Machine Learning (1326)

• Top R Packages for Machine Learning - Feb 3, 2017.
  
  What are the most popular ML packages? Let's look at a ranking based on package downloads and social website activity.

  Machine Learning, R, R Packages

• Learning to Learn by Gradient Descent by Gradient Descent - Feb 2, 2017.
  
  What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!

  Gradient Descent, Machine Learning, NIPS, Optimization

• Is Deep Learning the Silver Bullet? - Feb 1, 2017.
  
  With nearly every every smart young computer scientist planning to work on deep learning, are there really still artificial intelligence researchers working on other techniques? Is deep learning the AI silver bullet?

  AI, Deep Learning, Machine Learning

• 6 areas of AI and Machine Learning to watch closely - Jan 25, 2017.
  
  Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.

  AI, Deep Neural Network, Generative Adversarial Network, Machine Learning, Reinforcement Learning

• 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.

  Algorithms, Machine Learning, Programming, Python

• The Top Predictive Analytics Pitfalls to Avoid - Jan 23, 2017.
  
  Predictive modelling and machine learning are significantly contributing to business, but they can be very sensitive to data and changes in it, which makes it very important to use proper techniques and avoid pitfalls in building data science models.

  Bias, Machine Learning, Model Performance, Predictive Analytics, Regularization, Statistics

• The Data Science Puzzle, Revisited - Jan 20, 2017.
  
  The data science puzzle is re-examined through the relationship between several key concepts in the realm, and incorporates important updates and observations from the past year. The result is a modified explanatory graphic and rationale.

  AI, Big Data, Data Mining, Data Science, Deep Learning, Machine Learning

• Data Science of Sales Calls: 3 Actionable Findings - Jan 19, 2017.
  
  How does AI help sales and marketing teams in the organisation? Let’s understand Dos and don’ts of sales calls with the help of analysis of over 70,000+ B2B SaaS sales calls.

  AI, Gong.io, Machine Learning, Sales, Speech Recognition

• The Current State of Automated Machine Learning - Jan 18, 2017.
  
  What is automated machine learning (AutoML)? Why do we need it? What are some of the AutoML tools that are available? What does its future hold? Read this article for answers to these and other AutoML questions.

  Automated, Automated Data Science, Automated Machine Learning, Hyperparameter, Machine Learning

• 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.

  Algorithms, Big Data, Data, Datasets, Machine Learning

• 90 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning (updated) - Jan 17, 2017.
  
  Stay up-to-date in the data science with active blogs. This is a list of 90 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  Pages: 1 2

  Big Data, Blogs, Data Mining, Data Science, Machine Learning

• The Most Popular Language For Machine Learning and Data Science Is … - Jan 11, 2017.
  
  When it comes to choosing programming language for Data Analytics projects or job prospects, people have different opinions depending on their career backgrounds and domains they worked in. Here is the analysis of data from indeed.com with respect to choice of programming language for machine learning and data science.

  Data Science, Machine Learning, Programming Languages, Python, R, Scala

• How To Stay Competitive In Machine Learning Business - Jan 4, 2017.
  
  To stay competitive in machine learning business, you have to be superior than your rivals and not the best possible – says one of the leading machine learning expert. Simple rules are defined here to make that happen. Let’s see how.

  Business, Business Analytics, Data Management, Machine Learning, Research

• Generative Adversarial Networks – Hot Topic in Machine Learning - Jan 3, 2017.
  
  What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.

  Deep Learning, Generative Adversarial Network, Machine Learning, Neural Networks, NIPS

• 3 methods to deal with outliers - Jan 3, 2017.
  
  In both statistics and machine learning, outlier detection is important for building an accurate model to get good results. Here three methods are discussed to detect outliers or anomalous data instances.

  Machine Learning, Outliers, Statistics

• Ten Myths About Machine Learning, by Pedro Domingos - Jan 3, 2017.
  
  Myths on artificial intelligence and machine learning abound. Noted expert Pedro Domingos identifies and refutes a number of these myths, of both the pessimistic and optimistic variety.

  Machine Learning, Myths, Pedro Domingos

• Machine Learning and Cyber Security Resources - Jan 2, 2017.
  
  An overview of useful resources about applications of machine learning and data mining in cyber security, including important websites, papers, books, tutorials, courses, and more.
   
