- What Top Firms Ask: 100+ Data Science Interview Questions - Mar 22, 2017.
Check this out: A topic wise collection of 100+ data science interview questions from top companies.
- Getting Up Close and Personal with Algorithms - Mar 21, 2017.
We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. Read to learn more about Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.
- Toward Increased k-means Clustering Efficiency with the Naive Sharding Centroid Initialization Method - Mar 13, 2017.
What if a simple, deterministic approach which did not rely on randomization could be used for centroid initialization? Naive sharding is such a method, and its time-saving and efficient results, though preliminary, are promising.
- Netflix: Manager, Content Programming Science & Algorithms - Feb 27, 2017.
Seeking a Manager of Content Programming Science & Algorithms, an experienced and entrepreneurial-minded data scientist. This is high-impact and challenging role, and will require both strong leadership and technical prowess.
- 17 More Must-Know Data Science Interview Questions and Answers, Part 2 - Feb 22, 2017.
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.
- Top KDnuggets tweets, Jan 25-31: Python implementations of Andrew Ng #MachineLearning MOOC exercises - Feb 1, 2017.
#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.
- KDnuggets™ News 17:n04, Feb 1: Data Science and Python Wrangling: Pandas Cheat Sheet; Great Collection of Machine Learning Algorithms - Feb 1, 2017.
Also Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms; Bad Data + Good Models = Bad Results; Data Scientist - best job in America, again.
- Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms - Jan 25, 2017.
Interested in learning machine learning algorithms by implementing them from scratch? Need a good set of examples to work from? Check out this post with links to minimal and clean implementations of various algorithms.
- Zoll LifeVest: Advisory Researcher, Predictive Algorithms - Jan 18, 2017.
Seeking individuals to conduct applied research on predictive algorithms to advance the company strategy of improving outcomes for patients at risk of Sudden Cardiac Arrest, collaborating with teams of Physicians, Software Engineers, Data Scientists, and Machine Learning Specialists.
- More Data or Better Algorithms: The Sweet Spot - Jan 17, 2017.
We examine the sweet spot for data-driven Machine Learning companies, where is not too easy and not too hard to collect the needed data.
- 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.
- Ethical Implications Of Industrialized Analytics - Nov 29, 2016.
Analytics & Big Data will be involved in every aspect of our lives and we should handle the ethical dilemmas wisely to let innovation contribute more to our lives.
- Linear Regression, Least Squares & Matrix Multiplication: A Concise Technical Overview - Nov 24, 2016.
Linear regression is a simple algebraic tool which attempts to find the “best” line fitting 2 or more attributes. Read here to discover the relationship between linear regression, the least squares method, and matrix multiplication.
- Predictive Science vs Data Science - Nov 22, 2016.
Is Predictive Science accurately represented by the term Data Science? As a matter of fact, are any of Data Science's constituent sciences well-represented by the umbrella term? This post discusses a few of these points at a high level.
- The Foundations of Algorithmic Bias - Nov 16, 2016.
We might hope that algorithmic decision making would be free of biases. But increasingly, the public is starting to realize that machine learning systems can exhibit these same biases and more. In this post, we look at precisely how that happens.
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- Parallelism in Machine Learning: GPUs, CUDA, and Practical Applications - Nov 10, 2016.
The lack of parallel processing in machine learning tasks inhibits economy of performance, yet it may very well be worth the trouble. Read on for an introductory overview to GPU-based parallelism, the CUDA framework, and some thoughts on practical implementation.
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- Using Predictive Algorithms to Track Real Time Health Trends - Nov 4, 2016.
How to build a real-time health dashboard for tracking a person blood pressure readings, do time series analysis, and then graph the trends over time using predictive algorithms.
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- Decision Tree Classifiers: A Concise Technical Overview - Nov 3, 2016.
The decision tree is one of the oldest and most intuitive classification algorithms in existence. This post provides a straightforward technical overview of this brand of classifiers.
- KDnuggets™ News 16:n39, Nov 2: Machine Learning: A Complete and Detailed Overview; Learn Data Science in 8 (Easy) Steps - Nov 2, 2016.
Machine Learning: A Complete and Detailed Overview; Cartoon: Scary Big Data; Learn Data Science in 8 (Easy) Steps; Is Your Code Good Enough to Call Yourself a Data Scientist?; Using Machine Learning to Detect Malicious URLs; Frequent Pattern Mining and the Apriori Algorithm
- Frequent Pattern Mining and the Apriori Algorithm: A Concise Technical Overview - Oct 27, 2016.
This post provides a technical overview of frequent pattern mining algorithms (also known by a variety of other names), along with its most famous implementation, the Apriori algorithm.
