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#ML Weekly 20151005 – The Best New Machine Learning Content of the Week

  • More-flexible machine learning
  • Machine learning helps build better applications
  • Machine learning powers Splunk’s rise | #BigDataNYC
  • Qualcomm Showcases Machine Learning On A Snapdragon SoC | Androidheadlines.com
  • Microsoft + Machine Learning Can Help You Look Younger #StrataHadoop
  • Qualcomm Demos Server-Class Machine Learning with Zeroth – xda-developers
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#ML Weekly 20150915 – The Best New Machine Learning Content of the Week

  • Wandera uses machine learning to protect against new mobile security threats
  • Machine Learning and Its Impact on Cyber-Security – insideBIGDATA
  • Building Intelligence into your Apps with Azure Machine Learning with Corom Thompson (Channel 9)
  • Free 5-week course on data science and machine learning essentials begins Sept. 24 – WinBuzzer
  • Apple’s Quest for Machine Learning Services Bumps up against Privacy Protection – The Mac Observer
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#ML Weekly 20150907 – The Best New Machine Learning Content of the Week

  • 15 Players that Use Machine Learning in FinTech Space – Let’s Talk Payments
  • Machine learning: Mainstream tools for your business | ZDNet
  • Auction site Trade Me trials machine learning in the cloud | ZDNet
  • Machine Learning Method: Semi-Supervised Clustering
  • FeatureFu: A Machine Learning Toolkit Released as Open Source by LinkedIn
  • Twitter Is Using Machine Learning to Improve Its Machine Learning
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#ML Weekly 20150901 – The Best New Machine Learning Content of the Week

“Qualcomm’s Snapdragon 820 uses machine learning to fight malware”; “PayPal Fights Fraud with Machine Learning and ‘Human Detectives’”; “A Gentle Guide to Machine Learning”; “Machine Learning 2.0: What’s Old Is New Again”; “Alibaba’s artificial intelligence platform aids machine learning without need for coding – CloudHub”;

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#ML Weekly 20150825 – The Best New Machine Learning Content of the Week

“Featurizing Data: Spark and Beyond: Leverage data transformation capabilities in Spark with Machine Learning”; “An Introduction to Distributed Machine Learning”; “5 Techniques To Understand Machine Learning Algorithms Without the Background in Mathematics – Machine Learning Mastery”; “4 Machine Learning Algorithms That Shape Your Life”;

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Microsoft Azure Machine Learning Cheat Sheet v6 – Released today

The Microsoft Azure Machine Learning team just released a new version of the ML Algorithm Cheat Sheet to cover the latest ML algorithm choices. For a deeper discussion of the different types of machine learning algorithms, how they’re used, and how to use this cheat sheet for choosing the right algorithm, see How to choose[…]

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#ML Weekly 20150817 – The Top Machine Learning Content of the Week

Top New Content of the Week: “‘Machine teaching’ holds the power to illuminate human learning”; “How Machine Learning Changes the Game”; “‘Machine teaching’ holds the power to illuminate human learning”; “New software that use geometric matching and machine learning to mimic humans’ perception – DeepStuff.org”; “Managing unbalanced data for building machine learning models”;

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mlvisual

Interesting Visual Explaining Machine Learning to Beginners

R2D3 just released a great visual to explain Machine Learning to beginners.  It is a great way for non-ML people in your organization, e.g. sales & marketing people, to quickly pick up the Machine Learning basics and terminologies. http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

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azuremlbook

New Book: Machine Learning Projects for .NET Developers

Here’s a new book which is receiving a positive reception Machine Learning Projects for .NET Developers   Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You’ll code each project[…]

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machineteaching

Christopher Bishop: The future of Machine Learning is "Machine Teaching"

Christopher Bishop, a Distinguished Scientist from Microsoft, is leading an effort called ‘Machine Teaching’.  It will allow anyone to teach a computer how to do machine learning tasks, even if that person has no expertise in data analysis or computer science.  As machine learning becomes more widespread, more applications will start to leverage the power[…]