   

  Cybersecurity, Machine Learning, Security

• 5 Machine Learning Projects You Can No Longer Overlook, January - Jan 2, 2017.
  
  There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
   
   

  Boosting, C++, Data Preparation, Decision Trees, Machine Learning, Neural Networks, Optimization, Overlook, Pandas, Python, scikit-learn

• Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017 - Dec 20, 2016.
  
  As 2016 comes to a close and we prepare for a new year, check out the final instalment in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.

  2017 Predictions, AI, Artificial Intelligence, Machine Learning, Predictions

• 50+ Data Science, Machine Learning Cheat Sheets, updated - Dec 14, 2016.
  
  Gear up to speed and have concepts and commands handy in Data Science, Data Mining, and Machine learning algorithms with these cheat sheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark, Matlab, and Java.

  Cheat Sheet, Data Science, Django, Hadoop, Java, Machine Learning, MATLAB, Python, R

• arXiv Paper Spotlight: Why Does Deep and Cheap Learning Work So Well? - Dec 13, 2016.
  
  The recent paper at hand approaches explaining deep learning from a different perspective, that of physics, and discusses the role of "cheap learning" (parameter reduction) and how it relates back to this innovative perspective.

  Academics, arXiv, Deep Learning, Machine Learning

• Free ebooks: Machine Learning with Python and Practical Data Analysis - Dec 5, 2016.
  
  Two free ebooks: "Building Machine Learning Systems with Python" and "Practical Data Analysis" will give your skills a boost and make a great start in the New Year.

  Data Analysis, Free ebook, Machine Learning, Packt Publishing, Python

• Why Deep Learning is Radically Different From Machine Learning - Dec 5, 2016.
  
  There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference?

  Deep Learning, Machine Learning

• Top 10 Amazon Books in Artificial Intelligence & Machine Learning, 2016 Edition - Nov 30, 2016.
  
  Given the ongoing explosion in interest for all things Data Science, Artificial Intelligence, Machine Learning, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the AI & Machine Learning category.

  AI, Amazon, Artificial Intelligence, Books, Machine Learning

• Machine Learning vs Statistics - Nov 29, 2016.
  
  Machine learning is all about predictions, supervised learning, and unsupervised learning, while statistics is about sample, population, and hypotheses. But are they actually that different?

  Machine Learning, Statistics

• 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.

  Pages: 1 2

  Beginners, Classification, Clustering, Machine Learning, Pandas, Python, R, scikit-learn, Software Developer

• Continuous improvement for IoT through AI / Continuous learning - Nov 25, 2016.
  
  In reality, especially for IoT, it is not like once an analytics model is built, it will give the results with same accuracy till the end of time. Data pattern changes over the time which makes it absolutely important to learn from new data and improve/recalibrate the models to get correct result. Below article explain this phenomenon of continuous improvement in analytics for IoT.

  AI, Deployment, IoT, Machine Learning, Model Performance, Realtime Analytics

• Deep Learning Research Review: Reinforcement Learning - Nov 25, 2016.
  
  This edition of Deep Learning Research Review explains recent research papers in Reinforcement Learning (RL). If you don't have the time to read the top papers yourself, or need an overview of RL in general, this post has you covered.

  Pages: 1 2

  Deep Learning, Machine Learning, Reinforcement Learning

• Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning - Nov 23, 2016.
  
  Social media now not only shares friendship connections or photos of “selfies” but also spreads from political media to science information. Social network members are tending to more eagerly learn about big data, data science and machine learning through groups. We review the ten largest Facebook groups in this area.

  Big Data, Data Science, Facebook, Machine Learning

• Top 20 Python Machine Learning Open Source Projects, updated - Nov 21, 2016.
  
  Open Source is the heart of innovation and rapid evolution of technologies, these days. This article presents you Top 20 Python Machine Learning Open Source Projects of 2016 along with very interesting insights and trends found during the analysis.

  GitHub, Machine Learning, Open Source, Python, scikit-learn

• Questions To Ask When Moving Machine Learning From Practice to Production - Nov 18, 2016.
  
  An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.

  Data Science, Deep Learning, Deployment, Machine Learning, Production

• Combining Different Methods to Create Advanced Time Series Prediction - Nov 16, 2016.
  
  The results from combining methods for time series prediction have been quite promising. However, the degree of error for long-term predictions is still quite high. Sounds like a challenge, so some new experiments are forthcoming!