- Top KDnuggets tweets, Oct 12-18: #DeepLearning Key Terms, Explained; Free Foundations of #DataScience text PDF - Oct 20, 2016.
#DeepLearning Key Terms, Explained; Free Foundations of #DataScience text PDF; Top 12 Interesting Careers to Explore in #BigData; #ICYMI The 10 Algorithms #MachineLearning Engineers Need to Know
- Intellectual Ventures Lab: Sr. Machine Learning Algorithm Development Software Engineer - Oct 18, 2016.
Seeking a Senior Machine-Learning Algorithm Development Software Engineer to provide technical leadership to fast-paced machine-learning development projects.
- Rexer Analytics Data Science Survey Highlights - Oct 14, 2016.
Regression, Decision Trees, and Cluster analysis remain the most commonly used algorithms in the field, R continues to ascend, job satisfaction remains high, but customer understanding still needs improvement.
- Top Data Scientist Claudia Perlich’s Favorite Machine Learning Algorithm - Sep 27, 2016.
Interested in the reasons why a top data scientist is partial to one particular algorithm over others? Read on to find out.
- Comparing Clustering Techniques: A Concise Technical Overview - Sep 26, 2016.
A wide array of clustering techniques are in use today. Given the widespread use of clustering in everyday data mining, this post provides a concise technical overview of 2 such exemplar techniques.
- Data Science Basics: 3 Insights for Beginners - Sep 22, 2016.
For data science beginners, 3 elementary issues are given overview treatment: supervised vs. unsupervised learning, decision tree pruning, and training vs. testing datasets.
- Support Vector Machines: A Concise Technical Overview - Sep 21, 2016.
Support Vector Machines remain a popular and time-tested classification algorithm. This post provides a high-level concise technical overview of their functionality.
- KDnuggets™ News 16:n34, Sep 21: The Great Algorithm Tutorial Roundup; 7 Steps to Mastering Apache Spark 2.0 - Sep 21, 2016.
The Great Algorithm Tutorial Roundup; 7 Steps to Mastering Apache Spark 2.0; Machine Learning in a Year: From Total Noob to Effective Practitioner; Learning From Data (Introductory Machine Learning) Caltech MOOC
- The Great Algorithm Tutorial Roundup - Sep 20, 2016.
This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so!
- KDnuggets™ News 16:n33, Sep 14: Top Algorithms Used by Data Scientists; (Not So) New Data Scientist Venn Diagram - Sep 14, 2016.
Top Algorithms Used by Data Scientists; Guide To Understanding Convolutional Neural Nets; The (Not So) New Data Scientist Venn Diagram; Deep Learning Networks with Stochastic Depth.
- Top Algorithms and Methods Used by Data Scientists - Sep 12, 2016.
Latest KDnuggets poll identifies the list of top algorithms actually used by Data Scientists, finds surprises including the most academic and most industry-oriented algorithms.
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- New Poll: Which methods/algorithms you used for a Data Science or Machine Learning application? - Aug 26, 2016.
Which methods/approaches you used in the past 12 months for an actual Data Science-related application? Please vote and we will analyze and publish the results.
- Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - Aug 25, 2016.
Learn about LIME, a technique to explain the predictions of any machine learning classifier.
- A Gentle Introduction to Bloom Filter - Aug 24, 2016.
The Bloom Filter is a probabilistic data structure which can make a tradeoff between space and false positive rate. Read more, and see an implementation from scratch, in this post.
- KDnuggets™ News 16:n31, Aug 24: 10 Algo Machine Learning Engineers Need to Know; How to Become a Data Scientist; Gentle Tensorflow - Aug 24, 2016.
The 10 Algorithms Machine Learning Engineers Need to Know; How to Become a Data Scientist - Part 1; The Gentlest Introduction to Tensorflow - Part 1; Approaching (Almost) Any Machine Learning Problem.
- The 10 Algorithms Machine Learning Engineers Need to Know - Aug 18, 2016.
Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand.
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- Understanding the Empirical Law of Large Numbers and the Gambler’s Fallacy - Aug 12, 2016.
Law of large numbers is a important concept for practising data scientists. In this post, The empirical law of large numbers is demonstrated via simple simulation approach using the Bernoulli process.
- Contest 2nd Place: Automating Data Science - Aug 3, 2016.
This post discusses some considerations, options, and opportunities for automating aspects of data science and machine learning. It is the second place recipient (tied) in the recent KDnuggets blog contest.
- 10 Algorithm Categories for A.I., 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.
- 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 Trends and the Future of Artificial Intelligence - Jun 22, 2016.
