Advanced Time Series for Everyone
Mar 07, 2021
Large scale availability of time varying data originating in financial, economical and social systems has led to a rise in the interest in this class of algorithms. Understanding how to model and describe time series information is a fundamental skill in any data scientists toolkit.
In this lecture, we will introduce you to the libraries that Python provides for working with time series data. We’ll quickly review the ARIMA class of models before diving in detail into the more advance ARCH class of forecasting models. Both real and synthetic datasets will be used to illustrate the different kinds of models and their underlying assumptions.
Natural Language Processing (NLP) For Everyone
Apr 20, 2021
The rise of online social platforms has resulted in an explosion of written text in the form of blogs, posts, tweet, wiki pages, etc. This new wealth of data provides a unique opportunity to explore natural language in its many forms, both as a way of automatically extracting information from written text and as a way of artificially producing text that looks natural.
In this video we will introduce viewers to natural language processing from scratch. Each concept is introduced and explained through coding examples using nothing more than just plain Python and numpy. In this way, viewers will learn in depth about the underlying concepts and techniques instead of just learning how to use a specific NLP library.
NLP with Deep Learning for Everyone
Apr 27, 2021
Natural Language lies at the heart of current developments in Artificial Intelligence, User Interaction and Information Processing. The combination of unprecedented corpora of written text provided by Social Media and the massification of computational power has led to increased interest in the development of modern NLP tools based on state-of-the-art Deep Learning tools.
In this lecture, we will introduce participants to the fundamental concepts’ algorithms used for Natural Language Processing through an in-depth exploration of different examples built using the Keras Python framework for Deep Learning. Applications to real datasets will be explored in detail.