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    <title>Niall Martin</title>
    <link>https://niallmartin.io/</link>
    <description>Recent content on Niall Martin</description>
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      <title>Stock Market: Analysing and Predicting Trends</title>
      <link>https://niallmartin.io/posts/stock-market-analysing-and-predicting-trends/</link>
      <pubDate>Thu, 05 Dec 2019 00:00:00 +0000</pubDate>
      
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      <description></description>
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      <title>About Me</title>
      <link>https://niallmartin.io/about/</link>
      <pubDate>Sun, 03 Nov 2019 00:00:00 +0000</pubDate>
      
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      <description>I’m a data scientist from Ireland, with a doctorate in Machine Learning from the University of Cambridge. During my PhD, I specialised in the development of machine learning models to determine how vehicle manufacturers cheated fuel consumption standards. I’m a Fellow of the Insight Data Science program in Palo Alto, and a data scientist with Google Dublin.
I&#39;ve been lucky enough to follow my passion, and have a huge interest in machine learning, data analytics, methods to present data, and the role of AI in society.</description>
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      <title>Recommended Reading for Data Scientists</title>
      <link>https://niallmartin.io/posts/recommended-reading-for-data-scientists/</link>
      <pubDate>Fri, 24 May 2019 10:58:32 +0000</pubDate>
      
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      <description>Getting started in data science can be a daunting task. The area is broad, with little guidance on where one topic start and the next ends. Because of that, I&#39;ve pulled together this list of resources that particularly helped me when I was starting out.
I&#39;ve broken the links into the following four categories, each of which distinguishes between the different skills required data scientists:
 Data Analytics - Involves feature engineering and statistical tests.</description>
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      <title>Quotes</title>
      <link>https://niallmartin.io/quotes/</link>
      <pubDate>Wed, 31 Oct 2018 21:45:29 +0530</pubDate>
      
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      <description>Curated list of my favourite quotes, aphorisms, adages and maxims
 &amp;#34;We do not learn from experience... we learn from reflecting on experience.&amp;#34; John Dewey tags: life
 &amp;#34;All animals are equal but some animals are more equal than others.&amp;#34; Animal Farm, George Orwell tags: equality, society
 “Be the change that you wish to see in the world.” Mahatma Gandhi tags: motivation
 “Fill the unforgiving minute with sixty seconds&amp;#39; worth of distance run.</description>
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      <title>How to Start Learning Deep Learning</title>
      <link>https://niallmartin.io/posts/how-to-start-learning-deep-learning/</link>
      <pubDate>Wed, 23 May 2018 10:58:32 +0000</pubDate>
      
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      <description>The Basics Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it.
If you already have a basic understanding of linear algebra, calculus, probability and programming:
I recommend starting with Stanford’s CS231n. The course notes are comprehensive and written well.</description>
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      <title>The Simpler Derivation of Logistic Regression</title>
      <link>https://niallmartin.io/posts/the-simpler-derivation-of-logistic-regression/</link>
      <pubDate>Wed, 11 Jan 2017 10:58:32 +0000</pubDate>
      
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      <description>Main Derivation Logistic regression is one of the most popular ways to fit models for categorical data, especially for binary response data. It is the most important (and probably most used) member of a class of models called generalized linear models. Unlike linear regression, logistic regression can directly predict probabilities (values that are restricted to the (0,1) interval); furthermore, those probabilities are well-calibrated when compared to the probabilities predicted by some other classifiers, such as Naive Bayes.</description>
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      <title>Classification Model to Predict Loan Default</title>
      <link>https://niallmartin.io/posts/classification-model-to-predict-loan-default/</link>
      <pubDate>Fri, 19 Aug 2016 00:00:00 +0100</pubDate>
      
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      <description>As part of my Insight project, I choose to help Zidisha improve their default detection system. But wait, who is Zidisha?
Zidisha&amp;hellip; Zidisha is a non-profit startup that allows entrepreneurs in developing countries to request credit. Using a crowdsourcing financial model, individuals like Bineta are able to raise the necessary funds to grow their business. This approach allows entrepreneurs to develop sustainable businesses, and provides jobs for additional members in the community.</description>
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      <title>Building Classification Models</title>
      <link>https://niallmartin.io/posts/building-classification-models/</link>
      <pubDate>Thu, 11 Aug 2016 00:00:00 +0100</pubDate>
      
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      <description>Building Building on information presented in my previous post, this articles describes how bagging and boosted can be used to build a classification model. The data is available from Kaggle, and represents the financial transactions from two hundred thousand individuals (each with 800 features).
Using this dataset, I set about building a loan-default model. The overall pipeline for this project was split into a few stages, all of which are described below.</description>
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