain two methods: Latent Dirichlet Allocation and TextRank. The techniques are ingenious in how they work – try them yourself.