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A Visual Introduction to Machine Learning

A Visual Introduction to Machine Learning
Finding better boundaries Let's revisit the 73-m elevation boundary proposed previously to see how we can improve upon our intuition. Clearly, this requires a different perspective. By transforming our visualization into a histogram, we can better see how frequently homes appear at each elevation. While the highest home in New York is 73m, the majority of them seem to have far lower elevations. Your first fork A decision tree uses if-then statements to define patterns in data. For example, if a home's elevation is above some number, then the home is probably in San Francisco. In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. Tradeoffs Picking a split point has tradeoffs. Look at that large slice of green in the left pie chart, those are all the San Francisco homes that are misclassified. The best split Recursion

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A Tour of Machine Learning Algorithms In this post, we take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms available that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit. Statistics is Dead – Long Live Data Science… I keep hearing Data Scientists say that ‘Statistics is Dead’, and they even have big debates about it attended by the good and great of Data Science. Interestingly, there seem to be very few actual statisticians at these debates. So why do Data Scientists think that stats is dead? Where does the notion that there is no longer any need for statistical analysis come from? And are they right?

How to get started with Machine Learning on Bluemix There is a lot of talk about artificial intelligence (AI) these days, especially since Google’s AlphaGo beat a Go world champion. Companies like IBM are using this technology already in a number of products. For example on Bluemix developers can easily consume cognitive Watson services like speech or image recognition that use machine and deep learning under the cover. Start Here Get Started and Get Good at Applied Machine Learning Hi, Jason here. I’m the guy behind Machine Learning Mastery. My goal is to help you get started, make progress and kick butt with machine learning. Data Types 101 Ever looked at your data and wondered how and where to get started? If you don't know the difference between quantitative data and qualitative data then you're in the right place. Here is our guide to data types and how to deal with them...

10 Machine Learning Examples in JavaScript Machine learning libraries are becoming faster and more accessible with each passing year, showing no signs of slowing down. While traditionally Python has been the go-to language for machine learning, nowadays neural networks can run in any language, including JavaScript! The web ecosystem has made a lot of progress in recent times and although JavaScript and Node.js are still less performant than Python and Java, they are now powerful enough to handle many machine learning problems. Web languages also have the advantage of being super accessible - all you need to run a JavaScript ML project is your web browser.

ConvNetJS: Deep Learning in your browser ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. Description The library allows you to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (I am a PhD student at Stanford). How to backup Gmail: The ultimate guide This device is unable to play the requested video. This article was originally published in July 2015, updated in 2017, and now, in 2018. A few years ago, I moved off of Office 365 and Outlook and onto Gmail. Many of you thought I'd regret the move, but I have to tell you that Gmail has been a nearly frictionless experience. I don't think I'd ever go back to using a standalone email application. In fact, I'm moving as many applications as I can to the cloud, just because of the seamless benefits that provides.

Comparing machine learning classifiers based on their hyperplanes or decision boundaries - Data Scientist TJO in Tokyo In Japanese version of this blog, I've written a series of posts about how each kind of machine learning classifiers draws various classification hyperplanes or decision boundaries. So in this post I want to show you a summary of the series and how their hyperplanes or decision boundaries vary (translated from Japanese version). It must be interesting and help you understand a nature of each classifier. Here I chose some representative classifiers as follows: decision tree (DT), logistic regression (LR: only for linearly separable cases), support vector machine (SVM), neural networks (NN: back-propagation multi-layer perceptron) and random forest (RF).

Wooden Flute Sheet Music See the tablature for Amazing Grace and Zuni Sunrise below Explanation of Native American Flute Tablature: Nakai Tablature has become the standard method for writing music for the Native American flute. This system was first written about in Art of the Native American Flute, by R. Carlos Nakai and James DeMars, Canyon Record Productions, 1996.

Machine Learning Crash Course: Part 4 - The Bias-Variance Dilemma · ML@B By Daniel Geng and Shannon Shih13 Jul 2017 Here’s a riddle: So what does this have to do with machine learning? Well, it turns out that machine learning algorithms are not that much different from our friend Doge: they often run the risk of over-extrapolating or over-interpolating from the data that they are trained on. Introduction to Strategic Thinking Starts June 22, 2015 This is a short interdisciplinary course on strategic thinking and some of its most powerful tools. Strategic thinking is not exclusive to business or military applications. The skills taught in this course can be used by everyone. Young professionals can use the knowledge to effectively plan their careers, stay-at-home mothers can use it to improve how they communicate with their children, and entrepreneurs can use it to better position their business in the marketplace.

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