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<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:wfw="http://wellformedweb.org/CommentAPI/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:slash="http://purl.org/rss/1.0/modules/slash/" xmlns:georss="http://www.georss.org/georss" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" > <channel> <title>YOU CANalytics | </title> <atom:link href="https://ucanalytics.com/blogs/feed/" rel="self" type="application/rss+xml" /> <link>https://ucanalytics.com/blogs</link> <description>Explore the Power of Data Science</description> <lastBuildDate>Tue, 18 Aug 2020 09:02:51 +0000</lastBuildDate> <language>en-US</language> <sy:updatePeriod> hourly </sy:updatePeriod> <sy:updateFrequency> 1 </sy:updateFrequency> <generator>https://wordpress.org/?v=6.2.7</generator> <image> <url>https://i0.wp.com/ucanalytics.com/blogs/wp-content/uploads/2015/11/cropped-YOU-CANalytics-Logo-1.jpg?fit=32%2C32&ssl=1</url> <title>YOU CANalytics | </title> <link>https://ucanalytics.com/blogs</link> <width>32</width> <height>32</height></image> <site xmlns="com-wordpress:feed-additions:1">55531657</site> <item> <title>Machine Learning for Debt Collection and Recovery Scorecards for Banks</title> <link>https://ucanalytics.com/blogs/machine-learning-for-debt-collection-and-recovery-scorecards-for-banks/</link> <comments>https://ucanalytics.com/blogs/machine-learning-for-debt-collection-and-recovery-scorecards-for-banks/#comments</comments> <dc:creator><![CDATA[Roopam Upadhyay]]></dc:creator> <pubDate>Tue, 18 Aug 2020 14:11:56 +0000</pubDate> <category><![CDATA[Risk Analytics]]></category> <category><![CDATA[Video Discussion]]></category> <guid isPermaLink="false">http://ucanalytics.com/blogs/?p=11937</guid> <description><![CDATA[<p>Covid-19 pandemic has ignited an unprecedented risk for the economy. Banks and financial institutions across the globe are expected to register unusually high default rates on loans once the moratorium and forbearance imposed by the governments and the regulators are lifted. Scientific tools such as collection and recovery scorecards offer a mechanism to predict defaults</p><p><a class="excerpt-more blog-excerpt" href="https://ucanalytics.com/blogs/machine-learning-for-debt-collection-and-recovery-scorecards-for-banks/">Read More...</a></p><p>The post <a rel="nofollow" href="https://ucanalytics.com/blogs/machine-learning-for-debt-collection-and-recovery-scorecards-for-banks/">Machine Learning for Debt Collection and Recovery Scorecards for Banks</a> appeared first on <a rel="nofollow" href="https://ucanalytics.com/blogs">YOU CANalytics | </a>.</p>]]></description> <wfw:commentRss>https://ucanalytics.com/blogs/machine-learning-for-debt-collection-and-recovery-scorecards-for-banks/feed/</wfw:commentRss> <slash:comments>7</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">11937</post-id> </item> <item> <title>How data science will shape post-COVID banking? – Video Discussion</title> <link>https://ucanalytics.com/blogs/how-data-science-will-shape-post-covid-banking-video-discussion/</link> <comments>https://ucanalytics.com/blogs/how-data-science-will-shape-post-covid-banking-video-discussion/#respond</comments> <dc:creator><![CDATA[Roopam Upadhyay]]></dc:creator> <pubDate>Mon, 20 Jul 2020 14:02:02 +0000</pubDate> <category><![CDATA[Marketing Analytics]]></category> <guid isPermaLink="false">http://ucanalytics.com/blogs/?p=11933</guid> <description><![CDATA[<p>How data science will shape post-COVID banking? had a thought-provoking discussion with FrankBanker: 02:01 (Part 1) Impact on variables in Credit Models05:33 (Part 2) Are we going back to Judgemental Lending?07:50 (Part 3) Evaluating analytics readiness of Banks12:20 (Part 4) Is ‘IT’ the right place for Data Analytics?14:15 (Part 5) Changes in the Credit Scoring methodologies20:47</p><p><a class="excerpt-more blog-excerpt" href="https://ucanalytics.com/blogs/how-data-science-will-shape-post-covid-banking-video-discussion/">Read More...</a></p><p>The post <a rel="nofollow" href="https://ucanalytics.com/blogs/how-data-science-will-shape-post-covid-banking-video-discussion/">How data science will shape post-COVID banking? – Video Discussion</a> appeared first on <a rel="nofollow" href="https://ucanalytics.com/blogs">YOU CANalytics | </a>.