ML.NET – Machine Learning Introduction | ML.NET [1 of 8]


>>Hello everyone and
welcome to this series on ML.NET Machine Learning
Framework for.Net developers. My name is Pranav Rastogi
and I’m a Program Manager on the .Net Team focusing on making machine learning approachable
to.Net developers. In this series of videos we’ll
be looking in depth at ML.NET. Let’s start by understanding
what is machine learning. Machine learning is all about
programming the unprogrammable. Let’s say in this case we
want to write a function whether this picture is
an image of a dog or not. Machine learning will help us figure out whether this image
is a dog or not. Some other examples of
using machine learning are. Let’s say I want to predict
a price of a house. A price of a house can depend on
a variety of features such as, how many rooms does the house have? Does the house have any
special amenities like a pool? How big is the yard? All these features will determine what the exact
price of the house is. Now traditionally let’s
say we had to write a program to detect whether
this is a dog or not. What we would have done is we would
have looked at images of dogs and cats and you’d have looked
at certain characteristics like, how large are the ears? Do they have a tongue?
How big the tongue is? We would write this program
ourselves taking care of all the possibilities
to figure out whether the image is of a
dog or a cat or not. These are some example images that I would have looked at to figure out how to make the program understand whether this
image is a dog or a cat. So I’ll be looking at cat pictures, I’ll be looking at dog pictures, I’ll be looking at
different examples of all these images and trying
to write a program by myself. But with machine learning all of
this becomes much more simpler. Machine learning allows a
program to learn from a set of data to figure out what are all the characteristics
of a particular problem. So in this case what defines a dog, what defines a cat, the machine learning
program will look at different images and see, how big is the mouth? How big is the tongue? Does it have claws? Does it have whiskers? As it trains on more and more data, the machine learning
program will become more smarter and smarter in terms of figuring out
whether this image is of a dog or a cat or not. In the end what you would have
is a program that you can call to figure out what
type of animal is this. So machine learning is really about programming the unprogrammable. What this allows to do is it allows
you to write a function which trains on a set of data to figure out whether this picture
is of a dog or a cat. The machine learning model
that you get is train on lots of these images where it
learns about different features, whether how big the mouth is, whether the cat has claws or not, whether the cat has whiskers. So you don’t have to write all
of these constructs yourself. The machine-learning program looks
at all these images and learns and then it gives you
a function which is a machine learning model that
you can use in your application. Such advancement in machine
learning has opened many possibilities where you can use machine learning to train machine learning models over data
which is of different kinds, you can train machine learning
models to detect audio, to recommend for example new
music that you would like. You can use machine learning
on text-based scenarios so you can do things like
sentiment analysis, sales forecasting movie
recommendation, anomaly detection. You can use machine
learning on images, so you can classify images
whether it’s a cat or a dog, you can detect objects in an image. So you can effectively build a greater user experience
for online cataloging and the machine learning
can also be used to teach machines itself so they
can be much more smarter. In this video we looked at the
basics of what is machine learning. In the next video we
will take a look at ML.NET which is a framework for building machine learning
models for.Net developers

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