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<h2>Machine Learning</h2>
<p>The Machine Learning course offers students a comprehensive exploration of various topics essential to the field of data science. Throughout the course, students will gain knowledge and skills in probability and statistics, data mining, data preparation and cleaning, and image classification. The course begins with a strong foundation in probability and statistics. Moving on, students will delve into data mining, a crucial process of discovering patterns, relationships, and valuable information from vast amounts of data.The course also covers data preparation and cleaning, as these are crucial steps in the data analysis pipeline. Furthermore, the course includes a focus on image classification, a popular application of machine learning and computer vision.</p>
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<h3>Course's content</h3>
<p style="margin-left: 10px;">12 chapters</p>
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<div style="font-size: 18px; font-weight: bold;">Introduction</div>
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The introductory chapter to machine learning provides an overview of the fundamental concepts and techniques used in the field. It introduces key topics such as supervised and unsupervised learning, model evaluation, and the role of data in training machine learning models.
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<div style="font-size: 18px; font-weight: bold;">Statistics</div>
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The introductory chapter to machine learning provides an overview of the fundamental concepts and techniques used in the field. It introduces key topics such as supervised and unsupervised learning, model evaluation, and the role of data in training machine learning models.
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<div style="font-size: 18px; font-weight: bold;">Probability</div>
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The introductory chapter to machine learning provides an overview of the fundamental concepts and techniques used in the field. It introduces key topics such as supervised and unsupervised learning, model evaluation, and the role of data in training machine learning models.
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