kaggle


Perfect way to build a Predictive Model in less than 10 minutes

@machinelearnbot

In the last few months, we have started conducting data science hackathons. These hackathons are contests with a well defined data problem, which has be be solved in short time frame. They typically last any where between 2 – 7 days. If month long competitions on Kaggle are like marathons, then the...


Deploy Machine Learning Models from R Research to Ruby / Go Production with PMML

@machinelearnbot

Deploying models trained in your research environment is not always a simple task. Your research environment, your production programming language, and the interplay between them may affect the ease of introducing new statistical models in production. In this blog post, I'll demonstrate the complet...


Feature Engineering with Kaggle Tutorial

#artificialintelligence

In the two previous Kaggle tutorials, you learned all about how to get your data in a form to build your first machine learning model, using Exploratory Data Analysis and baseline machine learning models. Next, you successfully managed to build your first machine learning model, a decision tree clas...


The Myth of Entry-level Data Science - insideBIGDATA

#artificialintelligence

To understand how to become a data scientist, it's best to get on the same page on what data science is. Kevin is an entrepreneurial tech and data science leader with a decade of experience in big data and analytics. He holds multiple patents, has started and grown data science teams, and has destroyed 3 microwaves while sciencing. There might not be any topic a data scientist is asked more about than "how can I get into data science." It's a great career and every week in the last few years there's a new article about the unmet demand for "the best job in America."


Our Final Kaggle Dataset Publishing Awards Winners' Interviews (November 2017 and December 2017)

#artificialintelligence

As we move into 2018, the monthly Datasets Publishing Awards has concluded. We're pleased to have recognized many publishers of high-quality, original, and impactful datasets. It was only a little over a year ago that we opened up our public Datasets platform to data enthusiasts all over the world to share their work. We've now reached almost 10,000 public datasets, making choosing winners each month a difficult task! These interviews feature the stories and backgrounds of the November and December winners of the prize.


Throne AI is the Kaggle of Sports Predictions

@machinelearnbot

My big obsession of 2018 so far is sports prediction platform Throne AI. There's no better way to describe than Kaggle for sports. The platform provides users with data with which they use to build models to predict the outcome of sports matches. Each league on Throne AI counts as its own competition with its own ranking of users. It currently has the following league available: NFL, NBA, NHL, English Premier League, Serie A, La Liga, and the English Championship, with more to come.


Booz Allen & Kaggle's Annual Data Science Competition Puts Artificial Intelligence to Work Accelerating Life-Saving Medical Research

@machinelearnbot

Somewhere, buried in one of tens of millions of cell samples, could lie the next great breakthrough in disease prevention or cure. But one of the great barriers to finding it could be the need for human eyes to evaluate a corresponding mountain of cell images, one by one. In an era when terabytes of data can be analyzed in just a few days, the opportunity to enhance automation of biomedical analysis could help researchers achieve breakthroughs faster in the treatment of almost every disease--from cancer, diabetes and rare disorders to the common cold. To spur this automation, Booz Allen Hamilton (NYSE: BAH) and Kaggle today launched the 2018 Data Science Bowl, a 90-day competition that calls on thousands of participants globally to train deep learning models to examine images of cells and identify nuclei, regardless of the experimental setup--and without human intervention. Creators of the top algorithms will split $170,000 in cash and prizes, including an NVIDIA DGX Station, a personal AI supercomputer that delivers the computing capacity of 400 CPUs in a desktop workstation.


Top Machine Learning and Data Science Methods Used at Work – Critical Future

#artificialintelligence

The practice of data science requires the use algorithms and data science methods to help data professionals extract insights and value from data. A recent survey by Kaggle revealed that data professionals used data visualization, logistic regression, cross-validation and decision trees more than other data science methods in 2017. Looking ahead to 2018, data professionals are most interested in learning deep learning (41%). Kaggle conducted a survey in August 2017 of over 16,000 data professionals (2017 State of Data Science and Machine Learning). Their survey included a variety of questions about data science, machine learning, education and more.


Top Machine Learning and Data Science Methods Used at Work

#artificialintelligence

The practice of data science requires the use algorithms and data science methods to help data professionals extract insights and value from data. A recent survey by Kaggle revealed that data professionals used data visualization, logistic regression, cross-validation and decision trees more than other data science methods in 2017. Looking ahead to 2018, data professionals are most interested in learning deep learning (41%). Kaggle conducted a survey in August 2017 of over 16,000 data professionals (2017 State of Data Science and Machine Learning). Their survey included a variety of questions about data science, machine learning, education and more.


Top Machine Learning and Data Science Methods Used at Work

#artificialintelligence

The practice of data science requires the use algorithms and data science methods to help data professionals extract insights and value from data. A recent survey by Kaggle revealed that data professionals used data visualization, logistic regression, cross-validation and decision trees more than other data science methods in 2017. Looking ahead to 2018, data professionals are most interested in learning deep learning (41%). Kaggle conducted a survey in August 2017 of over 16,000 data professionals (2017 State of Data Science and Machine Learning). Their survey included a variety of questions about data science, machine learning, education and more.