Name: 機器學習的7個步驟 (The 7 Steps of Machine Learning)
Uploaded: 2021-01-14T07:14:35.000Z
Duration: 10 min 36 s
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to sorting cucumbers to detecting escalators

has granted computer systems entirely new abilities.

But how does it really work under the hood?

and use it as an excuse to talk about the process of getting

answers from your data using machine learning.

On this show, we'll explore the art, science,

to create a system that answers the question of whether a drink

This question answering system that we build

is created via a process called training.

In machine learning, the goal of training

is to create an accurate model that answers our questions

Our data will be collected from glasses of wine and beer.

There are many aspects of drinks that we could collect data on--

everything from the amount of foam to the shape of the glass.

But for our purposes, we'll just pick two simple ones--

the color as a wavelength of light and the alcohol content

The hope is that we can split our two types of drinks

We'll call these our features from now on--

be to run out to the local grocery store,

and get some equipment to do our measurements-- a spectrometer

It appears that our grocery store has an electronics

Once our equipment and then booze-- we got it all set up--

it's time for our first real step of machine

This step is very important because the quality

will directly determine how good your predictive model can be.

will be the color and alcohol content of each drink.

This will yield us a table of color, alcohol content,

So a few hours of measurements later, we've

gathered our training data and had a few drinks, perhaps.

And now it's time for our next step of machine learning--

where we load our data into a suitable place

and prepare it for use in our machine learning training.

We'll first put all our data together then randomize

part of determining whether a drink is beer or wine.

In other words, we want to make a determination of what

a drink is independent of what drink came before or after it

This is also a good time to do any pertinent visualizations

is any relevant relationships between different variables

as well as show you if there are any data imbalances.

For instance, if we collected way more data points about beer

than wine, the model we train will be heavily biased

toward guessing that virtually everything that it sees

is beer since it would be right most of the time.

may see beer and wine in equal amount, which

would mean that it would be guessing beer wrong half

We also need to split the data into two parts.

The first part used in training our model

The second part will be used for evaluating our train model's

We don't want to use the same data that the model was trained

on for evaluation since then it would just

just as you wouldn't want to use the questions from your math

Sometimes the data we collected needs other forms

like duplication, normalization, error correction, and others.

These would all happen at the data preparation step.

In our case, we don't have any further data preparation needs,

The next step in our workflow is choosing a model.

There are many models that researchers and data scientists

Some are very well suited for image data, others