This workshop focuses on the implementation of machine learning algorithms
using libraries such as scikit-learn. Students will tackle real world
issues and apply machine learning concepts in order to solve them.
This workshop will give students an idea on how to identify which algorithm fits
a given dataset, compare various models based on their error rate,
and gain practical experience on different phases of machine learning,
right from data cleaning to predictions.

**What will you learn while developing a project using Machine Learning ?**

- - Recognize problems that can be solved with Machine Learning.
- - Select the right technique (is it a classification problem? a regression? needs preprocessing?).
- - Load and manipulate data with Pandas.
- - Visualize and explore data with Matplotlib.
- - Build regression, classification and clustering models with Scikit-Learn.
- - Evaluate model performance with Scikit-Learn.
- - Build, train and serve a predictive model using Python.

The goto project to get started with Machine learning. In this project, predict which passengers survived the tragedy.

Breast cancer affects about 10% of all women at some stages of their life. Data show that the survival rate is 88% after five years from diagnosis and 80% after 10 years from diagnosis. With Machine Learning, it is possible to predict breast cancer recurrence.

We expect students to have a good understanding of python and basic machine learning concepts such as classification and regression. We also conduct workshops exclusively for these concepts.