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machinelearning.html
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<!DOCTYPE HTML>
<!--
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html5up.net | @ajlkn
Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
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<html>
<head>
<title>Machine Learning</title>
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<header id="header">
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<a href="index.html" class="logo">
<span class="symbol"><img src="images/logo.svg" alt="" /></span><span class="title">Elena Mendes</span>
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<h2>Menu</h2>
<ul>
<li><a href="index.html">Home</a></li>
<li><a href="generic.html">Understanding Artificial Intelligence</a></li>
<li><a href="generic.html">Numerical Analysis</a></li>
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<li><a href="machinelearning.html">Machine Learning</a></li>
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<h1>Machine Learning</h1>
<p>Using algorithms & statistical data to analyse data, uncover hidden patterns, and gain insights into data.</p>
<span class="image main"><img src="images/pic13.jpg" alt="" /></span>
<hr>
<p>Insert info here</p>
<hr>
<p><strong>Learning Outcomes</strong></p>
<ol>
<li>
Develop the understanding of key concepts of agent-based computing.
</li>
<li>
Understand the trends that have facilitated the development of agents as a paradigm.
</li>
<li>
Compare different types of agent-based systems to contrast their relative merits.
</li>
</ol>
<h2>Reflections</h2>
<p><strong>Introduction to Machine Learning (ML)</strong></p>
<p>I watched the lecture cast on machine learning, which aimed to provide an introduction to the topic. While the lecturecast covered various topics, including the regulation of big data, self-driving cars, and IoT, I found the presentation quite vague and disorganized. It seemed more like a collection of machine learning concepts rather than a cohesive overview.
<p>However, I did find the reading list to be more informative. I prefer short articles that can deliver key insights in a straightforward and concise manner, and I found this <a href="https://towardsdatascience.com/introduction-to-machine-learning-for-beginners-eed6024fdb08">article</a> by Ayush Pant quite interesting. </p>
<p> The other reading materials were a paper on <em><a href="https://www.ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.105.586495">Correlation and Regression</a></em>, a chapter in <em>Deep Learning & Social Computing</em>, and <em>An introduction to machine learning</em> Miroslav Kubát. While I understand that the complexity of machine learning requires understanding advanced concepts, I couldn't help but wonder if the reading list belonged to different units in the module.</p>
<p><strong>Exploratory Data Analysis</strong></p>
<p>Week 2 of the module seemed relaxed, and the reading list reflected the same. It was relatively easy and cohesive.
This week covered the Exploratory Data Analysis (EDA). I had never performed an exploratory data analysis before and was slightly intimidated. I had also failed to read through the week's recommended reading. After skimming through pages of online EDA tutorials, I happened to revert back to the recommended reading list, and I found <em><a href="https://medium.com/data-folks-indonesia/10-things-to-do-when-conducting-your-exploratory-data-analysis-eda-7e3b2dfbf812">10 Things to do when conducting your Exploratory Data Analysis (EDA)</a></em>. Lesson learned: always go through the reading lists before embarking on hour-long research.</p>
<p>To perform this EDA, I used Jupyter Notebook through the Conda environment (I have previously worked with these tools). You will find all the details of the EDA in my GitHub repository. This was a good week as it finally felt like the machine learning module I was looking forward to.</p>
<a href="https://github.com/ElenaM10/EDA-Auto-mpg.git" class="button brands style2 fa-github">EDA Auto-mpg Dataset</a>
<p><strong>Correlation and Regression</strong></p>
<p>Week 3 of the module, and we're diving into Correlation & Regression. I have grazed through this topic when I took the DeepLearning course by Andrew Ng but hadn't gotten into detail. Although the reading list for this week is only two sections from the Core book, I found myself having to re-read the topic. Thankfully, the <em><a href="https://www.youtube.com/watch?v=zM4VZR0px8Eo">Logistic Regression (Binary Classification) tutorial</a></em> was part of the required reading & I watched the EDA Seminar for week 2 (The Seminars were postponed by a week). </p>
<p>After conducting the correlation exercise in Jupyter, I want to improve my skill in interpreting correlation results. I need to research industry best practices regarding this interpretation.</p>
<a href="https://github.com/ElenaM10/Correlation-and-Regression.git">class="button brands style2 fa-github">Correlation & Regression</a>
<p><strong>Linear Regression with Scikit-Learn</strong></p>
<p>insert reflections</p>
<p><strong>Clustering with Python</strong></p>
<p>insert reflections</p>
<p><strong>Introduction to Artificial Neural Networks (ANNs)</strong></p>
<p>insert reflections</p>
<p><strong>Training an Artificial Neural Network</strong></p>
<p>insert reflections</p>
<p><strong>Introduction to Convolutional Neural Networks (CNNs)</strong></p>
<p>insert reflections</p>
<p><strong>CNN Interactive Learning</strong></p>
<p>insert reflections</p>
<p><strong>Model Selection and Evaluation</strong></p>
<p>insert reflections</p>
<p><strong> Industry 4.0 and Machine Learning</strong></p>
<p>insert reflections</p>
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