Applied Intro to Machine Learning
This workshop will cover some of the tools and workflow for getting started with applied ML. Attendees will learn how to use jupyter notebooks, and the scientific python ecosystem to experiment with machine learning.
- Create and run jupyter notebooks, virtual lab notebooks for interactive, reproducible python programming
- Learn how to use the SkLearn implementations of common machine learning algorithms
- How to visualise results with matplotlib
- A high level understanding of fundamental machine learning concepts: supervised vs unsupervised learning, classification vs regression, cross validation, loss minimization, hyper parameter search
- Pointers to resources for further learning
This workshop assumes:
- Basic knowledge of Python
Set up instructions
The easy way
The easy way of getting everything you need for this workshop is by the way of installing Anaconda.
The less easy way
Alternatively, you can manually install the following components.
Contributors: Tom Brady, Tom Read Cutting
Thanks to: Jared Khan
View code examples on GitHub
Applied Intro to Machine Learning Workshop
This repository will present an applied introduction to machine learning.
- What is "ML"
- Regression vs Classification
Applied example which shows, via a Jupyter notebook:
- Using KNN Classifier to classify images of handwritten digits
- Cross Validation
- Hyperparameter search
- Model performance visualization
Grabbing the Machine learning code
The majority of this workshop will be taught through the use of a Jupyter notebook, which you can download from here. Everything you need is in the
Alternatively, you can clone the repository using Git:
git clone https://github.com/hackersatcambridge/workshop-python-ml