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Best Machine Learning & AI Cloud Services in the Market

Microsoft Azure Machine Learning / Microsoft Project Oxford Cloud-based predictive analytics and publishing of APIs on the cloud.   Its allows you to Modal your way Deploy in minutes Expand your reach Project Oxford, leveraging Azure Machine Learning, allows developers to create smarter apps, which can do things like recognize faces and interpret natural language even[…]

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ML101: How to Choose a Machine Learning Algorithm for Multi-class Classification Problems

In classification, the target variable is categorical and unordered..  To solve a multi-class classification problem, we typically choose one of the following supervised learning algorithms in machine learning.   Algorithm Accuracy Training Time Linearity Parameters Additional Comments Multiclass Logistic regression ★☆☆ ★★★ ★★★ 5 Multiclass Decision Forest ★★★ ★★☆ ★☆☆ 6 Multiclass Decision Jungle ★★★[…]

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machine-learning

ML101: How to Choose Machine Learning Algorithms

For Classification Problems In classification, the target variable is categorical and unordered.   To solve a two-class classification problem, we typically choose one of the following supervised learning algorithms in machine learning. Algorithm Accuracy Training Time Linearity Parameters Additional Comments Two-Class Logistic Regression ★☆☆ ★★★ ★★★ 5 Two-Class Decision Forest ★★★ ★★☆ ★☆☆ 6 Two-Class[…]

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TwoClassClassifier

ML101: How to Choose a Machine Learning Algorithm for Two-class Classification Problems

In classification, the target variable is categorical and unordered..  To solve a two-class classification problem, we typically choose one of the following supervised learning algorithms in machine learning.   Algorithm Accuracy Training Time Linearity Parameters Additional Comments Two-Class Logistic Regression ★☆☆ ★★★ ★★★ 5 Two-Class Decision Forest ★★★ ★★☆ ★☆☆ 6 Two-Class Decision Jungle ★★★[…]

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regressionproblem

ML101: How to Choose a Machine Learning Algorithm for Regression Problems

In regression, the target variable is continuous or ordered whole values.  To solve a regression problem, we typically choose one of the following supervised learning algorithms in machine learning.   Algorithm Accuracy Training Time Linearity Parameters Additional Comments Linear Regression ★☆☆ ★★★ ★★★ 4 Bayesian Linear Regression ★☆☆ ★★☆ ★★★ 2 Decision Forest Regression ★★★[…]

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Reinforcement-Learning

ML101: Reinforcement Learning

Reinforcement Learning a the 3rd area in Machine Learning.  It’s different from Supervised Learning and Unsupervised Learning. In reinforcement learning, the algorithm gets to choose an action in response to each data point. The learning algorithm also receives a reward signal a short time later, indicating how good the decision was. Based on this, the[…]

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imagetagging

Automatic Image and Video Tagging

It’s a hot research area to automatically generate description or tag for images and videos.  How can machine “understand” an image or a video?  In this blog post, I curate the most popular and current approaches (most of them are presented in CVPR 2015) to tackle this challenge.   Approach 1: Multiple Instance Learning (MIL)[…]

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Hands-on Lab: Unsupervised Learning in Azure Machine Learning

Download the Lab This lab explores unsupervised learning in Azure Machine Learning and how to deploy a predictive model as a web service. The lab will walk through copying an experiment from the Azure Machine Learning Gallery into the ML Studio, creating a scoring experiment, deploying a model as a web service, and interacting with[…]

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Hands-on Lab: Text Analytics with R and Azure Machine Learning

Download the Lab This lab explores text analytics and R integration with Azure Machine Learning. It will walk through loading data from an external source, using R scripts in ML Studio, and common text analytics tasks and visualizations. Social media has become a very influential platform for companies, consumers, and professionals to express ideas and[…]

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Get Started with Azure Machine Learning in 9 Easy Steps

Even if you have never done machine learning before, you can build and understand how to build a Machine Learning model and solve a real-world problem in only 9 steps!  In this blog post, I will walk through accessing the ML Studio environment, exploring and visualizing data in Azure Machine Learning, and creating a simple[…]

