The Complete Python Course for Machine Learning Engineers

@machinelearnbot

"I took a few of your courses and you are an amazing teacher. Your courses have brought me up to speed on how to create databases and how to interact and handle Data Engineers and Data Scientists. I will be forever grateful." "By taking this course my perception has changed and now data science for me is more about data wrangling. Welcome to The Complete Course for Machine Learning Engineers.


Deploying Large Scale Classification Algorithms for Attribute Prediction

#artificialintelligence

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.


Approaching Machine learning problem – Bhushan Shewale – Medium

#artificialintelligence

An average data scientist deal with lots of data daily, around 60–70% time spend on data cleaning, data munging and convert the data into suitable form so that we can apply machine learning model on that data. This blog focuses on applying machine learning models, including the preprocessing steps. Many Data science enthusiast ask me how to solve machine learning problem? Before applying the machine learning models, the data must be converted to a tabular form. There is two types of data Numerical variable and Categorical variable.


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.


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.