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    <title>Linear Algebra Concepts for Data Science and Machine Learning</title>
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    <description>This blog explains the mathematics and theory behind key classical machine learning algorithms: Linear Regression, Logistic Regression, k-NN, Naive Bayes, and Decision Trees.</description>
    <pubDate>Wed, 28 Aug 2019 00:00:00 GMT</pubDate>
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    <title>Derivatives, Partial Derivatives, Vector and Matrix Calculus</title>
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    <pubDate>Wed, 30 Oct 2019 00:00:00 GMT</pubDate>
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    <title>Integration Techniques and Numerical Integration for Machine Learning</title>
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    <description>Computers use numerical methods to estimate integrals because real-world data is often discrete, and general-purpose algorithms are required to handle arbitrary functions. This blog covers deterministic methods for 1D integration, such as the trapezoidal and Simpsons rules, as well as Monte Carlo methods for high-dimensional or complex domains.</description>
    <pubDate>Sat, 17 Aug 2019 00:00:00 GMT</pubDate>
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    <title>A Sample of Probability Distributions and Their Properties</title>
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    <pubDate>Fri, 15 May 2020 00:00:00 GMT</pubDate>
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    <title>Understanding and Quantifying Uncertainties Related to Random Events</title>
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    <description>TDA.</description>
    <pubDate>Tue, 17 Dec 2019 00:00:00 GMT</pubDate>
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