Machine Learning Workshop 4

A self-directed group set up for people who want to work with others to learn machine learning.

Learn Machine Learning and Work Through Kaggle Competitions Meetup

***PLEASE BRING A LAPTOP****
***SPACES ARE LIMITED SO PLEASE CANCEL YOUR TICKET IF YOU CANNOT MAKE IT***
 
Schedule (Subject to some change):
6.30pm: Arrive
6.40pm: Welcome Talk & Pizza.
6.45pm: Talk by Sky and Cedric, Data Scientists at Compare the Market, on on 'How to productionize Machine Learning models', the end to end of how to solve a ML challenge.
7.15pm: Intro to the challenge and how to work through a machine learning problem.
7.15 -8.45pm: Break into groups and work through the set challenge or tutorials.
Practical Work Options
1. Tutorials provided below.
2. Titanic Kaggle.
3. The ML challenge provided - more details to follow.<A HREF="https://www.kaggle.com/c/titanic" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"></A>
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. The idea is to do this by working through tutorials or Kaggle competitions.
You will split into groups depending on what you want to work on. You can work as a team, individually or as a pair in these groups.
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. The groups will be split based on which challenges you would like to work on.
If you have not been to any of the other workshops you would get more out of the meetup by setting up your environments before coming along. Set up: 
Python (ideally version 3.7) <A HREF="https://www.anaconda.com/distribution/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">using Anaconda</A>.
If you install it using the above link Jupyter Notebook will automatically be downloaded. You can check it has worked by navigating to the folder you would like to use for your challenge files and typing:
<DIV>jupyter notebook</DIV>
For more information on how to use Juputer Notebook you can follow <A HREF="https://www.codecademy.com/articles/how-to-use-jupyter-notebooks" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">this tutorial</A>.
*********************************************************************************
Please register with your full, correct name, on Eventbrite to come along. Please bring your laptop. Please read and agree to the <A HREF="https://www.meetup.com/Learn-machine-learning-and-Work-through-Kaggle-competitions/pages/28057356/Code_of_Conduct/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">code of conduct</A> BEFORE REGISTERING. *********************************************************************************
How to work through these problems
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. 
 Suggested Tutorials
Statistics 
Linear regression tutorials: 
- <A HREF="https://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Simple linear regression</A>.
Data exploration 
<A HREF="https://elitedatascience.com/python-seaborn-tutorial" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Python Seaborn tutorial</A>.  <A HREF="https://elitedatascience.com/python-seaborn-tutorial" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"> </A>
Machine Learning 
<A HREF="https://www.tutorialspoint.com/machine_learning_with_python/index.htm" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Basic machine learning and Python tutorial</A>. Beginner level: (Please note this is not done with Jupyter notebook) 
<A HREF="%20https://elitedatascience.com/python-machine-learning-tutorial-scikit-learn" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Python Scikit </A>(General-purpose machine learning library) tutorial.
<A HREF="https://www.datacamp.com/community/tutorials/kaggle-machine-learning-eda" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Tutorial on titanic Kaggle</A>. <A HREF="https://www.datacamp.com/community/tutorials/kaggle-machine-learning-eda" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"> </A>
Machine Learning courses (Recommended)
<A HREF="https://www.fast.ai/%20." TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Fast AI tutorials.</A> <A HREF="https://www.fast.ai/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"> </A>
Cheat Sheet
<A HREF="https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Full ML algorithms</A>. <A HREF="https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"> </A>
Concepts 
<A HREF="https://vas3k.com/blog/machine_learning/%20" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Blog on ML</A>.

BGL Group

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Machine Learning Workshop 4

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