- Learning From Data (Introductory Machine Learning) Caltech course starts on edX Sep 18 - Sep 17, 2016.
This introductory Machine Learning course taught by top Caltech professor Abu-Mostafa covers theory, algorithms and applications, with focus on real understanding. Starts Sep 18, 2016 on edX.
- The Deception of Supervised Learning - Sep 13, 2016.
Do models or offline datasets ever really tell us what to do? Most application of supervised learning is predicated on this deception.
- 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.
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- Up to Speed on Deep Learning: July Update, Part 2 - Sep 7, 2016.
Check out this second installation of deep learning stories that made news in July. See if there are any items of note you missed.
- Top /r/MachineLearning Posts, August: Google Brain AMA, Image Completion with TensorFlow, Japanese Cucumber Farming - Sep 5, 2016.
Google Brain AMA; Image Completion with Deep Learning in TensorFlow; Japanese Cucumber Farming; Andrew Ng's machine learning class in Python; Google Brain datasets for robotics research
- Booking: Data Scientist – Machine Learning - Aug 31, 2016.
Booking.com is looking for rock star Data Scientists to add to join their highly successful Personalization Team, crunching data and providing customers with the most relevant personalized recommendations.
- Hitachi: Research Scientist, Machine Learning - Aug 30, 2016.
Hitachi is seeking a Research Scientist in the Big Data Laboratory located in Silicon Valley, with a mission of helping create new and innovative solutions in big data and advanced analytics.
- PAPIs 16 Conference on Predictive Applications & APIs, Oct 10-12, Boston - Aug 30, 2016.
PAPIs is the premier forum for the presentation of new machine learning APIs, techniques, architectures and tools to build intelligent applications. It also hosts the world’s 1st startup competition where the jury is an AI.
- Up to Speed on Deep Learning: July Update - Aug 29, 2016.
Check out this thorough roundup of deep learning stories that made news in July. See if there are any items of note you missed.
- 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.
- Is “Artificial Intelligence” Dead? Long Live Deep Learning?!? - Aug 26, 2016.
Has Deep Learning become synonymous with Artificial Intelligence? Read a discussion on the topic fuelled by the opinions of 7 participating experts, and gain some additional insight into the future of research and technology.
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- 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.
- Top KDnuggets tweets, Aug 17-23: Approaching (Almost) Any #MachineLearning Problem; #Database Nirvana – can one query language rule them all? - Aug 24, 2016.
In Search of #Database Nirvana - can one query language rule them all? Google Cloud Datalab: #Jupyter meets #TensorFlow, #cloud meets local deployment; Approaching (Almost) Any #MachineLearning Problem; The Gentlest Introduction to Tensorflow Part 1.
- 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 Gentlest Introduction to Tensorflow – Part 2 - Aug 19, 2016.
Check out the second and final part of this introductory tutorial to TensorFlow.
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- 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.
- 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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- 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.
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- The Gentlest Introduction to Tensorflow – Part 1 - Aug 17, 2016.
In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.
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- Tales from ICML: Insights and Takeaways - Aug 15, 2016.
The dust has settled from ICML 2016, having been held in June in NYC. Read some perspective on what was offered at the conference and relevant takeaways from a reflective attendee.
- Stop Blaming Terminator for Bad AI Journalism - Aug 11, 2016.
Too often, we blame The Terminator for the public's misconceptions concerning machine learning. But do James Cameron and the Austrian Oak stand wrongfully accused?
- 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.
- 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.
- 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.
- Top /r/MachineLearning Posts, July: Google Brain AMA, Geoff Hinton Awarded IEEE Medal, Hinton ANN Course Lives! - Aug 2, 2016.
Google Brain AMA; Geoff Hinton Awarded IEEE Medal; Geoff Hinton's ANN Course Lives; Google’s DeepMind Reduces Data Center Cooling Bill; Training an artificial neural network to play Diablo 2
- 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.
- 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.
- 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.
- 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.
