Our Favorite Posts Of Last Week (Apr 29, 2018)
Top KDnuggets tweets, Apr 1 : 4 : Top 20 Python #AI and #MachineLearning Open Source Projects; 7 Books to Grasp Mathematical Foundations of #DataScience
Most popular @KDnuggetsMost Retweeted: Top 20 Python #AI and #MachineLearning Open Source Projects https://t.co/YeQzx2aCx2 https://t.co/OV85HUzsTk Most Favorited: 7 Books to Grasp Mathematical Foundations of #DataScience and #MachineLearning https://t.co/o2gw1ZOoAJ https://t.
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Why I've lost faith in p values
This might not seem so bad. I'm still drawing the right conclusion over 90% of the time when I get a significant effect (assuming that I've done everything appropriately in running and analyzing my experiments).
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A visual introduction to machine learning
Let's revisit the 73-m elevation boundary proposed previously to see how we can improve upon our intuition. Clearly, this requires a different perspective.
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Blockchain Explained in 7 Python Functions
comments By Tom Cusack, Data Scientist in the Banking Sector I think for many people out there, Blockchain is this phenomenon, which is hard to get your head around.
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How to Train your Own Model with NLTK and Stanford NER Tagger? (for English, French, German…)
This guide shows how to use NER tagging for non-English languages with NLTK and Standford NER tagger. You can also use it to improve the Stanford NER Tagger.
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Local Search Analyst (SEO)
Do you love the challenge of SEO? Do you love details AND love people? Do you want to make a significant impact at work, instead of getting stuck in a faceless cubicle? Do you believe in the long term search engine optimization strategy to get ROI-producing results for clients?
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The ultimate guide to controlling Crawling and Indexing
Search engines crawl billions of pages every day. But they index fewer pages than this, and they show even fewer pages in their results. You want your pages to be among them. So, how do you take control of this whole process and improve your rankings?
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How to Use Correlation to Understand the Relationship Between Variables
There may be complex and unknown relationships between the variables in your dataset. It is important to discover and quantify the degree to which variables in your dataset are dependent upon each other.
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GluonNLP is a toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your Natural Language Processing (NLP) research. Make sure you have Python 2.7 or Python 3.6 and recent version of MXNet. You can install MXNet and GluonNLP using pip:
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