AI News, Machine Learning

Machine Learning

Artificial intelligence is split as 'narrow AI', designed to perform specific tasks inside a website, and 'general AI', which may learn and perform tasks anyplace.

ML is simply machine-controlled feature engineering, feature learning or knowledge illustration learning, to mechanically discover the representations required for feature detection or classification from information, or real-world knowledge as pictures, video, and device knowledge.

Transmogrification: The Magic of Feature Engineering

Machine learning algorithms often take center stage in machine learning and AI. However, in the real world, 90% of the time spent building models goes into ...

The 7 Steps of Machine Learning

How can we tell if a drink is beer or wine? Machine learning, of course! In this episode of Cloud AI Adventures, Yufeng walks through the 7 steps involved in ...

Machine Learning & Artificial Intelligence: Crash Course Computer Science #34

So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving ...

Mathematics of Machine Learning

Do you need to know math to do machine learning? Yes! The big 4 math disciplines that make up machine learning are linear algebra, probability theory, ...

How to Make a Text Summarizer - Intro to Deep Learning #10

Only a few days left to signup for my Decentralized Applications course! I'll show you how you can turn an article into a one-sentence ..

What is Artificial Intelligence Exactly?

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Predicting the Winning Team with Machine Learning

Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn ...

Understanding Feature Space in Machine Learning

Featured Talk ➟ Data Science Pop-up Seattle Presented by Alice Zheng - Director of Data Science at Dato Machine learning derives mathematical models from ...

Lecture 3 | Loss Functions and Optimization

Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model's predictions, and ...