About the camp
Intro to Machine Learning is a camp focusing on the basic algorithms of supervised and unsupervised machine learning. Workshops will deal with hands-on and small end-to-end projects using the popular Python library scikit-learn.
We will guide you through the steps of training and evaluating different machine learning models using multiple datasets. Our goal is to inspire and give you a good foundation to continue learning more about the field. The camp is perfect for you who wants to take your first step into the world of machine learning.
Before, in between and after the coding sessions there will be the opportunity to do online yoga together, watch movies together, cook-along and listen to inspirational talks from the teachers and mentors. Despite being online, the camp will be a social occasion to learn together with others and get a network within the Machine Learning field.
For each day of the camp, we will be programming approximately between 9 am and 4 pm. Both before and after there will be other social or recreational activities to do.
To get the most out of the camp it is great if you have basic knowledge in Python or a similar language. Basic knowledge includes variables and data types, lists, dictionaries and for loops, conditional statements and functions. It's also nice, but not necessary, if you know Pandas, matplotlib/seaborn, classes and objects.
If you do not meet the requirements, don’t worry. Then we suggest you to prepare by going through a tutorial before the camp. There are plenty of tutorials on the internet, so choose your favourite one or take a look at Codecademy or Udacity.
Expected learning outcomes
- A basic introduction about machine learning concepts (e.g. supervised vs. unsupervised) and solve some problems using Python.
- Real world application of ML
- Intro to classification algorithms and application with Iris data and classifying breast cancer tissue
- Regression algorithms and application on predicting wine quality and advertising sales based on social channels
- Getting familiar with clustering algorithms for discovering unknown patterns in data
- ML ethics such as responsibility and explicability of ML models