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What is NLP? Natural language processing explained

CIO

How natural language processing works NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. An NLP algorithm uses this data to find patterns and extrapolate what comes next. Google Cloud Translation.

Algorithm 889
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An Important Guide To Unsupervised Machine Learning

Smart Data Collective

Unsupervised ML uses algorithms that draw conclusions on unlabeled datasets. As a result, unsupervised ML algorithms are more elaborate than supervised ones, since we have little to no information or the predicted outcomes. Overall, unsupervised algorithms get to the point of unspecified data bits. Source ].

Learning 334
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Assisted Product Matching For eCommerce Companies using Python

Data Hut

Nobody has ever built product-matching algorithms that Completely automated product-matching. This concept is vital for several aspects of e-commerce operations, including price comparison, product recommendation, inventory management, and competitive analysis. Product matching for ecommerce is a hard problem to solve.

Matrix 52
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Python for Machine Learning: A Tutorial

IT Business Edge

Jump to: Machine Learning 101 Python Libraries and Tools Training a Machine Learning Algorithm with Python Using the Iris Flowers Dataset. You can use many different algorithms for machine learning. The choice of algorithm will depend on the problem you are trying to solve and the available data. Machine Learning 101.

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Customer Churn Model Using Smarten Assisted Predictive Modelling!

Elegant BI

Any kind of analysis initiates by looking upon the data. Keeping a note that we already have a predefined dataset uploaded to Smarten, let’s get started slowly but surely into how to open a loaded dataset in Smarten and make analysis. In order to select the best category of algorithm, users need to have some basic data literacy.

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Machine Learning Problems: The Easy Parts

Contify

As a programmer with data science background, my attention is invariably caught by the real-world situations where machine learning algorithms have made a difference, for example: email spam filtering, news categorization, review based recommendations, social media sentiments etc. just rephrased. It’s a constant learning process.

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Machine Learning Problems: The Easy Parts

Contify

As a programmer with data science background, my attention is invariably caught by the real-world situations where machine learning algorithms have made a difference, for example: email spam filtering, news categorization, review based recommendations, social media sentiments etc. just rephrased. It’s a constant learning process.