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- 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.
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- 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.
- 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.
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- 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.
- 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.
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- 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!
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- 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.
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- KDnuggets™ News 16:n26, Jul 20: Bayesian Machine Learning, Explained; Start Learning Deep Learning; Big Data is in Trouble - Jul 20, 2016.
Bayesian Machine Learning, Explained; How to Start Learning Deep Learning; Why Big Data is in Trouble: They Forgot About Applied Statistics; Data Mining/Data Science "Nobel Prize": 2016 SIGKDD Innovation Award to Philip S. Yu
- 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.
- KDnuggets™ News 16:n25, Jul 13: Top Machine Learning MOOCs; 5 Deep Learning Projects; Support Vector Machines Overview - Jul 13, 2016.
Top Machine Learning MOOCs and Online Lectures: A Comprehensive Overview; Support Vector Machines: A Simple Explanation; 5 Deep Learning Projects You Can No Longer Overlook; Why You Should Attend the Data Science Summit 2016 and 9 Talks To Be Excited About
- 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.
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- 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.
- 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.
- 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.
- 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.
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- 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.
- 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.
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- 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!
- 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.
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- 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.
- 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.
- Doing Data Science: A Kaggle Walkthrough Part 6 – Creating a Model - Jun 24, 2016.
In the final part of this 6 part series on the process of data science, and applying it to a Kaggle competition, building the predictive models is covered, and multiple algorithms are discussed.
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- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Open Source Machine Learning Degree - Jun 6, 2016.
A set of free resources for learning machine learning, inspired by similar open source degree resources. Find links to books and book-length lecture notes for study.
- 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.
- 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.
- 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.
- Top /r/MachineLearning Posts, May: TensorFlow Tricks; Machine Learning Tutorials; Google TPUs - Jun 1, 2016.
May on /r/MachineLearning was all about tutorials, TensorFlow, Google hardware, Deep Learning machine installations, and some laughs.
- 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.
- Interacting with Machine Learning – Here is Why You Should Care - May 30, 2016.
The issue of designing new interactive interfaces with machine learning systems that best serve our needs and help us build and maintain trust is a central issue in AI. Read one researcher's take on this topic.
- 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.
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- Top KDnuggets tweets, May 18-24: Google supercharges #MachineLearning, #DeepLearning tasks with TPU (Tensor Processing Unit) - May 25, 2016.
Stanford Crowd Course Initiative: #MachineLearning with #Python course; Practical Guide to Matrix Calculus for #DeepLearning; Build your own #DeepLearning Box < $1.5K
- 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:n19, May 25: Explain Machine Learning to Software Engineer; 5 Can’t Miss Machine Learning Projects - May 25, 2016.
How to Explain Machine Learning to a Software Engineer; 5 Machine Learning Projects You Can No Longer Overlook; Doing Data Science: A Kaggle Walkthrough Part 1 - Introduction; The Amazing Power of Word Vectors
- 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.
- Doing Data Science: A Kaggle Walkthrough Part 1 – Introduction - May 19, 2016.
This is the first post in a fantastic 6 part series covering the process of data science, and the application of the process to a Kaggle competition. Very thorough, and very insightful.
- 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.
- An Introduction to Semi-supervised Reinforcement Learning - May 17, 2016.
A great overview of semi-supervised reinforcement learning, including general discussion and implementation information.
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- The Good, The Bad, and The Deep Algorithms… at MLconf Seattle, May 20 - May 13, 2016.
MLconf in Seattle is a week away and we are getting a glimpse. Ethics in machine learning is the hottest conversation right now. Hear how a quantum molecular dynamic model made Uber service more reliable, get practical advice on next revolution in text search, and learn about multi-classification evaluation and ensemble learning.
- TPOT: A Python Tool for Automating Data Science - May 13, 2016.
TPOT is an open-source Python data science automation tool, which operates by optimizing a series of feature preprocessors and models, in order to maximize cross-validation accuracy on data sets.
