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Get Started in Machine Learning with Python scikit-learn - London

The School of Data Science

Monday, 21 July 2014 at 09:00 - Friday, 25 July 2014 at 18:00 (BST)

Get Started in Machine Learning with Python...

Registration Information

Registration Type Sales End Price Fee Quantity
Special Offer - Last 3 seats Ended £1,300.00 £0.00
Charity/NGO/Public Sector Ended £1,100.00 £0.00
Startup/Student Ended £1,050.00 £0.00
Standard Ended £1,500.00 £0.00

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Event Details

Get Started in Machine Learning

with Python scikit-learn

[5 day bootcamp to learn basic building blocks of practical Machine Learning]

 

Overview

Why write programs when the computer can instead learn them from data? In this bootcamp you will learn how to make this happen. Though it has been an area of active research for over 50 years, Machine Learning is currently undergoing a renaissance driven by Moore's law and the rise of big data. Large private and public investment in the area has given us selfdriving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Computer based machine learning algorithms now outperform humans on tasks such as handwritten digit recognition, traffic sign recognition, and even on some complex reasoning tasks as demonstrated by IBM's Watson winning Jeopardy.

 

This 5 day bootcamp is designed to help you learn basic principles needed to understand and apply Machine Learning models and methods using Python Scikit-Learn. Lots of hands-on examples to step through real-world application of Machine Learning. Attending this bootcamp will enable you to understand the basic concepts, become confident in applying the tools and techniques, and provide a firm foundation from which to explore more advanced methods.

Who Should Attend 

You are interested in Machine Learning.  You have read a book or taken an online course and now want to know more and learn how to apply Machine Learning to solve real problems. Well-suited to machine learning beginners or those with some experience. 


Learning Outcomes 

Attend this 5 day bootcamp to learn the basic concepts, models and techniques required to perform practicle Machine Learning. 

DAY 1

Understand how the structure and function of the human brain is different from a computer and how this affects learning in each.

Define Machine Learning, why it matters, and discuss its relationship to data mining, data science, and statistics.

Understand the steps in the machine learning pipeline, from data acquisition and feature generation, to training and model selection.

Overview of core Machine Learning terminology i.e. features, instance, model selection, bias, variance, generalization, precision, etc.

Review of the fundamentals of linear algebra, calculus, statistics, and probability theory.

DAY 2

Doing Machine Learning - Review fundamentals of practical Machine Learning

  • Reading the data and cleaning it.
  • Exploring and understanding the input data.
  • Analysing how best to present the data to the learning algorithm.
  • Choosing the right model and learning algorithm.
  • Measuring the performance correctly.

Basics of Python programming language and environment.

Scientific Python building blocks and workflow

  • NumPy: Base n-dimensional array package
  • SciPy: Fundamental library for scientific computing
  • IPython: Enhanced interactive console
  • Pandas: Data structures and analysis

Overview of Scikit-learn: Machine Learning in Python

Our first Machine Learning Application - K Nearest Neighbours

Labs

  • Setting up the environment
  • Python programing basics (load data, simple histogram, select rows, columns, scatter plot, simple stats, ...)
  • Linear Regression 

DAY 3

Generally Applied Algorithms and Applications

  • Naive Bayes
  • Support Vector Machines
  • Logistic Regression
  • Decision Trees

Labs

  • Detecting Spam using Machine Learning
  • Predicting house prices with regression
  • Image recognition with Support Vector Machines

DAY 4

Dimensionality Reduction - Reducing the number of random variables to consider

  • Feature selection and feature extraction methods
  • Principal Component Analysis

Clustering - Automatic grouping of similar objects into sets.

  • Overview of clustering methods
  • Applications and Algorithms

Basics of Crab - Recommender systems in Python

BigML - Putting the power of Machine Learning in your hands

Labs

  • Dimensionality reduction practical example
  • Clustering handwritten digits with k-means

DAY 5

Model Selection and Evaluation in Scikit-learn - Comparing, validating and choosing parameters and models

Overview of Pre-processing in Scikit-learn - Feature extraction and normalization.

Putting it all together -  Final Kaggle Project

Current Hot Topics

  • Large scale Machine Learning
  • Deep Learning
  • Watson style learning - Cognative Analytics 
  • Probabilistic Programming
  • Machine Learning as a Service   

Prerequisites 

Basic understanding of calculus, statistics, probability theory, linear algebra. This will be refreshed but not in detail. Basic knowledge of python is required. All lab sessions will be done using IPython notebooks and Scikit-learn.

Venue

This course will be held at etc.venues Moorgate at Tenter House. Tenter House is located in the heart of the City of London’s Square Mile a few yards from Moorgate underground station and 5 minutes walk from Liverpool Street mainline railway station. Once at Tenter House – check in to Reception where they will direct you up to the eighth floor.

etc.venues Moorgate 

8th Floor Tenter House

45 Moorfields

London EC2Y 9AE

 

Refreshments

Breakfast and lunch will be provided along with refreshments.  Teas and coffees throughout the day. Special dietary needs can be catered for, but please let us know at least 48 hours before your course. 

Corporate Booking/Group Registrations

Registering your whole team? Please contact yazdaan [at] persontyle.com for corporate booking to avail following discounts. 

2 delegates - 10%
3 delegates - 15%
4 and more delegates - 20%

Persontyle Scholarship Program

We aim to offer two scholarships seats in all open enrollment courses. If you are a student or an individual interested in learning Data Science and for some reasons can’t afford to pay then this scholarship is for you. Please submit your application with your choice of course, location and reason why you should be awarded this scholarship for consideration to yazdaan [at] persontyle.com

Please note, scholarship only covers the course cost, all other expenses e.g. travel, and stay is not included.
 


 Note:  

  • Student Discount - Proof of enrollment (i.e. valid identification card or a copy of your transcript from the current semester) will be required to receive the discount. 
  • Non-profit Organization Discount - Proof of full time employment at a non-profit organization and subject to verification of non-profit/NGO status.
  • Startup Discount - Proof of full time employment at a startup company is required 
  • All related transaction fees PayPal and Eventbrite are not refundable
  • Discount offers cannot be combined

Disclaimer:

We have the right to cancel the event for any reason at any time. In the event that the course is cancelled or rescheduled, we will work with you to apply your registration to another date or refund your fee in full. We are not responsible for any travel related expenses incurred by attendees for this event. This includes but not limited to transportation, hotel accommodations or any other travel related expenses secured by the attendee, due to a cancellation on our part.

Cancellation Policy:

If you must cancel for any reason, please notify us via email at hello@persontyle.com. Refunds will only be issued for cancellations received at least 15 days prior to the course, and may take up to 2 weeks' to process. 

  • 30 or more days from the event date: Full refund less transaction fees
  • 16-29 days from the event date: 50% refund
  • 15 or less days from the event date: No refund
Do you have questions about Get Started in Machine Learning with Python scikit-learn - London? Contact The School of Data Science

When & Where


etc.venues Moorgate
8th Floor Tenter House
45 Moorfields
EC2Y 9AE London
United Kingdom

Monday, 21 July 2014 at 09:00 - Friday, 25 July 2014 at 18:00 (BST)


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Organiser

The School of Data Science

The School of Data Science is an education initiative to help meet the world’s demand for professionals and leaders skilled in digital technologies and automated and intelligent methods of using data as a strategic resource. For more information, contact us by email hello@persontyle.com 

  Contact the Organiser

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