- Webinar: Improve Your CLASSIFICATION with CART(r) and RandomForests(r), Mar 29 - Mar 27, 2017.
We discuss the advantages of tree based techniques, including automatic variable selection, variable interactions, nonlinear relationships, outliers, and missing values.
- 7 More Steps to Mastering Machine Learning With Python - Mar 1, 2017.
This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
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- What I Learned Implementing a Classifier from Scratch in Python - Feb 28, 2017.
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.
- 17 More Must-Know Data Science Interview Questions and Answers - Feb 15, 2017.
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.
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- The Costs of Misclassifications - Dec 14, 2016.
Importance of correct classification and hazards of misclassification are subjective or we can say varies on case-to-case. Lets see how cost of misclassification is measured from monetary perspective.
- Data Science Basics: What Types of Patterns Can Be Mined From Data? - Dec 14, 2016.
Why do we mine data? This post is an overview of the types of patterns that can be gleaned from data mining, and some real world examples of said patterns.
- The Best Metric to Measure Accuracy of Classification Models - Dec 7, 2016.
Measuring accuracy of model for a classification problem (categorical output) is complex and time consuming compared to regression problems (continuous output). Let’s understand key testing metrics with example, for a classification problem.
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- arXiv Paper Spotlight: Automated Inference on Criminality Using Face Images - Dec 7, 2016.
This recent paper addresses the use of still facial images in an attempt to differentiate criminals from non-criminals, doing so with the help of 4 different classifiers. Results are as troubling as they are unsettling.
- Random Forests in Python - Dec 2, 2016.
Random forest is a highly versatile machine learning method with numerous applications ranging from marketing to healthcare and insurance. This is a post about random forests using Python.
- Introduction to Machine Learning for Developers - Nov 28, 2016.
Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning.
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- Neighbors Know Best: (Re) Classifying an Underappreciated Beer - Nov 24, 2016.
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.
- Artificial Intelligence Classification Matrix - Nov 3, 2016.
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.
- MLDB: The Machine Learning Database - Oct 17, 2016.
MLDB is an opensource database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
- The Evolution of Classification, Oct 19, Oct 26 Webinars - Oct 7, 2016.
Join us for this two part webinar series on the Evolution of Classification, presented by Senior Scientist, Mikhail Golovnya.
- Neural Designer: Predictive Analytics Software - Sep 26, 2016.
Neural Designer advanced neural network algorithms, combined with a simple user interface and fast performance, make it a great tool for data scientists. Download free 15-day trial version.
- A Primer on Logistic Regression – Part I - Aug 24, 2016.
Gain an understanding of logistic regression - what it is, and when and how to use it - in this post.
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- Improving Nudity Detection and NSFW Image Recognition - Jun 25, 2016.
This post discussed improvements made in a tricky machine learning classification problem: nude and/or NSFW, or not?
- Machine Learning Key Terms, Explained - May 25, 2016.
An overview of 12 important machine learning concepts, presented in a no frills, straightforward definition style.
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- KDnuggets™ News 16:n16, May 4: How to Remove Duplicates from Large Data; Datasets over Algorithms; When Automation goes too far - May 4, 2016.
How to Remove Duplicates in Large Datasets; The Development of Classification as a Learning Machine; Datasets Over Algorithms; Cartoon: When Automation Goes Too Far, and more.
- The Development of Classification as a Learning Machine - Apr 29, 2016.
An explanation of how classification developed as a learning machine, from LDA to the perceptron, on to logistic regression, and through to support vector machines.
- Salford Predictive Modeler 8: Faster. More Machine Learning. Better results - Apr 4, 2016.
Take a giant step forward with SPM 8: Download and try it for yourself just released version 8 and get better results.
- What Dog Breed is That? Let AI “fetch” it for you! - Feb 25, 2016.
Recently released AI app identifies dog breed information from pictures and mixes some fun too.
- Amazon Machine Learning: Nice and Easy or Overly Simple? - Feb 17, 2016.
Amazon Machine Learning is a predictive analytics service with binary/multiclass classification and linear regression features. The service is fast, offers a simple workflow but lacks model selection features and has slow execution times.
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- Data Analytics Boosting Digital Engagement at Australian Open 2016 - Jan 25, 2016.
Advanced analytics and visualization is enhancing fan experience and operational excellence at Australian Open 2016
- What questions can data science answer? - Jan 1, 2016.
There are only five questions machine learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next? We examine these questions in detail and what it implies for data science.
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- ebook: Learning Apache Mahout Classification - May 15, 2015.
If you are a data scientist with Hadoop experience and interest in machine learning, this book is for you. Learn about different classification in Apache Mahout and build your own classifiers.
- Fundamental methods of Data Science: Classification, Regression And Similarity Matching - Jan 12, 2015.
Data classification, regression, and similarity matching underpin many of the fundamental algorithms in data science to solve business problems like consumer response prediction and product recommendation.
- “Vite fait, bien fait” – Averaging improves both accuracy and speed of time series classification - Dec 21, 2014.
Time series classification using k-nearest neighbors and dynamic time warping can be improved in many practical applications in both speed and accuracy using averaging.
- Upcoming Webcasts on Analytics, Big Data, Data Science – Oct 7 and beyond - Oct 6, 2014.
Evolution of Classification, Billion Dollar Fraud Detection, Big Data Visualization, Deep Learning on Apache Spark, and more.
- One-handed Keystroke Biometric Identification Competition - Oct 2, 2014.
Build a biometric keystroke classifier in this new competition to help identify the features that best predict one-handed typing samples. The prize for first place is a fingerprint scanner.
- Upcoming Webcasts on Analytics, Big Data, Data Science – Sep 30 and beyond - Sep 29, 2014.
Not all graph databases are created equal, Evolution of Classification, Governing Big Data, Big Data Visualization, Best Practices for Applying Advanced Analytics in Hadoop, and more.
- Upcoming Webcasts on Analytics, Big Data, Data Science – Sep 22 and beyond - Sep 22, 2014.
Future of Hadoop Analytics, What Works: Open Source Analytics Software, Data Mining: Failure To Launch, Evolution of Classification, Not all Graph Databases are created equal, Best Practices for Applying Advanced Analytics in Hadoop, and more.
- Data Analytics for Business Leaders Explained - Sep 22, 2014.
Learn about a variety of different approaches to data analytics and their advantages and limitations from a business leader's perspective in part 1 of this post on data analytics techniques.
- Interview: Vita Markman, LinkedIn on Discovering Customer Insights through Sentiment Mining - Aug 5, 2014.
We discuss examples of discovery through sentiment mining, current trends, innovative applications, important soft skills, and more.
- Interview: Vita Markman, LinkedIn on Practical Solutions for Sentiment Mining Challenges - Aug 4, 2014.
We discuss sentiment data models, significance of linguistic features, handling the noise in social conversations, industry challenges, important use cases and the appropriateness of over-simplified binary classification.
- Interview: Kavita Ganesan, FindiLike on Building Decision Support Systems based on User Opinions - Jul 27, 2014.
We discuss the founding story of FindiLike, Opinion-driven Decision Support Systems (ODSS), challenges in analyzing user opinions, future of Sentiment Analysis, favorite books and more.
- Interview: Vasanth Kumar, Principal Data Scientist, Live Nation - May 2, 2014.
We discuss challenges in analyzing bursty data, real-time classification, relevance of statistics and advice for newcomers to Data Science.