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TwinsOrNot.net Source Code Download

Have you ever wondered how the well-known http://how-old.net and http://twinsornot.net were implemented?   Both are implemented using the Microsoft Project Oxford Face Algorithms.   TwinsOrNot.net has been open-sourced in GitHub.  You can download a copy of its source code from here.     ———————————————— p.s.  Here is a fun post by Stephan after I shared[…]

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health-care-industry

Machine Learning in Medical (Healthcare) Industry

Case study: Predixion built a healthcare solution based on machine learning for Carolina Health   Case study: Medication adherence AiCure uses mobile technology and facial recognition to determine if the right person is taking a given drug at the right time.

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overfitting

ML101: Overfitting vs Underfitting

Overfitting (aka. high variance) If we have too many features, the learned hypothesis may fit the training set very well (), but fail to generalize to new examples.  It occurs when a statistical model or machine learning algorithm captures the noise of the data.  Intuitively, overfitting occurs when the model or the algorithm fits the[…]

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logisticregression

ML101: Logistic Regression

Logistic regression, despite its name, is a linear model for classification rather than regression.  It is regressing for the probability of a categorical outcome. And this categorical outcome is captured in binary format, i.e. 0 or 1. Note: Logistic regression is also known as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier.  Frequently, “logistic[…]

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I used Machine Learning to Solve my Girlfriend’s Puzzle for me!

My girlfriend:  Scott, I want to test how much you know about me! Me:  No sweat!  It’s a piece of cake! My girlfriend: my friend (another girl) sent me some photos of high-heeled shoes and ask me if they look nice.  I don’t like them, so I suggested some new shoes.  Can you guess what[…]

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ML101: Polynomial Regression

Polynomial regression is a form of linear regression in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial. Here is the simplest Polynomial:  One independent variable – Second order (also known as quadratic function) The next larger Polynomial: One independent variable – Third order[…]

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gradientdescentmountain

ML101: Gradient Descent vs. Normal Equation

This blog talks about when you should use Gradient Descent and when you should use Normal Equation.  Here are some of their advantages and disadvantages. Let’s say that you have m training samples and n features.   Gradient Descent In Gradient Descent algorithm, in order to minimize the cost function J(θ), we take this iterative[…]

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EstimateMultipleLinearRegressionCoefficientsExample_01

ML101: Linear Regression with Multiple Variables (aka. Multivariate Linear Regression)

Linear Regression with Multiple Variables, also called as ‘Multivariate Linear Regression’, is used when you want to predict a single output value from multiple input value. For example, we want to predict the house price (y) from not only the house size, but also the number of bedrooms, the number of floors and the age[…]

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ML101: Linear Regression with One Variable (aka. Univariate Linear Regression)

Linear Regression with One Variable, also called as ‘Univariate Linear Regression’, is used when you want to predict a single output value from a single input value. For example, we want to predict the house price (y) solely from the house size (x) based on a linear regression model. If this topic is new to[…]

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IBM is going to offer Machine Learning capabilities

Quoted from today’s news, IBM is going to offer machine learning capabilities:  

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ML101: Regression vs Classification vs Clustering Problems

Regression In regression, the target variable is continuous or ordered whole values For example, suppose you are working on stock market prediction, and you would like to predict the price of a particular stock tomorrow (measured in dollars).   This is a regression problem because the target variable (stock price) is continuous. To solve a regression[…]

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plot_classification

ML101: Supervised Learning vs Unsupervised Learning Algorithms

Supervised Learning Supervised learning (machine learning) takes a known set of input data and known(labeled) responses to the data, and seeks to build a predictor model that generates reasonable predictions for the response to new data. For example, given data on how 1000 medical patients respond to an experimental drug (such as effectiveness of the treatment,[…]