</p>]]></description> <wfw:commentRss>https://ucanalytics.com/blogs/how-data-science-will-shape-post-covid-banking-video-discussion/feed/</wfw:commentRss> <slash:comments>0</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">11933</post-id> </item> <item> <title>Artificial Intelligence and Machine Learning for Business – A Video Talk</title> <link>https://ucanalytics.com/blogs/artificial-intelligence-and-machine-learning-for-business-a-video-talk/</link> <comments>https://ucanalytics.com/blogs/artificial-intelligence-and-machine-learning-for-business-a-video-talk/#comments</comments> <dc:creator><![CDATA[Roopam Upadhyay]]></dc:creator> <pubDate>Wed, 06 May 2020 15:55:12 +0000</pubDate> <category><![CDATA[Video Discussion]]></category> <guid isPermaLink="false">http://ucanalytics.com/blogs/?p=11924</guid> <description><![CDATA[<p>How will artificial intelligence and machine learning transform businesses? In this introductory part of the talk, learn how artificial intelligence will play a pivotal role to resolve conflicts within businesses.</p><p>The post <a rel="nofollow" href="https://ucanalytics.com/blogs/artificial-intelligence-and-machine-learning-for-business-a-video-talk/">Artificial Intelligence and Machine Learning for Business – A Video Talk</a> appeared first on <a rel="nofollow" href="https://ucanalytics.com/blogs">YOU CANalytics | </a>.</p>]]></description> <wfw:commentRss>https://ucanalytics.com/blogs/artificial-intelligence-and-machine-learning-for-business-a-video-talk/feed/</wfw:commentRss> <slash:comments>1</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">11924</post-id> </item> <item> <title>Data Science Career – Q&A Session with Roopam</title> <link>https://ucanalytics.com/blogs/data-science-career-qa-session-with-roopam/</link> <comments>https://ucanalytics.com/blogs/data-science-career-qa-session-with-roopam/#respond</comments> <dc:creator><![CDATA[Roopam Upadhyay]]></dc:creator> <pubDate>Sun, 12 Apr 2020 11:30:52 +0000</pubDate> <category><![CDATA[Video Discussion]]></category> <guid isPermaLink="false">http://ucanalytics.com/blogs/?p=11916</guid> <description><![CDATA[<p>This question and answer (Q&A) session will explore these topics: Identification of career opportunities in data science, machine learning, and Artificial Intelligence for beginners What to expect while starting your career in data science? How to make your career transition to data science, as an experienced professional, a smooth endeavor? What are the right strategies</p><p><a class="excerpt-more blog-excerpt" href="https://ucanalytics.com/blogs/data-science-career-qa-session-with-roopam/">Read More...</a></p><p>The post <a rel="nofollow" href="https://ucanalytics.com/blogs/data-science-career-qa-session-with-roopam/">Data Science Career – Q&A Session with Roopam</a> appeared first on <a rel="nofollow" href="https://ucanalytics.com/blogs">YOU CANalytics | </a>.</p>]]></description> <wfw:commentRss>https://ucanalytics.com/blogs/data-science-career-qa-session-with-roopam/feed/</wfw:commentRss> <slash:comments>0</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">11916</post-id> </item> <item> <title>Convolutional Neural Networks (CNN) Simplified (Part 4)</title> <link>https://ucanalytics.com/blogs/convolutional-neural-networks-cnn-simplified-part-4/</link> <comments>https://ucanalytics.com/blogs/convolutional-neural-networks-cnn-simplified-part-4/#comments</comments> <dc:creator><![CDATA[Roopam Upadhyay]]></dc:creator> <pubDate>Sun, 31 Mar 2019 10:14:03 +0000</pubDate> <category><![CDATA[Deep Learning Neural Networks]]></category> <guid isPermaLink="false">http://ucanalytics.com/blogs/?p=11583</guid> <description><![CDATA[<p>Welcome back to the deep learning example to build an OCR application. The idea of this simple application is to identify numbers in an image of written text. In the last part, we used three different models and got the following accuracy for identification of the test images: Model 1 – Logistic regression: 92% accuracy</p><p><a class="excerpt-more blog-excerpt" href="https://ucanalytics.com/blogs/convolutional-neural-networks-cnn-simplified-part-4/">Read More...</a></p><p>The post <a rel="nofollow" href="https://ucanalytics.com/blogs/convolutional-neural-networks-cnn-simplified-part-4/">Convolutional Neural Networks (CNN) Simplified (Part 4)</a> appeared first on <a rel="nofollow" href="https://ucanalytics.com/blogs">YOU CANalytics | </a>.