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- How Bing Predicts is forecasting everything from sports to political outcomes - May 13, 2016.
Bing Predicts is an innovative feature which now regularly makes headlines for its ability to analyze massive amounts of Web activity to forecast the outcomes of elections, voting-based reality TV shows, sports matchups and more.
- Top KDnuggets tweets, May 4-10: Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages - May 11, 2016.
Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages; Why AI development is going to get even faster; Why Implement #MachineLearning Algorithms From Scratch?
- 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.
- From Insight-as-a-Service to Insightful Applications - May 5, 2016.
Applications that combine machine learning, AI, and domain knowledge have strong potential for industry and investors.
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- Top /r/MachineLearning Posts, April: New Google Machine Learning Videos, Deep Learning Book, TensorFlow Playground - May 2, 2016.
Check out the most popular topics on Reddit's Machine Learning subreddit from April, including TensorFlow, deep learning, tutorials, self-reflection, and free books.
- Positioning a Machine Learning Company - Apr 28, 2016.
The classic guide for entrepreneurs preparing a pitch is Sequoia’s Business Plan Template. This post aims to be a mere addendum to that in the age of 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.
- Top Data Science Courses on Udemy - Apr 27, 2016.
An overview of the very best that Udemy has to offer in data science education. Includes courses covering machine learning, Python, Hadoop, visualization, and more.
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- 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.
- How machine learning is making bots more human - Apr 25, 2016.
See how machine learning is making bots more human than ever - read the interview with a 17-year-old Chinese girl named XiaoIce who is actually a artificially intelligent chatbot.
- 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.
- Black Box Challenge Machine Learning Competition - Apr 21, 2016.
Take part in an unusual machine learning competition — program an agent (in Python) that can play a game with unknown rules.
- Best Data Science, Machine Learning Blogs from Companies and Startups - Apr 21, 2016.
A collection of company data science blogs to follow and read. Top blogs have links to, and excerpts from, recent quality posts of particular interest.
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- KDnuggets™ News 16:n14, Apr 20: Top 15 Frameworks for Machine Learning Experts; How to Grow Your Own Data Scientists - Apr 20, 2016.
Top 15 Frameworks for Machine Learning Experts; How to Grow Your Own Data Scientists; Association Rules and the Apriori Algorithm: A Tutorial; Automated Machine Learning: Changing the Game.
- 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.
- The MBA Data Science Toolkit: 8 resources to go from the spreadsheet to the command line - Apr 18, 2016.
A great guide for the MBA, or any relatively non-technical convert, for getting comfortable with the command line and other technical skills required to excel in data science.
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- 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.
- 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.
- Automated Machine Learning: Changing the Game - Apr 14, 2016.
Making sense of the mountains of data collected on a daily basis requires specialized data science skills that are hard to come by, and hard to keep. Augmented or even eliminated some of these specialized tasks with machine learning.
- Stochastic Depth Networks Accelerate Deep Network Training - Apr 7, 2016.
Read about the presentation and overview of a new deep neural network architectural method, and the response to some strong reaction that it brought about.
- KDnuggets™ News 16:n12, Apr 6: Top 10 Essential Books; Perfect Data Science Interview - Apr 6, 2016.
Top 10 Essential Books for the Data Enthusiast; How to Compute the Statistical Significance of Two Classifiers Performance Difference; The Secret to a Perfect Data Science Interview; If Hollywood Made Movies About Machine Learning Algorithms.
- Top /r/MachineLearning Posts, March: Hugs, Deep Learning Navigation, 3D Face Capture, AlphaGo! - Apr 4, 2016.
What's huggable, adversarial images for deep learning, overview of real-time 3D face capture and reenactment, deep learning quadcopter navigation, and a whole lot of AlphaGo!
- 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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- Avoiding Complexity of Machine Learning Problems - Mar 31, 2016.
Sometimes machine learning is the perfect tool for a task. Sometimes it is unnecessary overkill. Here are important lessons learned from the Quora engineering team.
