<div>
  <div>MLlib is Apache Spark&#39;s scalable machine learning library. Today, Ben and Michael discuss the ease of use, performance, algorithms, and utilities included in this library and how to execute the best ML workflow with MLlib.<br><br>
</div><div>
<strong>In this episode...<br></strong><br>
</div><ol>
<li>Why stick with Spark libraries vs. a single node operation?</li>
<li>What algorithms are not in Spark Lib?</li>
<li>What is the min. package set to use for supervised learning?</li>
<li>Modeling and validation</li>
<li>Down-sampling your data</li>
<li>MLlib vs. scikit-learn</li>
<li>Resources</li>
</ol><div>
<br><strong>Sponsors<br></strong><br>
</div><ul>
<li><a href="https://topenddevs.com/" rel="nofollow">Top End Devs</a></li>
<li><a href="https://topenddevs.com/coaching" rel="nofollow">Coaching | Top End Devs</a></li>
</ul><div>
<br><strong>Links<br></strong><br>
</div><ul>
<li><a href="https://spark.apache.org/mllib/" rel="nofollow">MLlib | Apache Spark</a></li>
<li><a href="https://www.dominodatalab.com/data-science-dictionary/pyspark#:~:text=PySpark%20is%20the%20Python%20API,more%20scalable%20analyses%20and%20pipelines" rel="nofollow">What is PySpark? | Domino Data Science Dictionary</a></li>
<li><a href="https://archive.ics.uci.edu/ml/index.php" rel="nofollow">UCI Machine Learning Repository</a></li>
</ul>
</div>

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Exploratory Data Analysis (EDA) in Machine Learning - ML 075

Machine Learning is growing in leaps and bounds both in capability and adoption. Listen to our experts discuss the ideas and fundamentals needed to succeed as a Machine Learning Engineer.Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.