How to pronounce“machine learning”in English

Machine learning, as we were talking about, with software that we're developing ourselves.

Machine learning for us has widened our capacity to explore everything we've ever loved.

Machine learning, artificial-- For sure.

Machine learning is the topic could, the work right now in artificial intelligence.

machine learning but there are in the law for example you need a license to

Machine learning really is teaching machines how to find patterns in large amounts of data.

Machine learning can credit score people who've never had bank accounts, and even voice agents can conduct diligence calls

Machine learning still has a huge place and I'm going to give a couple examples of both machine learning and you know, gen AI here as we go.

machine learning to decide where to progress.

Machine learning models are really good at when you have an underlying data set that the relationships within that data set represent what

machine learning and others as artificial intelligence.

machine learning and AI.

Machine learning, extracting new knowledge from data, can help a computer achieve artificial intelligence.

machine learning, and so that's always been what we've done.

machine learning systems at scale, then you need to rely much more inexperienced, doesn't make a lot of sense to be trying to reinvent.

machine learning algorithms that could run it's search engine scale.

machine learning told me that 20 factors was a good approximation.

Machine learning hadn't done much about sample splitting or cross fitting, there were some techniques that we've introduced, coming from the statistical theory perspective.

Machine learning has a lot of potential for improving our ability to do policy analysis with data.

Machine learning has the potential to do a better job with that, because it has algorithms that have been refined and

Machine learning on the other hand is more flexible.

machine learning methods is kind of robust, flexible estimation guarantees.

Machine learning really at a high level, gives you predictions that are always pretty good but never great.

Machine learning method is going to tell you this.

Machine learning methods are about prediction, they're about giving a type of data, understanding what usually happens in a given situation.

machine learning estimates, and as output gives you confidence intervals.

Machine learning provides high quality predictions y hat.

machine learning algorithms, which is doing Bayesian regularization.

Machine learning is by some seen as just a fancy word for non and semiparametric econometrics.

Machine learning in the way that I presented it here often is a combination of using very flexible functional forms, regularization and tuning.

Machine learning is great it can fit these complex shapes so my friend who was at Microsoft made this goodness of fit for Clippy the paperclip who was Microsoft's

Machine learning basically says machine learning is everything.

Machine learning often, though, will use the language of answering questions or solving problems, even when they are not answering questions or solving problems.

Machine learning approaches for robustness.

Machine learning always has a test set.

Machine learning is great for medical imaging, not so great at doing physical exams or many of the other tasks there.

Machine learning, self-driving cars, cryptocurrency, drones, chatbots.

machine learning and such specialized expertise in HPC that there's no one human on this planet that knows both subjects to the levels of expertise needed.

machine learning challenge here, which is given some data set, including some on average correct behavior of asking the user to move up or move down,

Machine learning as a physical process.

Machine learning's in the mix?

- Machine learning says hold my beer.

Machine learning's pretty big.

machine learning, software interface design space?

machine learning learning in particular and data science

machine learning all that kind of stuff?

machine learning is is uh building on levels of abstraction that don't have messiness in them that uh at the operating system level

machine learning and the human brain i think are stretched sometimes right at the same time being able to backprop and being able to

machine learning like i was really interested in recommender systems and supervised learning and and the more i dug into it i was like oh