The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.
Discover the opportunities and use cases of the open banking movement using Tink’s open platform. Tink, fintech leader in Europe in open banking services (aggregation and enrichment of bank data), opens its platform with a self-service format so that fintech and SMEs across Europe can take advantage of the potential that comes with the open banking movement.
“We set out to create a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; and (v) be complemented by a forum for interactive discussion of technical details and to answer questions”.
I wrote this book because: • ML is not a recipe. It is not a matter of knowing the syntax and mechanics of various software packages.• ML is an art, not a science. (Hence the title of this book). • One does not have to be a math whiz or know advanced math in orer to use ML effectively, but one does need to understand the concepts well — the Why? and How? of ML methods
A database housing more than 100 Colab notebooks running ML code for various NLP tasks. Colab is an excellent destination to experiment with the latest models as it comes with a free GPU/TPU housed in Google’s back-end servers… And a collection of more than 400 NLP datasets that it include papers.
