Deploying Large Scale Classification Algorithms for Attribute Prediction

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In our last post we talked about automated product attribute classification using advanced text based machine learning techniques using the given product features like title, description etc. & predicting product attribute values from the defined set of values. As discussed as the catalogue size and no. of suppliers keep growing the problem of maintaining the catalogue accurately grows exponentially and there are thousands of attribute values and millions of products per day to classify. In this post, we are going to highlight some of the keys steps we utilized to deploy machine learning algorithms to classify thousands of attributes and deploying them on dataX, CrowdANALYTIX's proprietary big data curation and veracity optimization platform. As shown in the figure below - client product catalog is extracted, curated and a list of products (new products which need classification or old product refreshes) is sent to dataX . The dataX ecosystem is designed to onboard millions of products each day to make high precision predictions.


Microsoft, Machine Learning, And "Data Wrangling": ML Leverages Business Intelligence For B2B

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"Data wrangling" was an interesting phrase to hear in the machine learning (ML) presentations at Microsoft Ignite. Interesting because data wrangling is from business intelligence (BI), not from artificial intelligence (AI). Microsoft understands ML incorporates concepts from both disciplines. Further discussions point to another key point: Microsoft understands that business-to-business (B2B) is just as fertile for ML as business-to-consumer (B2C). ML applications with the most press are voice, augmented reality and autonomous vehicles.


Software for Data Mining, Analytics,Data Science, and Knowledge Discovery

AITopics Original Links

Classification software: building models to separate 2 or more discrete classes using Multiple methods Decision Tree Rules Neural Bayesian SVM Genetic, Rough Sets, Fuzzy Logic and other approaches Analysis of results, ROC Social Network Analysis, Link Analysis, and Visualization software Text Analysis, Text Mining, and Information Retrieval (IR) Web Analytics and Social Media Analytics software. BI (Business Intelligence), Database and OLAP software Data Transformation, Data Cleaning, Data Cleansing Libraries, Components and Developer Kits for creating embedded data mining applications Web Content Mining, web scraping, screen scraping.


From Data Analysis to Machine Learning

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This article was originally posted here, by Mubashir Qasim. "In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization.


From Data Analysis to Machine Learning

#artificialintelligence

This article was originally posted here, by Mubashir Qasim. In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization.