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        <title><![CDATA[Stories by Idil Ismiguzel on Medium]]></title>
        <description><![CDATA[Stories by Idil Ismiguzel on Medium]]></description>
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            <title>Stories by Idil Ismiguzel on Medium</title>
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        <item>
            <title><![CDATA[Enhance Content Moderation for ChatGPT with OpenAI’s Moderation API]]></title>
            <description><![CDATA[<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/data-science/enhance-content-moderation-with-openais-moderation-api-bb0b865d883b?source=rss-6d965c736f2------2"><img src="https://cdn-images-1.medium.com/max/2600/0*DY2v0OS3Y-B0vOdF" width="3913"></a></p><p class="medium-feed-snippet">Seamlessly integrate a moderation endpoint into your pipelines with ChatGPT</p><p class="medium-feed-link"><a href="https://medium.com/data-science/enhance-content-moderation-with-openais-moderation-api-bb0b865d883b?source=rss-6d965c736f2------2">Continue reading on TDS Archive »</a></p></div>]]></description>
            <link>https://medium.com/data-science/enhance-content-moderation-with-openais-moderation-api-bb0b865d883b?source=rss-6d965c736f2------2</link>
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            <category><![CDATA[large-language-models]]></category>
            <category><![CDATA[chatgpt]]></category>
            <category><![CDATA[python]]></category>
            <category><![CDATA[ai]]></category>
            <category><![CDATA[content-moderation]]></category>
            <dc:creator><![CDATA[Idil Ismiguzel]]></dc:creator>
            <pubDate>Fri, 07 Jul 2023 04:47:28 GMT</pubDate>
            <atom:updated>2023-08-03T08:27:00.192Z</atom:updated>
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            <title><![CDATA[Mastering Prompt Engineering to Unleash ChatGPT’s Potential]]></title>
            <description><![CDATA[<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/data-science/mastering-prompt-engineering-to-unleash-chatgpts-potential-9578a3fe799c?source=rss-6d965c736f2------2"><img src="https://cdn-images-1.medium.com/max/2600/0*DnZwVEOWJkaFzXE9" width="6000"></a></p><p class="medium-feed-snippet">Explore best practices and enhance your prompts for better results</p><p class="medium-feed-link"><a href="https://medium.com/data-science/mastering-prompt-engineering-to-unleash-chatgpts-potential-9578a3fe799c?source=rss-6d965c736f2------2">Continue reading on TDS Archive »</a></p></div>]]></description>
            <link>https://medium.com/data-science/mastering-prompt-engineering-to-unleash-chatgpts-potential-9578a3fe799c?source=rss-6d965c736f2------2</link>
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            <category><![CDATA[data-science]]></category>
            <category><![CDATA[prompt-engineering]]></category>
            <category><![CDATA[ai]]></category>
            <category><![CDATA[python]]></category>
            <category><![CDATA[chatgpt]]></category>
            <dc:creator><![CDATA[Idil Ismiguzel]]></dc:creator>
            <pubDate>Sat, 24 Jun 2023 05:50:37 GMT</pubDate>
            <atom:updated>2023-07-07T07:14:24.823Z</atom:updated>
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        <item>
            <title><![CDATA[Understanding Gradient Descent for Machine Learning]]></title>
            <description><![CDATA[<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/data-science/understanding-gradient-descent-for-machine-learning-246e324c229?source=rss-6d965c736f2------2"><img src="https://cdn-images-1.medium.com/max/2600/0*QwxbG48p-LLNRjEa" width="5472"></a></p><p class="medium-feed-snippet">A deep dive into Batch, Stochastic, and Mini-Batch Gradient Descent algorithms using Python</p><p class="medium-feed-link"><a href="https://medium.com/data-science/understanding-gradient-descent-for-machine-learning-246e324c229?source=rss-6d965c736f2------2">Continue reading on TDS Archive »</a></p></div>]]></description>
            <link>https://medium.com/data-science/understanding-gradient-descent-for-machine-learning-246e324c229?source=rss-6d965c736f2------2</link>
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            <category><![CDATA[data-science]]></category>
            <category><![CDATA[deep-dives]]></category>
            <category><![CDATA[optimization]]></category>
            <category><![CDATA[python]]></category>
            <category><![CDATA[machine-learning]]></category>
            <dc:creator><![CDATA[Idil Ismiguzel]]></dc:creator>
            <pubDate>Sun, 21 May 2023 15:42:50 GMT</pubDate>
            <atom:updated>2023-05-21T15:42:50.215Z</atom:updated>
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        <item>
            <title><![CDATA[A Guide to Association Rule Mining]]></title>
            <description><![CDATA[<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/data-science/a-guide-to-association-rule-mining-96c42968ba6?source=rss-6d965c736f2------2"><img src="https://cdn-images-1.medium.com/max/2600/0*ap-ZMb3c6aIRhPFb" width="5184"></a></p><p class="medium-feed-snippet">Create insights from frequent patterns using market basket analysis with Python</p><p class="medium-feed-link"><a href="https://medium.com/data-science/a-guide-to-association-rule-mining-96c42968ba6?source=rss-6d965c736f2------2">Continue reading on TDS Archive »</a></p></div>]]></description>
            <link>https://medium.com/data-science/a-guide-to-association-rule-mining-96c42968ba6?source=rss-6d965c736f2------2</link>
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            <category><![CDATA[machine-learning]]></category>
            <category><![CDATA[recommendation-system]]></category>
            <category><![CDATA[association-rule]]></category>
            <category><![CDATA[data-science]]></category>
            <category><![CDATA[hands-on-tutorials]]></category>
            <dc:creator><![CDATA[Idil Ismiguzel]]></dc:creator>
            <pubDate>Wed, 05 Apr 2023 14:03:47 GMT</pubDate>
            <atom:updated>2023-06-05T18:32:26.785Z</atom:updated>
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            <title><![CDATA[Hands-On Topic Modeling with Python]]></title>
            <description><![CDATA[<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/data-science/hands-on-topic-modeling-with-python-1e3466d406d7?source=rss-6d965c736f2------2"><img src="https://cdn-images-1.medium.com/max/2600/0*SWoPNOOJPlx4jtEd" width="5184"></a></p><p class="medium-feed-snippet">A tutorial on topic modeling using Latent Dirichlet Allocation (LDA) and visualization with pyLDAvis</p><p class="medium-feed-link"><a href="https://medium.com/data-science/hands-on-topic-modeling-with-python-1e3466d406d7?source=rss-6d965c736f2------2">Continue reading on TDS Archive »</a></p></div>]]></description>
            <link>https://medium.com/data-science/hands-on-topic-modeling-with-python-1e3466d406d7?source=rss-6d965c736f2------2</link>
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            <category><![CDATA[nlp]]></category>
            <category><![CDATA[data-science]]></category>
            <category><![CDATA[data-visualization]]></category>
            <category><![CDATA[machine-learning]]></category>
            <category><![CDATA[hands-on-tutorials]]></category>
            <dc:creator><![CDATA[Idil Ismiguzel]]></dc:creator>
            <pubDate>Wed, 14 Dec 2022 21:18:19 GMT</pubDate>
            <atom:updated>2022-12-14T21:18:19.073Z</atom:updated>
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        <item>
            <title><![CDATA[Outlier Detection with Simple and Advanced Techniques]]></title>
            <description><![CDATA[<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/data-science/detecting-outliers-with-simple-and-advanced-techniques-cb3b2db60d03?source=rss-6d965c736f2------2"><img src="https://cdn-images-1.medium.com/max/2600/0*HD2QNGmyckBibnJV" width="4096"></a></p><p class="medium-feed-snippet">A tutorial on how to detect outliers using standard deviation, interquartile range, isolation forest, DBSCAN, and local outlier factor</p><p class="medium-feed-link"><a href="https://medium.com/data-science/detecting-outliers-with-simple-and-advanced-techniques-cb3b2db60d03?source=rss-6d965c736f2------2">Continue reading on TDS Archive »</a></p></div>]]></description>
            <link>https://medium.com/data-science/detecting-outliers-with-simple-and-advanced-techniques-cb3b2db60d03?source=rss-6d965c736f2------2</link>
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            <category><![CDATA[machine-learning]]></category>
            <category><![CDATA[outliers]]></category>
            <category><![CDATA[python]]></category>
            <category><![CDATA[data-mining]]></category>
            <category><![CDATA[data-science]]></category>
            <dc:creator><![CDATA[Idil Ismiguzel]]></dc:creator>
            <pubDate>Thu, 17 Nov 2022 16:40:47 GMT</pubDate>
            <atom:updated>2022-12-07T10:45:50.242Z</atom:updated>
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        <item>
            <title><![CDATA[Imputing Missing Data with Simple and Advanced Techniques]]></title>
            <description><![CDATA[<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/data-science/imputing-missing-data-with-simple-and-advanced-techniques-f5c7b157fb87?source=rss-6d965c736f2------2"><img src="https://cdn-images-1.medium.com/max/2600/0*hD9Wfz3fHuvjte0K" width="5472"></a></p><p class="medium-feed-snippet">A tutorial on mean, mode, time series, KNN, and MICE imputation</p><p class="medium-feed-link"><a href="https://medium.com/data-science/imputing-missing-data-with-simple-and-advanced-techniques-f5c7b157fb87?source=rss-6d965c736f2------2">Continue reading on TDS Archive »</a></p></div>]]></description>
            <link>https://medium.com/data-science/imputing-missing-data-with-simple-and-advanced-techniques-f5c7b157fb87?source=rss-6d965c736f2------2</link>
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            <category><![CDATA[data-science]]></category>
            <category><![CDATA[python]]></category>
            <category><![CDATA[machine-learning]]></category>
            <category><![CDATA[hands-on-tutorials]]></category>
            <category><![CDATA[scikit-learn]]></category>
            <dc:creator><![CDATA[Idil Ismiguzel]]></dc:creator>
            <pubDate>Thu, 12 May 2022 04:57:42 GMT</pubDate>
            <atom:updated>2022-05-12T04:57:42.320Z</atom:updated>
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        <item>
            <title><![CDATA[Hyperparameter Tuning with Grid Search and Random Search]]></title>
            <description><![CDATA[<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/data-science/hyperparameter-tuning-with-grid-search-and-random-search-6e1b5e175144?source=rss-6d965c736f2------2"><img src="https://cdn-images-1.medium.com/max/2600/0*nL_xScrIqOriYBjZ" width="6000"></a></p><p class="medium-feed-snippet">And a deep dive into how to combine them</p><p class="medium-feed-link"><a href="https://medium.com/data-science/hyperparameter-tuning-with-grid-search-and-random-search-6e1b5e175144?source=rss-6d965c736f2------2">Continue reading on TDS Archive »</a></p></div>]]></description>
            <link>https://medium.com/data-science/hyperparameter-tuning-with-grid-search-and-random-search-6e1b5e175144?source=rss-6d965c736f2------2</link>
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            <category><![CDATA[python]]></category>
            <category><![CDATA[data-science]]></category>
            <category><![CDATA[machine-learning]]></category>
            <category><![CDATA[artificial-intelligence]]></category>
            <category><![CDATA[education]]></category>
            <dc:creator><![CDATA[Idil Ismiguzel]]></dc:creator>
            <pubDate>Wed, 29 Sep 2021 18:06:55 GMT</pubDate>
            <atom:updated>2023-03-29T08:46:58.981Z</atom:updated>
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            <title><![CDATA[Practical Guide to Ensemble Learning]]></title>
            <description><![CDATA[<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/data-science/practical-guide-to-ensemble-learning-d34c74e022a0?source=rss-6d965c736f2------2"><img src="https://cdn-images-1.medium.com/max/2400/0*4PEmHFnOiFbBLnou" width="2400"></a></p><p class="medium-feed-snippet">Improve your model with voting, bagging, boosting and stacking</p><p class="medium-feed-link"><a href="https://medium.com/data-science/practical-guide-to-ensemble-learning-d34c74e022a0?source=rss-6d965c736f2------2">Continue reading on TDS Archive »</a></p></div>]]></description>
            <link>https://medium.com/data-science/practical-guide-to-ensemble-learning-d34c74e022a0?source=rss-6d965c736f2------2</link>
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            <category><![CDATA[technology]]></category>
            <category><![CDATA[python]]></category>
            <category><![CDATA[artificial-intelligence]]></category>
            <category><![CDATA[machine-learning]]></category>
            <category><![CDATA[education]]></category>
            <dc:creator><![CDATA[Idil Ismiguzel]]></dc:creator>
            <pubDate>Fri, 30 Jul 2021 08:08:06 GMT</pubDate>
            <atom:updated>2021-09-28T15:54:22.222Z</atom:updated>
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        <item>
            <title><![CDATA[Hands-on Survival Analysis with Python]]></title>
            <description><![CDATA[<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/data-science/hands-on-survival-analysis-with-python-270fa1e6fb41?source=rss-6d965c736f2------2"><img src="https://cdn-images-1.medium.com/max/2600/1*Zqk9SXBRdwmuaxd_phazag.jpeg" width="4240"></a></p><p class="medium-feed-snippet">What companies can learn from employee turnover data</p><p class="medium-feed-link"><a href="https://medium.com/data-science/hands-on-survival-analysis-with-python-270fa1e6fb41?source=rss-6d965c736f2------2">Continue reading on TDS Archive »</a></p></div>]]></description>
            <link>https://medium.com/data-science/hands-on-survival-analysis-with-python-270fa1e6fb41?source=rss-6d965c736f2------2</link>
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            <category><![CDATA[artificial-intelligence]]></category>
            <category><![CDATA[python]]></category>
            <category><![CDATA[data-science]]></category>
            <category><![CDATA[innovation]]></category>
            <category><![CDATA[education]]></category>
            <dc:creator><![CDATA[Idil Ismiguzel]]></dc:creator>
            <pubDate>Sat, 03 Jul 2021 22:23:34 GMT</pubDate>
            <atom:updated>2021-09-28T15:55:22.160Z</atom:updated>
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