The confluence of data flywheels, the algorithm economy, and cloud-hosted intelligence means every company can now be a data company, every company can now access algorithmic intelligence, and every app can now be an intelligent app.
- A Visual Explanation of the Back Propagation Algorithm for Neural Networks - Jun 17, 2016.
A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.
- Figuring Out the Algorithms of Intelligence - Jun 15, 2016.
Marvin Minsky, the father of AI, passed away this year. One of his inventions was the confocal microscope, which we used to take this high-resolution picture of a live brain circuit. Something in these cells allows them to automatically identify useful connections and establish useful networks out of information.
- Deep Learning, Pachinko, and James Watt: Efficiency is the Driver of Uncertainty - Jun 8, 2016.
A reasoned discussion of why the next generation of data efficient learning approaches rely on us developing new algorithms that can propagate stochasticity or uncertainty right through the model, and which are mathematically more involved than the standard approaches.
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- Data Science of Variable Selection: A Review - Jun 7, 2016.
There are as many approaches to selecting features as there are statisticians since every statistician and their sibling has a POV or a paper on the subject. This is an overview of some of these approaches.
- The Truth About Deep Learning - Jun 6, 2016.
An honest look at deep learning, what it is not, its advantages over "shallow" neural networks, and some of the common assumptions and conflations that surround it.
- KDnuggets™ News 16:n17, May 11: Machine Learning Algorithms From Scratch; Big Data Leaders on LinkedIn; Data Science Career - May 11, 2016.
Why Implement Machine Learning Algorithms From Scratch? Meet the 11 Big Data, Data Science Leaders on LinkedIn; Free Advice For Building Your Data Science Career; 10 Essential Books for Data Enthusiast.
- Why Implement Machine Learning Algorithms From Scratch? - May 6, 2016.
Even with machine learning libraries covering almost any algorithm implementation you could imagine, there are often still good reasons to write your own. Read on to find out what these reasons are.
- 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.
- Datasets Over Algorithms - May 3, 2016.
The average elapsed time between key algorithm proposals and corresponding advances is about 18 years; the average elapsed time between key dataset availabilities and corresponding advances is less than 3 years, 6 times faster.
- Metrics Gone Wrong – How Companies Are Optimizing The Wrong Way - Apr 20, 2016.
A critique of the over-abundant and misguided pursuit of metric completeness, and how it can result in incorrect "optimization."
- Basics of GPU Computing for Data Scientists - Apr 7, 2016.
With the rise of neural network in data science, the demand for computationally extensive machines lead to GPUs. Learn how you can get started with GPUs & algorithms which could leverage them.
- If Hollywood Made Movies About Machine Learning Algorithms - Apr 1, 2016.
A lighthearted take on the kind of movie Hollywood would produce if it took on machine learning algorithms.
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- KDnuggets™ News 16:n08, Mar 2: Citizen Data Scientist Mirage; Spark Tipping Point; 80% Machine Learning - Mar 2, 2016.
The Mirage of a Citizen Data Scientist; Why Spark Reached the Tipping Point in 2015; The Machine Learning Problem of The Next Decade; How The Algorithm Economy And Containers Are Changing The Apps.
- How The Algorithm Economy And Containers Are Changing The Apps - Feb 29, 2016.
Algorithmic Intelligence has been a driving force for many today’s technology companies. Understand how these organisations are using algorithms and container services for creating value from data.
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- The Art of Data Science: The Skills You Need and How to Get Them - Dec 28, 2015.
Learn, how to turn the deluge of data into the gold by algorithms, feature engineering, reasoning out business value and ultimately building a data driven organization.
- Top KDnuggets tweets, Dec 14-20: DeepLearning in a Nutshell: History and Training; Top 10 #MachineLearning Algorithms, updated - Dec 21, 2015.
Top 10 #MachineLearning Algorithms, updated; Cartoon: Surprise #DataScience #Recommendations; DeepLearning in a Nutshell: History and Training; Update: Google #TensorFlow #DeepLearning Is Improving.
- The Master Algorithm – new book by top Machine Learning researcher Pedro Domingos - Sep 25, 2015.
Wonderfully erudite, humorous, and easy to read, the Master Algorithm by top Machine Learning researcher Pedro Domingos takes you on a journey to visit the 5 tribes of Machine Learning experts and helps you understand what the Master Algorithm can be.
- Interview: Stefan Groschupf, Datameer on Why Domain Expertise is More Important than Algorithms - Aug 6, 2015.
We discuss large-scale data architectures in 2020, career path, open source involvement, advice, and more.
- TheWalnut.io: An Easy Way to Create Algorithm Visualizations - Jul 29, 2015.
Google's DeepDream project has gone viral which allows to visualize the deep learning neural networks. It highlights a need for a generalized algorithm visualization tool, in this post we introduce to you one such effort.
