This is a paper in a "Seminal Papers in ML" series by MIT Machine Intelligence Community (MIC). MIT MIC aims to educate the community at-large about machine learning and lower the barriers to entry. To learn more, please visit https://mitmic.io or email mic-exec@mit.edu.
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In order for data scientists to be effective at a startup, they need to be able to build services that other teams can use, or that products can use directly.
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Today, we're going to look at 5 popular clustering algorithms that data scientists need to know and their pros and cons!
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Having done training and mentoring for quite a while, I noticed something that is not taught in most data science curriculum that I have come across but in my opinion, its an essential business knowledge that the data scientist need.
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A guide to making the most out of your data.
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New breakthroughs in AI make the headlines everyday. Far from the buzz of customer-facing businesses, the wide adoption and powerful applications of Machine Learning in Finance are less well known.
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Gopalakrishnan allayed unknown fears of AI, terming it as a transition none can avoid but adopt in due course.
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In honor of Amazon Prime Day, let's take a look at the inner workings of this company that is pushing the bounds of innovation, not only with Amazon prime, but the many other cutting edge management strategies.
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In this post, I am going to verify this statement using a Principal Component Analysis ( PCA ) to try to improve the classification performance of a neural network over a dataset.
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You are lucky to be in the Artificial Intelligence (AI) enabled network, working on or studying machine learning, data science, business intelligence, or any other AI domains that are buzz words in the cutting-edge technology industry.
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In our last post, we started using Data Science for Credit Risk Modeling by analyzing loan data from Lending Club.
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Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms.
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Last Earnings, Twitter Inc. soared the most since its market debut in 2013 after it posted the first revenue growth in four quarters, driven by improvements to its app and added video content that are persuading advertisers to boost spending on the social network - Bloomberg
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In the world of AI, data is king. It's what powers the deep learning machines that have become the go-to method for solving many challenging real-world AI problems. The more high quality data we have, the better our deep learning models perform.
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In a perfect world, data scientists would take subjectivity out of their conclusions when examining findings.
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Stagraph is a new simple visual interface for R, which focuses on data import, data wrangling and data visualization.
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A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.
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Over the past few years, startups have brought in a lot of stuff. From healthcare to banking and finance, each sector has gone through a wave of change to bring ease to work. Looking at the latest trends that have come up into the Artificial Intelligence (AI), here's a list of top 10 AI-based startups which are leading the race.
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