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bigdatasets

Online Data Sets for Machine Learning

Here is a list of publicly available data sets that can be used for self-training in Machine Learning.   US Government Open Data http://www.data.gov US Census Bureau http://www.census.gov/ UC Irvine Machine Learning Repository http://archive.ics.uci.edu/ml/, http://archive.ics.uci.edu/ml/datasets.html, http://www.sgi.com/tech/mlc/db/ (archive) John Hopkins University http://www.biostat.jhsph.edu/courses/bio624/datasets/datasets.htm Microsoft Azure Marketplace http://datamarket.azure.com/browse/Data Amazon Public Data Sets http://aws.amazon.com/public-data-sets/ DataMarket http://datamarket.com/ World Bank http://data.worldbank.org/[…]

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keyphraseextraction

Best Key Phrase Extraction APIs in the Market

Update on 6/30/2015: added Proxem’s API “ontology based topic détection” to the list thanks to Tom‘s comment. Here is a list of machine learning resources that helps to extract key phrases in the input text. Commercial APIs: 1. Azure Machine Learning’s Text Analytics service Text Analytics API is a suite of text analytics services built[…]

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scikit-learn Machine Learning Algorithm Cheat Sheet

scikit-learn is a Machine Learning library in Python.  This flow chart shows how to do machine learning.   Also see Microsoft Azure Machine Learning Algorithm Cheat Sheet   Dlib C++ Library Machine Learning Algorithm Cheat Sheet

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Dlib C++ Library Machine Learning Algorithm Cheat Sheet

  Dlib is a general purpose cross-platform C++ library designed using contract programming and modern C++ techniques.  It covers Machine Learning algorithms.  A major design goal of this portion of the library is to provide a highly modular and simple architecture for dealing with kernel Machine Learning algorithms. http://dlib.net/ml.html   Also see: Microsoft Azure Machine[…]

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azureml

Microsoft Azure Machine Learning Algorithm Cheat Sheet

Update: A new version of the Microsoft Azure Machine Learning Algorithm Cheat Sheet is now Available. For a deeper discussion of the different types of machine learning algorithms and how they’re used, see How to choose an algorithm in Azure Machine Learning. For a list of all the machine learning algorithms available in Machine Learning[…]

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Best Data Science Online Courses

Updated on Jun 14, 2015 You can consider online classes from Coursera for self-study.  Coursera provides online classes (most of them are free) offered by university professors, typically attended worldwide by thousands of students and working professionals. In particular, consider the Data Science Specialization from John Hopkins University, which offers a guaranteed certificate demonstrating your ability.[…]

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Business Intelligence (BI) Workflow

  BI system has 3 important phases, Collect, Analyze and View Collect data Data in an enterprise can be stored in various formats. Now these formats can vary from normalized structured RDBMS to excel sheets or probably unstructured file formats. So the first step in BI is to collect all these unstructured and scattered data[…]

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Data mart vs. Data warehouse

  Data Warehouse Data Warehouse is a system used for reporting and data analysis. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating analytical reports for knowledge workers throughout the enterprise. Examples of reports could range from annual and quarterly[…]

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Laurie Frick: Turning Personal Big Data into Art

Today I attended a presentation by Laurie Frick, a data artist exploring patterns of self-tracking sensors, surveillance, etc. Using her background in high-technology she offers an alternative view of privacy and a glimpse into the future of human data portraits with handmade installations from her personal data.  Her art is so amazing as to be[…]

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Microsoft Open-Sources its Build System on GitHub Today

Today, Microsoft announced that its build system called ‘MSBuild’ is open-sourced on GitHub and part of the .NET Foundation.   The sources on GitHub are closely aligned with the version that ships with Visual Studio. So What’s MSBuild and its purpose? MSBuild is the build platform that enables all build activity in the Visual Studio world.  […]

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hubot

Github Hubot: Set up an 'R2' robot in your coding team

Today, I discovered a very interesting project created by Github:  Hubot (https://hubot.github.com/) In Github’s own words: GitHub, Inc., wrote the first version of Hubot to automate our company chat room. Hubot knew how to deploy the site, automate a lot of tasks, and be a source of fun in the company. Eventually he grew to[…]