  ARIMA, Data Science, Machine Learning, Prediction, Time Series

• An Intuitive Explanation of Convolutional Neural Networks - Nov 11, 2016.
  
  This article provides a easy to understand introduction to what convolutional neural networks are and how they work.

  Pages: 1 2 3

  Convolutional Neural Networks, Deep Learning, Explanation, Machine Learning, Neural Networks

• A Quick Introduction to Neural Networks - Nov 9, 2016.
  
  This article provides a beginner level introduction to multilayer perceptron and backpropagation.

  Pages: 1 2 3

  Backpropagation, Deep Learning, Machine Learning, Neural Networks

• How to Rank 10% in Your First Kaggle Competition - Nov 9, 2016.
  
  This post presents a pathway to achieving success in Kaggle competitions as a beginner. The path generalizes beyond competitions, however. Read on for insight into succeeding while approaching any data science project.

  Pages: 1 2 3 4

  Beginners, Competition, Data Science, Kaggle, Machine Learning, Python

• Agilience Top Artificial Intelligence, Machine Learning Authorities - Nov 7, 2016.
  
  Agilience developed a new way to find authorities in social media across many fields of interest. In previous post we reviewed the top authorities in Data Mining and Data science; in this post we review top authorities in Artificial Intelligence and Machine Learning which includes Vineet Vashishta, Kirk D. Borne, KDnuggets, James Kobielus, Kaggle and more.

  Pages: 1 2

  About KDnuggets, Agilience, AI, Artificial Intelligence, Influencers, Kaggle, Kirk D. Borne, Machine Learning

• Top /r/MachineLearning Posts, October: NSFW Image Recognition, Differentiable Neural Computers, Hinton on Coursera - Nov 4, 2016.
  
  NSFW Image Recognition, Differentiable Neural Computers, Hinton's Neural Networks for Machine Learning Coursera course; Introducing the AI Open Network; Making a Self-driving RC Car

  DeepMind, Geoff Hinton, Image Recognition, Machine Learning, Neural Networks, Reddit, Self-Driving Car

• How Can Lean Six Sigma Help Machine Learning? - Nov 1, 2016.
  
  The data cleansing phase alone is not sufficient to ensure the accuracy of the machine learning, when noise / bias exists in input data. The lean six sigma variance reduction can improve the accuracy of machine learning results.

  Data Cleaning, Machine Learning, Predictive Analytics, Statistics

• Machine Learning: A Complete and Detailed Overview - Oct 28, 2016.
  
  This is an overview (with links) to a 5-part series on introductory machine learning. The set of tutorials is comprehensive, yet succinct, covering many important topics in the field (and beyond).

  Machine Learning

• Automated Machine Learning: An Interview with Randy Olson, TPOT Lead Developer - Oct 28, 2016.
  
  Read an insightful interview with Randy Olson, Senior Data Scientist at University of Pennsylvania Institute for Biomedical Informatics, and lead developer of TPOT, an open source Python tool that intelligently automates the entire machine learning process.

  Automated Data Science, Automated Machine Learning, Machine Learning, Python, scikit-learn

• Learn Data Science in 8 (Easy) Steps - Oct 27, 2016.
  
  Want to learn data science? Check out these 8 (easy) steps to set out in the right direction!

  Pages: 1 2

  Big Data, Data Science, DataCamp, Machine Learning

• 5 EBooks to Read Before Getting into A Machine Learning Career - Oct 21, 2016.
  
  A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.

  Bayesian, Data Science, Deep Learning, Free ebook, Machine Learning, Reinforcement Learning

• A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18! - Oct 20, 2016.
  
  This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.

  Pages: 1 2

  Beginners, Machine Learning, Neural Networks, Python, scikit-learn

• 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.

  Classification, Database, Machine Learning, TensorFlow, Transfer Learning

• Top /r/MachineLearning Posts, September: Open Images Dataset; Whopping Deep Learning Grant; Advanced ML Courseware - Oct 7, 2016.
  
  Google Research announces the Open Images dataset; Canadian Government Deep Learning Research grant; DeepMind: WaveNet - A Generative Model for Raw Audio; Machine Learning in a Year - From total noob to using it at work; Phd-level machine learning courses; xkcd: Linear Regression

  Canada, Courses, Deep Learning, Generative Models, Geoff Hinton, Machine Learning, Reddit, xkcd

• Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team - Oct 4, 2016.
  
