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workshop-prerequisites.html
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<!DOCTYPE html>
<html lang="en">
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<link rel="canonical" href="https://in.pycon.org/2018/convince-my-boss.html">
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<meta name="description" content="PyCon India, the premier conference in India on using and developing the Python programming language is conducted annually by the Python developer community and it attracts the best Python programmers from across the country and abroad.">
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<title>PyCon India 2018 in Hyderabad | October 5th to 9th</title>
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<h2 class="small-caps center">Workshop Prerequisites</h2>
<p class="mb-4">
Below are the prerequisites detailed for the workshops either by a Keynote or a Sponsor. All other workshop details can be seen from the <a href="https://in.pycon.org/cfp/2018/proposals/"><strong>CFP</strong></a> itself.
</p>
<hr>
<section id="keynote1" class="workshops">
<div class="workshop-spacing"></div>
<h3>Jupyter hub and Binder</h3>
<h4>by Carol Wiling</h4>
<p class="mb-4">
<strong>Abstract -</strong>
We'll take an in-depth look at JupyterLab and its use for data science and scientific computing. We'll look at the new user interface functionality available to users of JupyterLab. Later, we will move beyond an individual's workflow with JupyterLab and see how JupyterHub brings JupyterLab to groups of users. We'll wrap up with a discussion of Binder and its ability to provide ephemeral computing environments for Jupyter notebooks and beyond.
</p>
</section>
<hr>
<h3>Sponsored Workshops</h3>
<hr>
<section id="sponsor1" class="workshops">
<h3>Understanding NLP - What's the feeling ?</h3>
<h4>By Lakshya Sivaramakrishnan (Google)</h4>
<p class="mb-4">
<strong>Abstract -</strong>
This intermediate level talk would cover the basics required for natural language processing using NLTK in Python. We would then see it working in action through a sentiment analysis use-case.
<br>
<strong>Prerequisites - </strong>
Basic knowledge of Python.
</p>
</section>
<hr>
<section id="sponsor2" class="workshops">
<h3>Programming a Quantum Computer using Cirq</h3>
<h4>by Vamsi Krishna Devabathini (Google)</h4>
<p class="mb-4">
<strong>Abstract -</strong>
This intermediate level workshop will focus on introduction to quantum computing and <a href="https://github.com/quantumlib/Cirq">Cirq</a>, python framework for creating quantum programming. It also includes a hands on tutorials implementing simple quantum algorithm(s) using Cirq.
<br>
<strong>Prerequisites - </strong>
Basic knowledge of Python. Knowledge of Quantum computing is a plus but not required.
</p>
</section>
<hr>
<section id="sponsor3" class="workshops">
<h3>Python ML On the Cloud</h3>
<h4>By Krishna Balaga (IBM)</h4>
<p class="mb-4">
<strong>Abstract -</strong>
A no tricks under the sleeve session with pure python at its heart for understanding and
Solving Machine Learning problems. This can be categorized as a beginner level workshop
on how to get the basics right. We start with a problem statement and then proceed with
obtaining the required dataset, getting it ready for our ML model, build and evaluate the
model and finally we teach you how to host your ML model on the cloud so that it's just an
API call away. By the end of the session, you will be able to scheme out a healthy approach
to any harder set of machine learning problems.
<br>
<strong>Agenda - </strong>
<ol>
<li>Introduction to Machine Learning
<ul style="list-style-type:disc">
<li>AI vs ML vs DL</li>
<li>Supervised vs Unsupervised</li>
<li>Ml Algorithms and its use cases</li>
</ul>
</li>
<li>Machine Learning Pipeline
<ul style="list-style-type:disc">
<li> Golden Rule of ML</li>
<li>Goals of Pre-processing
<ul style="list-style-type:circle">
<li>Handling missing data</li>
<li>Data Transformation</li>
<li>Outliers</li>
<li>Categorical Data</li>
</ul>
</li>
<li>Feature Extraction
<ul style="list-style-type:circle">
<li>Co-relation of Features</li>
<li>Dimensionality Reduction</li>
<li>PCA</li>
</ul>
</li>
<li>Model Training
<ul style="list-style-type:circle">
<li>Linear Regression</li>
<li>Decision Tree</li>
<li>Ensemble Models</li>
<li>Feedback and Deployment</li>
</ul>
</li>
</ul>
</li>
<li>Train a Logistic Regression Model (HANDS ON Lab)
<ul style="list-style-type:disc">
<li>Hands-on</li>
<li>Loading the data and connecting to Cloud Object Storage</li>
<li>Splitting the data, Feature Engineering and model fitment</li>
</ul>
</li>
<li>Model Evaluation and improvisation
<ul style="list-style-type:disc">
<li>ROC Analysis</li>
<li>Hyper-Parameter Optimization</li>
</ul>
</li>
<li>Exporting the Model as an API endpoint
<ul style="list-style-type:disc">
<li>Python notebook based code demonstration</li>
</ul>
</li>
</ol>
</p>
</section>
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