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.
We knew Azure is one of the fastest growing Cloud services around the world it helps developers and IT Professionals to create and manage their applications. When Azure HDInsight has huge success in Hadoop based technology, For Marketing Leaders in Big data Microsoft has taken another step and introduced Azure Machine Learning which is also called as "Azure ML". After the release of Azure ML, the developers feel easy to build applications and Azure ML run's under a public cloud by this user need not to download any external hardware or software. Azure Machine Learning is combined in the development environment which is renamed as Azure ML Studio. The main reason to introduce Azure ML to make users to create a data models without the help of data science background, In Azure ML Data models, are Created with end-to-end services were as ML Studio is used to build and test by using drag-and-drop and also we can deploy analytics solution for our data's too.
The global influence of Big Data is not only growing but seemingly endless. The trend is leaning towards knowledge that is attained easily and quickly from massive pools of Big Data. Today we are living in the technological world that Dr. Usama Fayyad and his distinguished research fellows discussed in the introductory explanations of Knowledge Discovery in Databases (KDD) predicted nearly two decades ago. Indeed, they were precise in their outlook on Big Data analytics. In fact, the continued improvement of the interoperability of machine learning, statistics, database building and querying fused to create this increasingly popular science- Data Mining and Knowledge Discovery. The next generation computational theories are geared towards helping to extract insightful knowledge from even larger volumes of data at higher rates of speed. As the trend increases in popularity, the need for a highly adaptive solution for knowledge discovery will be necessary. In this research paper, we are introducing the investigation and development of 23 bit-questions for a Metaknowledge template for Big Data Processing and clustering purposes. This research aims to demonstrate the construction of this methodology and proves the validity and the beneficial utilization that brings Knowledge Discovery from Big Data.
Earlier this week, I did a Facebook Live Code along session. In it, we used some basic Natural Language Processing to plot the most frequently occurring words in the novel Moby Dick. In this live post, you'll learn how to build a data science pipeline to plot frequency distributions of words in Moby Dick, among many other novels. We won't give you the novels: you'll learn to scrape them from the website Project Gutenberg (which basically contains a large corpus of books) using the Python package requests and how to extract the novels from this web data using BeautifulSoup. In the process, you'll learn about important aspects of Natural Language Processing (NLP) such as tokenization and stopwords.
Every once in a while an innocuous technical term suddenly enters public discourse with a bizarrely negative connotation. I first noticed the phenomenon some years ago, when I saw a Republican politician accusing Hillary Clinton of "parsing." From the disgust with which he said it, he clearly seemed to feel that parsing was morally equivalent to puppy-drowning. It seemed quite odd to me, since I'd only ever heard the word "parse" used to refer to the computer analysis of sentence structures. The most recent word to suddenly find itself stigmatized by Republicans (yes, it does somehow always seem to be Republican politicians who are involved in this particular kind of linguistic bullshittery) is "encryption."