  This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.

  Automated, Automated Data Science, Automated Machine Learning, Competition, Machine Learning, scikit-learn

• Deep Learning Reading Group: SqueezeNet - Sep 29, 2016.
  
  This paper introduces a small CNN architecture called “SqueezeNet” that achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.

  Compression, Deep Learning, Lab41, Machine Learning, Neural Networks

• Data Science of Sales Calls: The Surprising Words That Signal Trouble or Success - Sep 29, 2016.
  
  While not as profound a problem as uncovering the secrets of the universe, how to conduct a successful sales conversation is an age-old problem, impacting millions of people every day.

  Gong.io, Machine Learning, Sales, Speech Recognition

• 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.

  Algorithms, Clustering, K-means, Machine Learning

• Deep Learning Reading Group: Deep Residual Learning for Image Recognition - Sep 22, 2016.
  
  Published in 2015, today's paper offers a new architecture for Convolution Networks, one which has since become a staple in neural network implementation. Read all about it here.

  Academics, Convolutional Neural Networks, Deep Learning, Image Recognition, Lab41, Machine Learning, Neural Networks

• 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.

  Algorithms, Machine Learning, Support Vector Machines

• 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!

  Algorithms, Clustering, Decision Trees, K-nearest neighbors, Machine Learning, PCA, Poll, random forests algorithm, Regression, Statistics, Text Mining, Time Series, Visualization

• Urban Sound Classification with Neural Networks in Tensorflow - Sep 12, 2016.
  
  This post discuss techniques of feature extraction from sound in Python using open source library Librosa and implements a Neural Network in Tensorflow to categories urban sounds, including car horns, children playing, dogs bark, and more.

  Pages: 1 2

  Deep Learning, Feature Extraction, Machine Learning, Neural Networks, TensorFlow

• 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.

  Algorithms, Classifier, Explanation, Interpretability, LIME, Machine Learning, Prediction

• The Gentlest Introduction to Tensorflow – Part 2 - Aug 19, 2016.
  
  Check out the second and final part of this introductory tutorial to TensorFlow.

  Pages: 1 2

  Beginners, Deep Learning, Gradient Descent, Machine Learning, TensorFlow

• Top Machine Learning Projects for Julia - Aug 19, 2016.
  
  Julia is gaining traction as a legitimate alternative programming language for analytics tasks. Learn more about these 5 machine learning related projects.

  Deep Learning, Julia, Machine Learning, Open Source, scikit-learn

• 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.

  Pages: 1 2

  Algorithms, Machine Learning, Supervised Learning, Unsupervised Learning

• Approaching (Almost) Any Machine Learning Problem - Aug 18, 2016.
  
  If you're looking for an overview of how to approach (almost) any machine learning problem, this is a good place to start. Read on as a Kaggle competition veteran shares his pipelines and approach to problem-solving.

  Pages: 1 2

  Advice, Feature Selection, Kaggle, Machine Learning, Modeling

• Contest 2nd Place: Automated Data Science and Machine Learning in Digital Advertising - Aug 4, 2016.
  
  This post is an overview of an automated machine learning system in the digital advertising realm. It is an entrant and second-place recipient in the recent KDnuggets blog contest.

  Advertising, Automated, Automated Data Science, Automated Machine Learning, Claudia Perlich, Machine Learning

• 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.

  Algorithms, Automated, Automated Data Science, Feature Selection, Machine Learning

• What Statistics Topics are Needed for Excelling at Data Science? - Aug 2, 2016.
  
  Here is a list of skills and statistical concepts suggested for excelling at data science, roughly in order of increasing complexity.

  Bayesian, Distribution, Machine Learning, Markov Chains, Probability, Regression, Statistics

• And the Winner is… Stepwise Regression - Aug 1, 2016.
  
  This post evaluates several methods for automating the feature selection process in large-scale linear regression models and show that for marketing applications the winner is Stepwise regression.

  Automated Data Science, Feature Selection, Linear Regression, Machine Learning, Predictive Analytics

• Dataiku DSS 3.1 – Now with 5 ML Backends & Scala! - Aug 1, 2016.
  
  Introducing Dataiku DSS 3.1, with new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface.