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opensource

A Complete List of .NET Open Source Developer Projects

NET Implementations .NET Core – Core .NET Framework C# Native – Compiles C# to native. Cosmos – C# Open Source Managed Operating System, an operating system “construction kit”. Fling OS – C# Operating System designed for people to learn low-level development from. Mono – Cross-platform implementation of .NET Framework. MOSA Project – Managed Operating System[…]

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How to customize and deploy an MVCForum website to Azure

I decided to create a personal technical forum in Microsoft Azure, using MVCForum, an open-source forum platform. http://ask.scottge.net The Microsoft Azure web app gallery does support MVCForum.  By following the wizard you can deploy the forum with just a few clicks.  But I am going to download the MVCForum source code, customize it and manually[…]

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TwinsOrNot.net Source Code Download

Have you ever wondered how the well-known http://how-old.net and http://twinsornot.net were implemented?   Both are implemented using the Microsoft Project Oxford Face Algorithms.   TwinsOrNot.net has been open-sourced in GitHub.  You can download a copy of its source code from here.     ———————————————— p.s.  Here is a fun post by Stephan after I shared[…]

2 comments
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#SQLServer Weekly 20151005 – the Best New SQL Server Content of the Week

  • Enhanced Map with Custom Geography (SQL Server-compatible via WKT/STAsText)
  • SQL Server: Migrations/Upgrades – 10 Things to Think About | Official Pythian Blog | Pythian®
  • Bye bye 32-bit (X86) SQL Server components!
  • Troubleshooting SQL Server : Do You Use These Shortcuts?
  • Exploring the Crucial Five Must-Have SQL Server Tools for DBAs | HostReview.com
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#SQLServer Weekly 20150915 – the Best New SQL Server Content of the Week

  • What’s new with Microsoft SQL Server Analysis Services Tabular models in SQL Server 2016 CTP 2.3
  • How To Begin Your Career As a SQL Server DBA – udemy #SQL #DBA course – Udemy 100% off Udemy coupon
  • Monitoring and Visualizing SQL Server using System Center Operations Manager – System Center Central
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#SQLServer Weekly 20150907 – the Best New SQL Server Content of the Week

  • Now Hiring: Build Our SQL Server Triage Specialist Team (Remote Position) – Brent Ozar Unlimited®
  • Automating SQL Server Configuration with PowerShell and WMI
  • SQL Server 2016 – TempDB
  • Microsoft’s SQL Server 2016 CTP 2.3 now available to download – Petri
  • Retrieving Table Metadata from SQL Server Catalog Views
  • SQL Server Management Studio August 2015 Preview #SSMS
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#SQLServer Weekly 20150901 – the Best New SQL Server Content of the Week

“What Technical Experts Are Probably Not Telling You About SQL Server Availability Groups”; “Writing Perfmon data to SQL Server”; “Eugene SQL Server User Group meeting Jan 13 Evening”; “SQL Server Change Data Capture Application”; “The Essential Guide the SQL Server 2014: Clustered Columnstore Index”; “How to check if a Database exists in Sql Server”;

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#SQLServer Weekly 20150825 – the Best New SQL Server Content of the Week

“SQL Server 2005 end-of-life: how to plan for the future | Information Age”; “New Features that You Should Know in SQL Server 2016 -“; “The right data for the job: Analysing the SQL Server numeric data types”; “Different type of Sql Statement in Sql Server – Niraj Tiwari”; “Charlotte SQL Server User Group – Wed May 21st – Meeting Invitation and RSVP”;

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#SQLServer Weekly 20150817 – the Top New SQL Server Content of the Week

Top New Content of the Week: “How to restore SQL Server database with Veeam Explorer for SQL Server”; “Sql Server Interview questions: What is the XML datatype?”; “ColumnStore Indexes in MS SQL Server”; “Utah SQL Server Group”; “Dealing with an Inherited SQL Server”; “Beneficios de la tecnología En-Memoria de SQL Server 2014”;

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