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- Pattern Curators of the Cognitive Era - Mar 31, 2016.
Machine learning has a critical dependency on human learning. But not just on Data Scientists, but on legions of people who legions of individuals who prepare training data to guide algorithms.
- How To Become A Machine Learning Expert In One Simple Step - Mar 29, 2016.
This post looks at perhaps the most important, and often overlooked, step in learning machine learning, an aspect which can make the biggest difference in one's skill set.
- 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.
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- 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.
- Salford Systems: Software Engineer. Machine Learning algorithms. C++ - Mar 12, 2016.
Salford Systems is an advanced data mining software and consulting company, affiliated with some of the world greatest Machine Learning scientists, and leads the industry with exciting, innovative products.
- 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.
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- 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.
- 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.
- Machine Learning at your fingertips – 60+ free APIs, from HPE Haven OnDemand - Feb 29, 2016.
HPE Haven on Demand has 60+ Machine Learning free APIs to connect, extract, analyze, search, predict - get your API Key and RSVP for the HPE Analytics World Tour.
- The Machine Learning Problem of The Next Decade - Feb 26, 2016.
How can businesses integrate imperfect machine-learning algorithms into their workflow?
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- HPI Future SOC Lab offers researchers free access to a powerful Big Data & Computing infrastructure - Feb 19, 2016.
The HPI Future SOC (Service-Oriented Computing) Lab is a cooperation of the Hasso Plattner Institute (HPI) and industrial partners, providing free access to a powerful Big Data & Computing infrastructure. It is now accepting project proposals.
- 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.
- Ensemble Methods: Elegant Techniques to Produce Improved Machine Learning Results - Feb 12, 2016.
Get a handle on ensemble methods from voting and weighting to stacking and boosting, with this well-written overview that includes numerous Python-style pseudocode examples for reinforcement.
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- 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.
- Machine Learning Course for R&D Specialists, 4-8 April, Delft, The Netherlands - Feb 1, 2016.
Do you want to go beyond theory and learn how to create working Machine Learning solutions? This 5-day course provides you with practical step-by-step methodology.
- Introducing Quora’s Machine Learning Sessions Series - Jan 19, 2016.
Quora is launching a new format for interacting with domain experts and sharing knowledge, and its first topic is Machine Learning. Yoshua Bengio is the first expert, and he is accepting questions now.
- 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.
- Hitchhikers Guide to Azure Machine Learning Studio - Jan 15, 2016.
Learn Azure ML Studio through this brief hands-on tutorial. This step-by-step guide will help you get a quick-start and grasp the basics of this Predictive Modeling tool.
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- 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.
- 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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- TensorFlow is Terrific – A Sober Take on Deep Learning Acceleration - Dec 30, 2015.
TensorFlow does not change the world. But it appears to be the best, most convenient deep learning library out there.
- 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.
- The future of analytics – top 5 predictions for 2016 - Dec 23, 2015.
Analytics has never been more needed or interesting and the future looks exciting. Top 2016 trends include Machine learning established in the enterprise, Internet of Things hype hits reality, and Big data moves beyond hype to enrich modeling.
- 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.
- KDnuggets™ News 15:n41, Dec 16: Top 10 Machine Learning Projects on Github; How to use Python and R together - Dec 16, 2015.
Top 10 Machine Learning Projects on Github; Using Python and R together: 3 main approaches; Top 2015 KDnuggets Stories on Analytics, Big Data, Data Science; 22 Big Data experts predictions for 2016.
- Top KDnuggets tweets, Dec 07-13: 50 Useful Machine Learning and Prediction APIs; 35 R Job Interview Questions, Answers - Dec 14, 2015.
R Programming: 35 Job #Interview Questions and Answers; A Look into #MachineLearning First Cheating #Scandal; The current state of #machine #intelligence 2.0 ; #Dilbert Dark #humor on combining #DNA tests and #Bevaviour #Predictions;