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Resources made for the Machine Learning Community's tracks.

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# MLRC
Code made for the Machine Learning Community's tracks.

### If you missed one of our lectures and want to catch up with other students, you have to finish the video lectures in the following table before attending the next lecture.

<table>
  <tr>
    <th>Week</th>
    <th>Compensation Materials</th>
  </tr>
  <tr>
    <td>1</td>
    <td><ul>
        <li>Chapter1 :: Hands on Machine Learning with Scikit-learn and Tensorflow</li>
        <li>Week 3 videos from this course https://www.coursera.org/learn/ml-clustering-and-retrieval</li>
        <li>SL1 and SL2 videos from this course https://www.udacity.com/course/machine-learning--ud262</li>
        <li>Chapters 1,2,3,4 from this repo https://github.com/jvns/pandas-cookbook/tree/v0.2/cookbook</li>
        <li>Reproduce this code https://www.kaggle.com/moghazy/week-1</li>
        </ul>
    </td>

</tr>
  <tr>
    <td>2</td>
    <td> <ul>
        <li>Chapter 3 Section Performance Measures :: Hands on Machine Learning with Scikit-learn and Tensorflow</li>
        <li>Weeks 1, 2 and 3 videos from this course https://www.coursera.org/learn/neural-networks-deep-learning</li>
        <li>SL1 and SL2 videos from this course https://www.udacity.com/course/machine-learning--ud262</li>
        <li>Try to reproduce the code from this kernel https://www.kaggle.com/moghazy/intro-to-dnns-with-data-augmentation-python</li>
        </ul>  
   </td>
  </tr>
  <tr>
    <td>3</td>
    <td>TBA</td>
  </tr>
  
</table>

MLRC

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