Do you know the basics of supervised learning and want to learn to use state-of-the-art models on real-world datasets? Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting that has taken data science by storm, with models using XGBoost regularly winning many online data science competitions and used at scale across different industries. In this course, you'll learn how to use this powerful library alongside pandas and scikit-learn to build and tune supervised learning models. You'll work with real-world datasets to solve classification as well as regression problems.
Achieving sustainable growth while coping with a population decline calls for "Society 5.0," a super smart society where we can resolve various social challenges by incorporating the innovations of the fourth industrial revolution such as the "internet of things," big data, artificial intelligence, robots and the sharing economy into every industry and society. Japan, in a sense, is far ahead of the rest of the world in realizing this new society, as it is compelled to do so. About 27.3 percent of Japan's 127 million people were aged 65 or higher in 2016, with the ratio expected to reach 38.4 percent by 2065, according to the Ministry of Internal Affairs and Communications. The country's medical expenses are also expected to increase. The Ministry of Health, Labor and Welfare reported ¥41.3 trillion in medical costs in fiscal 2016, and they are expected to increase to ¥57.8 trillion by fiscal 2025, according to the National Federation of Health Insurance Societies.
The Data Science Bowl is underway again, and this year, deep learning is the game. For the next 90 days, data scientists will have the chance to submit algorithms that can identify nuclei in cell samples without human intervention. The idea is to speed up drug target identification by tasking a deep learning model with analyzing millions of cell samples, rather than relying on human scientists. "All current options for nuclei detection require time-consuming biologist intervention. There are no deep learning models available today that can identify nuclei across multiple experimental setups and testing conditions," the event's official statement says.
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ... Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... Google announces scholarship program to train 1.3 lakh Indian developers in emerging technologies 43806 views Want to be a millionaire before you turn 25? Study artificial intelligence or machine learning 43173 views
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.
The world's top 32 drone pilots will compete Saturday in Las Vegas for the world champion title in the International Drone Racing Assn.'s top challenge. Semi-professionals wearing virtual reality headgear compete for a $50,000 cash prize in the Challengers Cup Final on Friday and Saturday at the South Point hotel-casino at 9777 S. Las Vegas Blvd. Competitors qualified during 2017 races that began in Buenos Aires, Argentina, and concluded in Manila, the Philippines. Visitors can buy tickets to watch for $20. You'll be admitted to Friday's practice runs and the competition on Saturday afternoon. The elimination round will get underway at 12:30 p.m. with the finals set for 3:20 p.m. Saturday.
The accuracy of a predictive model can be boosted in two ways: Either by embracing feature engineering or by applying boosting algorithms straight away. Having participated in lots of data science competition, I've noticed that people prefer to work with boosting algorithms as it takes less time and produces similar results. There are multiple boosting algorithms like Gradient Boosting, XGBoost, AdaBoost, Gentle Boost etc. Every algorithm has its own underlying mathematics and a slight variation is observed while applying them. If you are new to this, Great!
We've had an amazing turn out for the second FB live-coding event! Thanks to all that joined Hugo Bowne-Anderson in December in submitting several submissions to Kaggle's infamous Titanic Machine Learning Competition!. For those that missed it, here is a recap to find all the useful links and notebooks to take you from zero to one with machine learning in Python. This live code-along session covers how to build an algorithm that predicts whether any given passenger on the Titanic survived or not, given data on them such as the fare they paid, where they embarked and their age. Hugo shows you how to do so using the Python programming language, Jupyter notebooks and state-of-the-art packages such as pandas, scikit-learn and seaborn.
"What I love," says Alyssa Siefert, Engineering Director at Yale Center for Biomedical Innovation and Technology (CBIT), "is a democratization of problem solving." Siefert is one of the lead organizers of the Yale Healthcare Hackathon, an event in its fifth year that brings together a diverse group of clinicians, engineers, designers, patients and community members Jan 19-21 at Yale School of Medicine to come up with solutions to healthcare challenges. Last year, the event had representatives from eight countries and two dozen universities, and those numbers have been on the rise. About half the participants are non-Yale. The main sponsor of this year's event is 4Catalyzer, a Guilford, Connecticut-based accelerator founded by Dr. Jonathan Rothberg, who serves as its Chief Strategy Officer, for launching new biomedical startups with a heavy emphasis on medical devices, artificial intelligence and big data.
What can you build in 30 hours straight? As a group of second year college students with a growing portfolio of work, my team and I wanted to find out. So we signed up to a hackathon. It was a Financial Technology (or'Fintech') hackathon organized by DCB Bank in the city of Mumbai. Although we were clueless about Fintech, we wanted to give it a try, in the hope of coming with an idea that solves a general problem.