  Data Science, Dataiku, Machine Learning, Scala

• 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.

  Pages: 1 2

  Machine Learning, Python, Titanic

• 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.

  Machine Learning, Python, scikit-learn, Titanic

• 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!

  Pages: 1 2

  Advice, Career, Data Science, Data Scientist, Dataquest, Machine Learning, Portfolio, Project, Python

• 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.

  Pages: 1 2

  Machine Learning, Neural Networks, TensorFlow

• 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.
   
   

  Bayesian, Explained, LDA, Machine Learning

• 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.

  C++, Deep Learning, Javascript, Machine Learning, Neural Networks, Overlook, Python

• 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

  AI, Machine Learning, Optimization, Reinforcement Learning, Supervised Learning

• 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.

  Andrew Ng, Coursera, Deep Learning, edX, Machine Learning, MOOC, Nando de Freitas, Tom Mitchell, Udacity

• 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.

  Aylien, Explanation, Machine Learning, Support Vector Machines

• 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.

  Logistic Regression, Machine Learning, Regression

• 5 More Machine Learning Projects You Can No Longer Overlook - Jun 28, 2016.
  
  There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects.

  Computer Vision, Data Preparation, Data Preprocessing, Javascript, Machine Learning, Natural Language Processing, NLP, Overlook, Python

• Regularization in Logistic Regression: Better Fit and Better Generalization? - Jun 24, 2016.
  
  A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.

  Cost Function, Logistic Regression, Machine Learning, Regression, Regularization

• Top Machine Learning Libraries for Javascript - Jun 24, 2016.
  
  Javascript may not be the conventional choice for machine learning, but there is no reason it cannot be used for such tasks. Here are the top libraries to facilitate machine learning in Javascript.

  Andrej Karpathy, Convolutional Neural Networks, Deep Learning, Javascript, Machine Learning, Neural Networks

• 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.

  Algorithmia, Algorithms, Artificial Intelligence, Cloud, Machine Intelligence, Machine Learning

• New Andrew Ng Machine Learning Book Under Construction, Free Draft Chapters - Jun 20, 2016.
  
  Check out the details on Andrew Ng's new book on building machine learning systems, and find out how to get your free copy of draft chapters as they are written.

  Andrew Ng, Book, Free ebook, Machine Learning

• 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.

  Algorithms, Backpropagation, Explanation, Machine Learning, Neural Networks

• Machine Learning Classic: Parsimonious Binary Classification Trees - Jun 14, 2016.
  
  Get your hands on a classic technical report outlining a three-step method of construction binary decision trees for multiple classification problems.

  Decision Trees, Leo Breiman, Machine Learning, Statistics

• How to Select Support Vector Machine Kernels - Jun 13, 2016.
  
  Support Vector Machine kernel selection can be tricky, and is dataset dependent. Here is some advice on how to proceed in the kernel selection process.

  Machine Learning, Support Vector Machines

• AIG & Zurich on Machine Learning in Insurance - Jun 10, 2016.
  
  Where and how can machine learning be practically applied by insurers? And is it worth it? Read the white paper from insurance experts at AIG and Zurich.

  AIG, Insurance, Machine Learning, White Paper

• Where are the Opportunities for Machine Learning Startups? - Jun 8, 2016.
  
  Machine learning has permeated data-driven businesses, which means almost all businesses. Here are a few areas where it’s possible that big corporations haven’t already eaten everybody’s lunch.

  Machine Learning, Startup

• Ethics in Machine Learning – Summary - Jun 6, 2016.
  
  Still worried about the AI apocalypse? Here we are discussion about the constraints and ethics for the machine learning algorithms to prevent it.

  AI, Ethics, Machine Learning, MLconf, Seattle, WA

• 5 Reasons Machine Learning Applications Need a Better Lambda Architecture - Jun 2, 2016.
  
  The Lambda Architecture enables a continuous processing of real-time data. It is a painful process that gets the job done, but at a great cost. Here is a simplified solution called as Lambda-R (ƛ-R) for the Relational Lambda.

  Applications, Lambda Architecture, Machine Learning, Monte Zweben, Splice Machine

• Udacity Nanodegree Programs: Machine Learning, Data Analyst, and more - Jun 1, 2016.
  
  Develop new skills. Be in demand. Accelerate your career with the credential that fast-tracks you to career success.

