The climate crisis is here. Everyone must adapt. If carried out thoughtfully, the ways in which rural and urban populations adapt to the climate crisis have the potential to reduce environmental impact in the long run. Hear from Sādu on the ways in which eco-conscious decision making can have a measurable impact on the climate […]
Feature Selection for Machine Learning by Soledad Galli When we build machine learning models with the aim to use them in production, we probably don’t want to use all the variables available in the data. Sure, adding more variables rarely makes a model less accurate, but there are certain disadvantages to including an excess of […]
Data Pre-processing and Feature Engineering for Machine Learning by Soledad Galli Data in its raw format is almost never suitable for use to train Machine Learning Models. In fact, Data scientists devote a big part of their time to clean and pre-process data. Feature engineering refers to the various processes and techniques that we can […]
Advanced Python Libraries for Data Science by Miguel Pereira Python is currently the most widely used programming language in the fields of machine learning, deep learning and AI. In-depth technical knowledge of machine learning python libraries is thus an indispensable part of the data scientist toolkit from beginner to expert level. This talk will cover […]
Introduction to Linear Algebra by Antonio Campello In this introductory session on linear algebra, we will explain the main linear algebra concepts and terminology behind some of the core data science algorithms. We will review the concepts of linear combination, vector representation, dot product, matrix decomposition and matrix factorisation. For each concept, we will provide […]
Bayesian Machine Learning by Egor Kraev In classical machine learning, one looks for the ‘best’ vector of model parameters to fit the data, leading to overfitting if one is not careful and requiring extra effort to understand model uncertainty. In contrast, in Bayesian machine learning one considers all possible parameter vectors compatible with the data […]
Introduction to Statistics by Charis Chanialidis Will a new drug decrease blood pressure? What is the probability that England will win the next world cup? Can I predict which customers are most likely to stop using my product? These are some of the questions that one can answer by taking advantage of Statistics. In this […]
Introduction to Data Science R by Helen Anstey & Thomas Constant This session will introduce the use of the R programming language for data science applications. It will cover the exploration and filtering of data, visualization, and the process of creating a simple classification model.
Introduction to GIT by Aaron Collis & Simeon Parris This session will introduce the concept of source control and detail the major benefits of using Git for source control in software development. It will also highlight many of its fundamental concepts and present a number of the commands that are specific to Git. Aaron and […]