Add a page for workshop prerequisites and description about inpycon2018 HOT 6 CLOSED

Jupyter hub and Binder by Carol Wiling

Workshop Details:

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.

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Programming a Quantum Computer using Cirq by Vamsi Krishna Devabathini (Google)

Abstract: This intermediate level workshop will focus on introduction to quantum computing and Cirq, python framework for creating quantum programming. It also includes a hands on tutorials implementing simple quantum algorithm(s) using Cirq.

Prerequisites: Basic knowledge of Python. Knowledge of Quantum computing is a plus but not required.

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Understanding NLP - What's the feeling ? By Lakshya Sivaramakrishnan (Google)

Abstract: 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.

Pre-requisites: Basic knowledge of Python

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Python ML On the Cloud By Krishna Balaga (IBM)

Abstract:

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.

Pyconf Agenda:

1. Introduction to Machine Learning

   a. AI vs ML vs DL
   b. Supervised vs Unsupervised
   c. Ml Algorithms and its use cases

2. Machine Learning Pipeline

   a. Golden Rule of ML
   b. Goals of Pre-processing

    i. Handling missing data
    ii. Data Transformation
    iii. Outliers
    iv. Categorical Data
   

   c. Feature Extraction

    i. Co-relation of Features
    ii. Dimensionality Reduction
    iii. PCA
   

   d. Model Training

    i. Linear Regression
    ii. Decision Tree
    iii. Ensemble Models
    iv. Feedback and Deployment
   

3. Train a Logistic Regression Model (HANDS ON Lab)

   a. Hands-on
   b. Loading the data and connecting to Cloud Object Storage
   c. Splitting the data, Feature Engineering and model fitment

4. Model Evaluation and improvisation

   a. ROC Analysis
   b. Hyper-Parameter Optimization

5. Exporting the Model as an API endpoint

   a. Python notebook based code demonstration

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@ananyo2012 on it.

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@ananyo2012 any word on description or prerequisites of "Scaling Python up and out with Numba and Dask" by "Travis Oliphant"?

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