- SAS: Machine Learning Algorithm Research/Developer - Jul 15, 2015.
Developer with a strong analytical background and excellent programming skills to collaborate with a team developing new machine learning algorithms for NLP, text classification, sentiment analysis, and similar tasks.
- Top 10 Data Mining Algorithms, Explained - May 21, 2015.
Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.
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- Year in Review: Top KDnuggets tweets in October - Dec 29, 2014.
Data mining classics: Classifying Shakespearean Drama; Air traffic data is being analyzed to predict Ebola Spread; A Great Collection of #MachineLearning Algorithms; Best Programming Language for Machine Learning.
- Companion Website for “Data Mining and Analysis: Fundamental Concepts and Algorithms” - Nov 19, 2014.
Supplementary materials for the textbook Data Mining and Analysis: Fundamental Concepts and Algorithms are now available online and include figures, slides, datasets, videos, and more. Download them today.
- Advanced Data Analytics for Business Leaders Explained - Sep 24, 2014.
A business-level explanation of most important data analytics and machine learning methods, including neural networks, deep learning, clustering, ensemble methods, SVM, and when do use what models.
- 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.
- Sibyl: Google’s system for Large Scale Machine Learning - Aug 20, 2014.
A review of 2014 keynote talk about Sibyl, Google system for large scale machine learning. Parallel Boosting algorithm and several design principles are introduced.
- ASE International Conference on Big Data Science 2014: Day 4 Highlights - Aug 8, 2014.
Highlights from the presentations by Data Science leaders from UC Berkeley, Clark Atlanta Univ, Florida Institute of Technology, Rober Bosh LLC and HP on day 4 of ASE Conference on Big Data Science 2014, Stanford.
- Book: Data Classification: Algorithms and Applications - Aug 2, 2014.
Learn a wide variety of data classification techniques and their methods, domains, and variations in this comprehensive survey of the area of data classification.
- Interview: Thomas Levi, POF on How Online Dating is Improving Matching through Big Data - Jul 29, 2014.
We discuss Big Data use cases at Plenty of Fish, insights from text mining of user profiles, using topic modeling for developing user archetypes, challenges and more.
- Adobe: Manager – Algorithms / Machine Learning, 30834 - Jul 29, 2014.
Lead the Algorithms and Data Sciences group, help build the next generation of products that will allow digital marketers to maximize revenue and expand their brand presence.
- Interview: Cliff Lyon, Stubhub on Mastering the Art of Recommendation and Personalization Analytics - Jul 18, 2014.
We discuss challenges in designing recommendation and personalization systems, how to select the right metrics, and learning regarding presentation of recommendation on different channels.
- Book: Data Classification: Algorithms and Applications - Jun 14, 2014.
This new book explores the underlying algorithms of classification and applications in text, multimedia, social network, biological data, and other domains. 25% off with KDnuggets discount.
- The Algorithm that Runs the World Can Now Run More of It - Jun 13, 2014.
The most important algorithm, used for optimizing almost everything, is linear programming. New advances allow linear programming problems to be solved faster using the new commercial parallel simplex solver.
- Top stories for May 25-31 - Jun 1, 2014.
New Poll: Analytics, Data Mining, Data Science Software Used? Where to Learn Deep Learning - Courses, Tutorials, Software; Interview: Martin Hack, CEO, Skytree on Industrializing Machine Learning for Big Data; Data Mining and Analysis: Fundamental Concepts and Algorithms.
- Top KDnuggets tweets, May 26-27 - May 28, 2014.
Machine Learning Algorithms Tour: Regression, kNN, Regularization, Decision Tree; Where to Learn Deep Learning - Courses, Tutorials, Software; 9 Courses on Data Science, R, Machine Learning start on Coursera.
- Book: Data Mining and Analysis: Fundamental Concepts and Algorithms - May 27, 2014.
This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. Companion website has data, slides and other teaching material.
- Human Dynamics – Data Mining Mobile Phone Usage - May 6, 2014.
Mobile phone usage contains a gold mine of insights. We examine what was learned about human social connections from the first-ever extensive study of social interactions in Mexico.
- 9 Free Books for Learning Data Mining and Data Analysis - Apr 29, 2014.
Whether you are learning data science for the first time or refreshing your memory or catching up on latest trends, these free books will help you excel through self-study.
- MMDS 2014: Workshop on Algorithms for Modern Massive Data Sets, Berkeley, June 2014 - Mar 25, 2014.
The MMDS 2014 will address algorithmic, mathematical, and statistical challenges in modern statistical data analysis. Registration is open and you can apply to present a poster.