  Machine Learning, Online Education, Udacity

• Top 10 Open Dataset Resources on Github - May 31, 2016.
  
  The top open dataset repositories on Github include a variety of data, freely available for use by researchers, practitioners, and students alike.

  Datasets, GitHub, Machine Learning, Open Data

• A Concise Overview of Standard Model-fitting Methods - May 27, 2016.
  
  A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.

  Pages: 1 2

  Cost Function, Gradient Descent, Machine Learning, Sebastian Raschka

• How to Explain Machine Learning to a Software Engineer - May 20, 2016.
  
  How do you explain what machine learning is to the uninitiated software engineer? Read on for one perspective on doing so.

  Automating, Machine Learning, Software Engineer

• 5 Machine Learning Projects You Can No Longer Overlook - May 19, 2016.
  
  We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.

  Data Cleaning, Deep Learning, Machine Learning, Open Source, Overlook, Pandas, Python, scikit-learn, Theano

• 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.

  Algorithms, Machine Learning

• Machine Learning for Artists – Video lectures and notes - Apr 28, 2016.
  
  Art has always been deep for those who appreciate it... but now, more than ever, deep learning is making a real impact on the art world. Check out this graduate course, and its freely-available resources, focusing on this very topic.

  Art, Convolutional Neural Networks, Deep Learning, Machine Learning, Recurrent Neural Networks

• Microsoft is Becoming M(ai)crosoft - Apr 25, 2016.
  
  This post is an overview and discussion of Microsoft's increasing investment in, and approach to, artificial intelligence, and how their philosophy differs from their competitors.

  AI, Artificial Intelligence, Computer Vision, Cortana, Machine Learning, Microsoft, Natural Language Processing, Speech Recognition

• Top 10 IPython Notebook Tutorials for Data Science and Machine Learning - Apr 22, 2016.
  
  A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.

  Data Science, Deep Learning, GitHub, IPython, Machine Learning, Python, Sebastian Raschka, TensorFlow

• Top 15 Frameworks for Machine Learning Experts - Apr 19, 2016.
  
  Either you are a researcher, start-up or big organization who wants to use machine learning, you will need the right tools to make it happen. Here is a list of the most popular frameworks for machine learning.

  Data Science Tools, Deep Learning, Devendra Desale, Machine Learning, MLlib

• Using Big Data Analytics To Prevent Crimes The “Minority Report” Way - Apr 18, 2016.
  
  The idea of using artificial intelligence for the crime prevention has been around for more than a decade. In this post, we present four examples, including how using analytics, we can prevent a criminal from re-offending.

  Big Data Analytics, Crime, Machine Learning, Surveillance

• What Developers Actually Need to Know About Machine Learning - Apr 14, 2016.
  
  Some guidance on what, exactly, it is that developers need to know to get up to speed with machine learning.

  Advice, Developers, Machine Learning

• 100 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning - Mar 29, 2016.
  
  Stay on top of your data science skills game! Here’s a list of about 100 most active and interesting blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  Pages: 1 2

  Big Data, Blogs, Data Science, Deep Learning, Hadoop, Machine Learning

• Don’t Buy Machine Learning - Mar 28, 2016.
  
  In many projects, the amount of effort spent on R&D on Machine Learning is usually a small fraction of the total effort, or it’s not even there because we plan it for a future phase after building the application first.

  Advice, Industry, Machine Learning

• The Data Science Puzzle, Explained - Mar 10, 2016.
  
  The puzzle of data science is examined through the relationship between several key concepts in the data science realm. As we will see, far from being concrete concepts etched in stone, divergent opinions are inevitable; this is but another opinion to consider.

  Pages: 1 2

  Artificial Intelligence, Data Mining, Data Science, Deep Learning, Explained, Machine Learning

• AI and Machine Learning: Top Influencers and Brands - Mar 8, 2016.
  
  Onalytica gives us a new list of the top 100 Artifical Intelligence and Machine Learning influencers and brands, and provides some insight into the relationships between them.

  About Gregory Piatetsky, AI, Influencers, Kirk D. Borne, Machine Learning, Onalytica, Top list

• scikit-feature: Open-Source Feature Selection Repository in Python - Mar 3, 2016.
  
  scikit-feature is an open-source feature selection repository in python, with around 40 popular algorithms in feature selection research. It is developed by Data Mining and Machine Learning Lab at Arizona State University.

