Machine Learning with Scikit-learn

Objective

This tutorial will offer an introduction to the core concepts of machine learning, and how they can be easily applied in Python using Scikit-learn. We will use the scikit-learn API to introduce and explore the basic categories of machine learning problems, related topics such as feature selection and model validation, and the application of these tools to real-world data sets.

Description

Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python. It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.

The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn. This stack that includes:

1. NumPy: Base n-dimensional array package
2. SciPy: Fundamental library for scientific computing
3. Matplotlib: Comprehensive 2D/3D plotting
4. IPython: Enhanced interactive console
5. Sympy: Symbolic mathematics
6. Pandas: Data structures and analysis

This tutorial will focus on the concepts underlying Machine Learning algorithms like supervised, unsupervised and reinforcement with real life examples and its usages in Big Data field.

Requirements

This tutorial will require recent installations of numpy, scipy, matplotlib, scikit-learn, and ipython with ipython notebook.

For users who do not yet have these packages installed, a relatively painless way to install all the requirements is to use a distribution such as Anaconda CE, which includes all of the requirements, works on Mac, Linux, and Windows, and can be downloaded and installed for free.

Speaker bio

A junior at SSN College of Engineering with main experience in Python.

My research interests lie in artificial life, and artificial intelligence using computer science and statistics as tools to understand life and its numerous aspects.

I have done numerous open source projects which includes

1. FlaskBlog - An extensive tutorial on writing web applications in Python using the Flask microframework.
2. PyTransmit - With more than 2000 downloads per month, PyTransmit is a simple wrapper on top of the ftplib package which provides an object that can be used to make FTP calls to a PyTransmit installation.
3. HNScrapy - Hacker News Crawler based upon Scrapy which crawls the entire site and stores the links in the Database.
4. Pygram - Instagram-like image filters.

You can find the source code from my github profile.

You can contact me at contact@pypix.com

Links

• http://scikit-learn.org/stable/
• https://store.continuum.io/cshop/anaconda/

Comments

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  [-][+] Nar Kumar 274 days ago (edited 274 days ago)

  I just noticed description to this proposal is blatant copy from PyCon 2014 talk by Jake Vanderplas and Olivier Grisel https://us.pycon.org/2014/schedule/presentation/131/

  Not sure if it matters.

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    [-][+] Aravind Krishnaswamy 220 days ago

    This matters, and thanks for pointing it out. Plagiarism is a serious issue.

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  [-][+] Erwan Lent 276 days ago

  Looks like this would be a really interesting talk. Can't wait to see how it goes!

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  [-][+] Siddharth Kulshresth 267 days ago

  Looking forward to this one!

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