</p>]]></description> <wfw:commentRss>https://ucanalytics.com/blogs/convolutional-neural-networks-cnn-simplified-part-4/feed/</wfw:commentRss> <slash:comments>4</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">11583</post-id> </item> <item> <title>Deep Learning Models Simplified (Part 3)</title> <link>https://ucanalytics.com/blogs/deep-learning-models-simplified-part-3/</link> <comments>https://ucanalytics.com/blogs/deep-learning-models-simplified-part-3/#comments</comments> <dc:creator><![CDATA[Roopam Upadhyay]]></dc:creator> <pubDate>Mon, 29 Oct 2018 21:05:54 +0000</pubDate> <category><![CDATA[Deep Learning Neural Networks]]></category> <guid isPermaLink="false">http://ucanalytics.com/blogs/?p=11578</guid> <description><![CDATA[<p>Facebook was a major sensation and a source of great amusement in a British country house in the early 20th century. It was such a big hit that it got a special mention in a newspaper published in the year 1902. Facebook, then, of course, had a completely different meaning than the online social media we</p><p><a class="excerpt-more blog-excerpt" href="https://ucanalytics.com/blogs/deep-learning-models-simplified-part-3/">Read More...</a></p><p>The post <a rel="nofollow" href="https://ucanalytics.com/blogs/deep-learning-models-simplified-part-3/">Deep Learning Models Simplified (Part 3)</a> appeared first on <a rel="nofollow" href="https://ucanalytics.com/blogs">YOU CANalytics | </a>.</p>]]></description> <wfw:commentRss>https://ucanalytics.com/blogs/deep-learning-models-simplified-part-3/feed/</wfw:commentRss> <slash:comments>4</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">11578</post-id> </item> <item> <title>Math of Deep Learning Neural Networks – Simplified (Part 2)</title> <link>https://ucanalytics.com/blogs/math-of-deep-learning-neural-networks-simplified-part-2/</link> <comments>https://ucanalytics.com/blogs/math-of-deep-learning-neural-networks-simplified-part-2/#comments</comments> <dc:creator><![CDATA[Roopam Upadhyay]]></dc:creator> <pubDate>Mon, 01 Oct 2018 09:49:12 +0000</pubDate> <category><![CDATA[Deep Learning Neural Networks]]></category> <guid isPermaLink="false">http://ucanalytics.com/blogs/?p=11290</guid> <description><![CDATA[<p>Welcome back to this series of articles on deep learning and neural networks. In the last part, you learned how training a deep learning network is similar to a plumbing job. This time you will learn the math of deep learning. We will continue to use the plumbing analogy to simplify the seemingly complicated math. I</p><p><a class="excerpt-more blog-excerpt" href="https://ucanalytics.com/blogs/math-of-deep-learning-neural-networks-simplified-part-2/">Read More...</a></p><p>The post <a rel="nofollow" href="https://ucanalytics.com/blogs/math-of-deep-learning-neural-networks-simplified-part-2/">Math of Deep Learning Neural Networks – Simplified (Part 2)</a> appeared first on <a rel="nofollow" href="https://ucanalytics.com/blogs">YOU CANalytics | </a>.</p>]]></description> <wfw:commentRss>https://ucanalytics.com/blogs/math-of-deep-learning-neural-networks-simplified-part-2/feed/</wfw:commentRss> <slash:comments>6</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">11290</post-id> </item> <item> <title>Deep Learning and Neural Networks – Simplified (Part 1)</title> <link>https://ucanalytics.com/blogs/deep-learning-and-neural-networks-simplified-part-1/</link> <comments>https://ucanalytics.com/blogs/deep-learning-and-neural-networks-simplified-part-1/#comments</comments> <dc:creator><![CDATA[Roopam Upadhyay]]></dc:creator> <pubDate>Mon, 27 Aug 2018 14:57:23 +0000</pubDate> <category><![CDATA[Deep Learning Neural Networks]]></category> <guid isPermaLink="false">http://ucanalytics.com/blogs/?p=11156</guid> <description><![CDATA[<p>The entire field of artificial intelligence, in the last few years, is built upon deep learning or deep neural networks. Notably, Apple’s Siri, Google-DeepMinds’ AlphaGo, or the self-driving mechanism in Tesla cars are all based on deep learning. Here, my goal is to make deep learning neural networks much more accessible for everyone. In this series</p><p><a class="excerpt-more blog-excerpt" href="https://ucanalytics.com/blogs/deep-learning-and-neural-networks-simplified-part-1/">Read More...