Learn Machine Learning Workshop 5

PLEASE SIGN UP ON EVENTBRITE TO COME
PLEASE BRING A LAPTOP*
Eventbrite link: https://www.eventbrite.co.uk/e/learn-machine-learning-workshop-5-tickets-62639368036?ref=estw

This is a meet-up hold in partnership with Dataiku 's Analytics & Data Science meet-up: https://www.meetup.com/Analytics-Data-Science-by-Dataiku-London/events/261929355/

SCHEDULE

6:30: Arrivals + Networking + Pizza/Beer

7:00pm: Jaymin Mistry, data scientist at PA Consulting, 'Natural Language Processing'*** (20-25 min talk with a few minutes for Q&A).

7:30pm: Noha data scientist at dataiku, 'Practical ML session using wine dataset'. Noha will be on hand to help those who get stuck while working through the challenge. (PLEASE bring a laptop).*

8:30pm: End and more networking for anyone who wants to stick around

*** This talk will cover the basics of Natural Language processing: why and how you might use it to solve problems as a data scientist. It will then cover the key features of the spaCy package and why you might (like to) use it.

• You can alternatively work on the dataset from workshop 4 if you wish to continue with work you started then.

Who is this meetup for?

Ideally you have a background in software engineering, science or mathematics. All of these will make it easier to get stuck in solving the challenges.

What is the aim of this meet-up?

The aim of these meetups is to provide an environment for you to teach yourself machine learning.

Any ability is welcome. This is a self-lead learning course and is based on everyone helping each other.

Do I have to have been to the previous workshops to come along?

No.
We will be working in an environment set up by dataiku which will be very easy to get started with.

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Please register with your full, correct name, on Eventbrite to come along.

Please bring your laptop.

Please read and agree to the code of conduct BEFORE REGISTERING.:
https://www.meetup.com/Learn-machine-learning-london/pages/28057356/Code_of_Conduct/

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Machine learning workflow:

1st Define the problem, your goal and what success would look like.

2nd Collect your data. You should split your data into training data and test data. Remove the label you are trying to predict from your test data and use this data to test the accuracy of your model. In the case of Kaggle this is done for you.

3rd Exploratory analysis and data prep. Visualise your train data set through bar charts etc to try to gain an understanding of what factors are important in predicting your label. Clean both data sets.

4th Predictive model logic. This is your machine learning model. Use any libraries out there to help you write an accurate model. Use this to predict the labels for your test data set.

5th Evaluate the accuracy of your model. If using Kaggle submit your result to the challenge and you will get your % accuracy.

6th Optimise and Improve. Re-iterate over steps 3 and 4 until your get your desired accuracy level.