Where and How is AI Actually Being Adopted
Summary: Adoption of AI/ML by larger companies has more than doubled since last year according to these survey results from McKinsey and Stanford’s Human-Centered AI ...
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19 Great Articles About Natural Language Processing (NLP)
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, c...
Python machine learning libraries
Introduction This blog is a part of the learn machine learning coding basics in a weekend . We recommend the book Python Data Science Handbook by Jake VanderPlas There ar...
Math You Don't Need to Know for Machine Learning
Grab a copy of The Elements of Statistical Learning (“the machine learning bible”) and you might be a little overwhelmed by the mathematics. For example, thi...
How to Configure the Number of Layers and Nodes in a Neural Network
This article was written by Jason Brownlee. Artificial neural networks have two main hyperparameters that control the architecture or topology of the network: the numbe...
What Are Machine Learning Models Hiding?
This article was written by Vitaly Shmatikov. Machine learning is eating the world. The abundance of training data has helped ML achieve amazing results for object reco...
Top takeaways from R Studio conf 2019
Introduction During the most recent decade, the force originating from both the scholarly community and industry has lifted the R programming language. Also, they have wo...
IMPACT: Choose a domain which enables you to create scalable solutions to meaningful global problems
This is the first article in what will be a three-part series: “How to make your mark on the world as a talented, socially conscious data scientist.” In thi...
A Plethora of Original, Not Well-Known Statistical Tests
Many of the following statistical tests are rarely discussed in textbooks or in college classes, much less in data camps. Yet they help answer a lot of different and inte...
DSC Webinar Series: Creating Business Applications With R & Python
Across industries, data scientists are creating powerful models and analytics to solve urgent business problems. However, in far too many cases, these analytics never rea...