  Data Mining, Data Science, Feature Extraction, Feature Selection, Machine Learning, Python

• 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.

  Amazon, Classification, Machine Learning, MLaaS

• Does Machine Learning allow opposites to attract? - Feb 11, 2016.
  
  Most online dating sites use 'Netflix-style' recommendations which match people based on their shared interests and likes. What about those matches that work so well because people are so different - here is my example.

  Love, Machine Learning, Online Dating, Recommendations

• Yahoo Releases the Largest-ever Machine Learning Dataset for Researchers - Jan 18, 2016.
  
  Are you interested in massive amounts of data for research? Yahoo has just released the largest-ever machine learning dataset to the research community.

  Anonymized, Dataset, Machine Learning, Yahoo

• 20 Questions to Detect Fake Data Scientists - Jan 1, 2016.
  
  Hiring Data Scientists is no easy job, particularly when there are plenty of fake posers. Here is a handy list of questions to help separate the wheat from the chaff.

  Data Scientist, Data Visualization, import.io, Kirk D. Borne, Machine Learning, Outliers

• 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.

  Pages: 1 2

  Classification, Clustering, Machine Learning, Regression

• Tour of Real-World Machine Learning Problems - Dec 26, 2015.
  
  The tour lists 20 interesting real-world machine learning problems for data science enthusiasts to learn by solving.

  Datasets, Kaggle, Learning from Data, Machine Learning, Research, UCI

• Top 10 Machine Learning Projects on Github - Dec 14, 2015.
  
  The top 10 machine learning projects on Github include a number of libraries, frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.

  Pages: 1 2

  GitHub, Machine Learning, Matthew Mayo, Open Source, scikit-learn, Top 10

• Beyond One-Hot: an exploration of categorical variables - Dec 8, 2015.
  
  Coding categorical variables into numbers, by assign an integer to each category ordinal coding of the machine learning algorithms. Here, we explore different ways of converting a categorical variable and their effects on the dimensionality of data.

  Data Exploration, Machine Learning, Python, Will McGinnis

• 50 Useful Machine Learning & Prediction APIs - Dec 7, 2015.
  
  We present a list of 50 APIs selected from areas like machine learning, prediction, text analytics & classification, face recognition, language translation etc. Start consuming APIs!

  Pages: 1 2

  API, Data Science, Face Recognition, IBM Watson, Image Recognition, Machine Learning, NLP, Sentiment Analysis

• 5 Tribes of Machine Learning – Questions and Answers - Nov 27, 2015.
  
  Leading researcher Pedro Domingos answers questions on 5 tribes of Machine Learning, Master Algorithm, No Free Lunch Theorem, Unsupervised Learning, Ensemble methods, 360-degree recommender, and more.

  Ensemble Methods, Machine Learning, Pedro Domingos, Recommender Systems

• Detecting In-App Purchase Fraud with Machine Learning - Nov 25, 2015.
  
  Hacking applications allow users to make in-app purchases for free. With help from a few big games in the GROW data network we were able to build a model that classifies each purchase as real or fraud, with a very high level of accuracy.

  Fraud Detection, Machine Learning, Online Games

• 7 Steps to Mastering Machine Learning With Python - Nov 19, 2015.
  
  There are many Python machine learning resources freely available online. Where to begin? How to proceed? Go from zero to Python machine learning hero in 7 steps!

  Pages: 1 2

  7 Steps, Anaconda, Caffe, Deep Learning, Machine Learning, Matthew Mayo, Python, scikit-learn, Theano

• What No One Tells You About Real-Time Machine Learning - Nov 9, 2015.
  
  Real-time machine learning has access to a continuous flow of transactional data, but what it really needs in order to be effective is a continuous flow of labeled transactional data, and accurate labeling introduces latency.

  Dmitry Petrov, Machine Learning, Real-time

• 5 Best Machine Learning APIs for Data Science - Nov 5, 2015.
  
  Machine Learning APIs make it easy for developers to develop predictive applications. Here we review 5 important Machine Learning APIs: IBM Watson, Microsoft Azure Machine Learning, Google Prediction API, Amazon Machine Learning API, and BigML.

