Coque tom et jerry iphone xr Advanced Machine Learning with Basic Excel

Advanced Machine Learning with Basic Excel In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniques in question are math free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. Implementations in Python, R, Julia and Perl are provided, but here we focus on an Excel version coque iphone 6 magie that does not even require any Excel macros, coding, plug ins, or anything other than the most basic version of Excel. It coque iphone 6 superhero is actually easily implemented in standard, basic SQL too, and we invite readers to work on an SQL version. Source for picture:Logistic Regression vs Decision Trees iphone 6 coque mustang vs coque cuir rouge huawei p8 lite 2017 SVM In short, we offer here an Excel template for machine learning demonter coque huawei p smart and statistical computing, and it is quite powerful for an Excel spreadsheet. The technique blends multiple algorithms that at first glance look traditional and math heavy, such coque telephone iphone 6 transparente as decision trees, regression (logistic or linear) and confidence intervals. But they are radically different, can fit huawei p6 lite 2017 coque in a small spreadsheet (though coque p20 lite huawei disney the Python version is more powerful, flexible, and efficient), and do not involve math beyond high school level. In particular, no matrix algebra is required to understand the methodology. Most everyone else believed or made people believe that only complex system work, and have spent their time complexifying algorithms rather than simplifying them (partly for job security purposes.) Node table (extract, from spreadsheet) Who should use the spreadsheet First, the spreadsheet (as well as the Python, R, Perl or coque huawei y6 2018 lion Julia version) are coque huawei p20 lite flament rose free to use and modify, even for commercial, purposes,or to make a product coque iphone 6 unicorn out of it and sell it. It is part of my concept of open patent, in which I share all my intellectual property publicly and coque huawei y 5ii silicone for free. The spreadsheet is designed coque hybride iphone 6 plus as a tutorial, though it processes the same data set as the one used for the Python version. It is aimed coque iphone 6 plus rigolote at people that are not professional coders, people who manage data scientists, BI experts, MBA professionals, and people from other fields, with an interest in understanding the mechanics of some state of the art machine learning techniques, without having to spend months or years learning mathematics, programming, and computer science. A few hours is needed to understand the coque rinochild huawei p30 details. This spreadsheet can be the first step to help you transition to a new, more analytical career path, or to better understand the data scientists that you manage or interact with. Or to spark a career in data science. Or even to teach machine learning concepts to high school students. The spreadsheet also features a traditional technique (linear regression) for comparison purposes. 2. Description of the techniques used Here we yoowei coque huawei y6 explain the differences between the standard and the Excel versions, and we provide an overview, at a high level, of the techniques being used, as well as why they are better in pretty much all applications, especially with unstructured and large data sets. Detailed descriptions are available in coque iphone 6 walt disney the coque huawei p 20 lite amazon articles referenced in this psmart plus huawei 2019 coque section. Spreadsheet versus Python version The Python version (also available in R, Perl and Julia) of the core technique is described here. Python / Perl offer the following advantages over Excel: It easily handles version 2.0of HDT including overlapping nodes It easily handles big datasets, even in a distributed environment if needed It easily handles a large number of nodes Of course, it is incredibly faster for large data sets The Excel version has the advantage of iphone 6 coque liquide being interactive, and you can share it with people who are not data scientists. But Excel (at least the template provided here) is mostly limited to nodes that form a partition of the feature space, that is, it is limited to non overlapping nodes: see HDTversion 1.0. So even if we have two nodes, one for the keyword data, and one for the keyword data science, in version 1,0, they are not overlapping: text buckets contain either data and not data science, or data science. In version 2.0, we no longer have this restriction. Note that nodes can be a combination of any number of keyword values or any other variables (called features in machine learning), and these variables can be quantitative or not. For those familiar with computer science, nodes, both in the Excel or the Python version, are represented here as key value pairs, typically stored as hash coque iphone 6 peinture tables in Perl or Python, and as concatenated strings in Excel. For statisticians, nodes are just nodes of decision trees, though no tree structure is used (nor built) in my methodology and this is why it is sometimes referred to as hidden decision trees (HDT). But you don’t need to understand this to use the methodology or understand how the spreadsheet works. What is it about What kind of algorithms are offered The methodology features an hybrid algorithm with essentially two components: Data aggregation into bins, based on sound feature selection, binning continuous and even discrete features, and metric design, not unlike decision trees. However, no tree is actually built, and the nodes may belong to several overlapping small decision trees, each one corresponding to a case or cluster easy to interpret. This is particularly true in HDT 2.0.

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