</a></p><p>The post <a rel="nofollow" href="https://ucanalytics.com/blogs/deep-learning-and-neural-networks-simplified-part-1/">Deep Learning and Neural Networks – Simplified (Part 1)</a> appeared first on <a rel="nofollow" href="https://ucanalytics.com/blogs">YOU CANalytics | </a>.</p>]]></description> <wfw:commentRss>https://ucanalytics.com/blogs/deep-learning-and-neural-networks-simplified-part-1/feed/</wfw:commentRss> <slash:comments>16</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">11156</post-id> </item> <item> <title>Machine Learning : Cross Validation and Hyper-Parameter Tuning (Part 3)</title> <link>https://ucanalytics.com/blogs/machine-learning-cross-validation-and-hyper-parameter-tuning-part-3/</link> <comments>https://ucanalytics.com/blogs/machine-learning-cross-validation-and-hyper-parameter-tuning-part-3/#comments</comments> <dc:creator><![CDATA[Roopam Upadhyay]]></dc:creator> <pubDate>Sun, 26 Aug 2018 11:39:06 +0000</pubDate> <category><![CDATA[Machine Learning and Artificial Intelligence]]></category> <category><![CDATA[Regularization and Cross Validation]]></category> <guid isPermaLink="false">http://ucanalytics.com/blogs/?p=10774</guid> <description><![CDATA[<p>In the last part of this series on fundamental machine learning, you learned about regularization and cross-validation. Here, you will gain a sound understanding of model hyper-parameter tuning to develop robust models. The machines do learn but they still need a good human tutor. In the last part, you were also introduced to my paternal grandmother to</p><p><a class="excerpt-more blog-excerpt" href="https://ucanalytics.com/blogs/machine-learning-cross-validation-and-hyper-parameter-tuning-part-3/">Read More...</a></p><p>The post <a rel="nofollow" href="https://ucanalytics.com/blogs/machine-learning-cross-validation-and-hyper-parameter-tuning-part-3/">Machine Learning : Cross Validation and Hyper-Parameter Tuning (Part 3)</a> appeared first on <a rel="nofollow" href="https://ucanalytics.com/blogs">YOU CANalytics | </a>.</p>]]></description> <wfw:commentRss>https://ucanalytics.com/blogs/machine-learning-cross-validation-and-hyper-parameter-tuning-part-3/feed/</wfw:commentRss> <slash:comments>2</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">10774</post-id> </item> <item> <title>Machine Learning : Regularization – Ridge, Lasso, & Elastic Net Simplified (Part 2)</title> <link>https://ucanalytics.com/blogs/machine-learning-regularization-ridge-lasso-elastic-net-simplified-part-2/</link> <comments>https://ucanalytics.com/blogs/machine-learning-regularization-ridge-lasso-elastic-net-simplified-part-2/#comments</comments> <dc:creator><![CDATA[Roopam Upadhyay]]></dc:creator> <pubDate>Sat, 28 Apr 2018 14:00:19 +0000</pubDate> <category><![CDATA[Machine Learning and Artificial Intelligence]]></category> <category><![CDATA[Regularization and Cross Validation]]></category> <guid isPermaLink="false">http://ucanalytics.com/blogs/?p=10717</guid> <description><![CDATA[<p>In the previous article, we started with the theme that overfitting is an inherent problem in machine learning associated with big data. Essentially, if you have many variables and their polynomial terms (X-variables) in a model you could fit any response data (y-variable) to perfection. This perfect fit for the observed data is overfitting since this model will</p><p><a class="excerpt-more blog-excerpt" href="https://ucanalytics.com/blogs/machine-learning-regularization-ridge-lasso-elastic-net-simplified-part-2/">Read More...</a></p><p>The post <a rel="nofollow" href="https://ucanalytics.com/blogs/machine-learning-regularization-ridge-lasso-elastic-net-simplified-part-2/">Machine Learning : Regularization – Ridge, Lasso, & Elastic Net Simplified (Part 2)</a> appeared first on <a rel="nofollow" href="https://ucanalytics.com/blogs">YOU CANalytics | </a>.</p>]]></description> <wfw:commentRss>https://ucanalytics.com/blogs/machine-learning-regularization-ridge-lasso-elastic-net-simplified-part-2/feed/</wfw:commentRss> <slash:comments>2</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">10717</post-id> </item> </channel></rss> If you would like to create a banner that links to this page (i.e. this validation result), do the following:
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