  Pages: 1 2

  Amazon, API, Azure ML, BigML, DeZyre, Google, IBM Watson, Machine Learning

• The Best Advice From Quora on ‘How to Learn Machine Learning’ - Oct 15, 2015.
  
  Top machine learning writers on Quora give their advice on learning machine learning, including specific resources, quotes, and personal insights, along with some extra nuggets of information.

  Pages: 1 2

  Books, Machine Learning, Matthew Mayo, MOOC, Quora, Sean McClure, Xavier Amatriain

• Does Deep Learning Come from the Devil? - Oct 9, 2015.
  
  Deep learning has revolutionized computer vision and natural language processing. Yet the mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.

  Berlin, Deep Learning, Machine Learning, Support Vector Machines, SVM, Vladimir Vapnik, Yandex, Zachary Lipton

• Topological Analysis and Machine Learning: Friends or Enemies? - Sep 29, 2015.
  
  What is the interaction between Topological Data Analysis and Machine Learning ? A case study shows how TDA decomposition of the data space provides useful features for improving Machine Learning results.

  Ayasdi, Machine Learning, random forests algorithm, Topological Data Analysis

• 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.

  Algorithms, Book, Machine Learning, Pedro Domingos

• Top 10 Quora Machine Learning Writers and Their Best Advice - Sep 18, 2015.
  
  Top Quora machine learning writers give their advice on pursuing a career in the field, academic research, and selecting and using appropriate technologies.

  Machine Learning, Quora, random forests algorithm, Top 10, Xavier Amatriain, Yoshua Bengio

• 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more - Sep 4, 2015.
  
  Here is a great collection of eBooks written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data Science Tools, and Programming Languages for Data Science.

  Book, Brendan Martin, Data Mining, Data Science, Free ebook, Machine Learning, Python, R, SQL

• Gartner 2015 Hype Cycle: Big Data is Out, Machine Learning is in - Aug 28, 2015.
  
  Which are the most hyped technologies today? Check out Gartner's latest 2015 Hype Cycle Report. Autonomous cars & IoT stay at the peak while big data is losing its prominence. Smart Dust is a new cool technology for the next decade!

  Big Data, Citizen Data Scientist, Gartner, Machine Learning

• Recycling Deep Learning Models with Transfer Learning - Aug 14, 2015.
  
  Deep learning exploits gigantic datasets to produce powerful models. But what can we do when our datasets are comparatively small? Transfer learning by fine-tuning deep nets offers a way to leverage existing datasets to perform well on new tasks.

  Deep Learning, Image Recognition, ImageNet, Machine Learning, Neural Networks, Transfer Learning, Zachary Lipton

• Three Essential Components of a Successful Data Science Team - Aug 10, 2015.
  
  A Data Science team, carefully constructed with the right set of dedicated professionals, can prove to be an asset to any organization,

  Business Analyst, Data Engineer, Data Science Team, Machine Learning, Team

• 50+ Data Science and Machine Learning Cheat Sheets - Jul 14, 2015.
  
  Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms.

  Cheat Sheet, Data Science, Django, Hadoop, Machine Learning, Python, R

• Can deep learning help find the perfect date? - Jul 10, 2015.
  
  When a Machine Learning PhD student at University of Montreal starts using Tinder, he soon realises that something is missing in the dating app - the ability to predict to which girls he is attracted. Harm de Vries applies Deep Learning to assist in the pursuit of the perfect match.

  Deep Learning, ICML, Love, Machine Learning, Online Dating, Predictive Analytics

• Top 20 R Machine Learning and Data Science packages - Jun 24, 2015.
  
  We list out the top 20 popular Machine Learning R packages by analysing the most downloaded R packages from Jan-May 2015.

  CRAN, Data Science, Machine Learning, R, R Packages, Top list

• Top 10 Machine Learning Videos on YouTube - Jun 23, 2015.
  
  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.

  Andrew Ng, Computer Vision, Deep Learning, Geoff Hinton, Google, Grant Marshall, Machine Learning, Neural Networks, Robots, Video Games, Youtube

• In Machine Learning, What is Better: More Data or better Algorithms - Jun 17, 2015.
  
  Gross over-generalization of “more data gives better results” is misguiding. Here we explain, in which scenario more data or more features are helpful and which are not. Also, how the choice of the algorithm affects the end result.

  Big Data Hype, Data Quality, IMDb, Machine Learning, Quora, Xavier Amatriain