Machine learning for everybody
In this lecture , we 'll talk about supervised and unsupervised learning
models. We 'll also see how we can program it on Google CoLab. In
order to import the data set, NumPy will import NumPy, pandas and
matplotlib. And then we 'll import other things as we go. So basically,
this command here just reads some CSV file that you pass in CSV has
come about comma separated values. So now if I pass in a names here
, then it basically assigns these labels to the columns of this data set.
So I 'm just going to take these values and make that the column
names. We 're going to use this example to try to predict for future
samples. Each of these samples have one quality for each or one value
for each of these labels up here. And that is something known as
classification. So for you know , sample zero, I have 10 different
features. So I have. 10 different values that I can pass into some
model. And I can spit out.
Machine learning is a sub domain of computer science that focuses on
certain algorithms that might help a computer learn from data without
a programmer telling the computer exactly what to do. That 's what we
call explicit programming. So AI is artificial intelligence. Data science
is a field that attempts to find patterns and draw insights from data.
All of these fields kind of overlap and all of them might use machine
learning. A machine learning model looks like you have a bunch of
inputs that are going into some model. And then the model is spitting
out an output , which is our prediction. In both of these, there 's no
inherent order built into either of these categorical data sets. That 's
not like we can rate us one and France to Japan three , etc. One hot
encoding is saying if it matches some category, make that a one. And
if it does n't just make that zero, that's one hot encoding. There are
also a different type of qualitative feature. These are known as ordinal
pieces of data because they have some sort of inherent order. So for
these types of data sets we can just mark them from one to five.
There are some different tasks in supervised learning that there are
different tasks. In supervised learning, there is one classification, and
basically classification. But there is also binary classification. And
binary classification, you might have hot dog , or not hot dog. So
there's only two categories that you 're working with something that is
something and something that's is n't binary. Each row is a different
sample in the data. So each row of this will be fed into our model. And
our model will make some sort of prediction. And what we do is we
compare that prediction to the actual value of y. So I 've condensed
this to a chocolate bar to kind of talk about some of the other concepts
in machine learning. We break up our whole data set that we have into
three different types of data sets. We call it the training data set, the
validation data set and the testing data set. The validation set is kind
of used as a reality check during or after training to ensure that the
model can handle unseen data still.
In this lecture , we 'll talk about supervised and unsupervised learning
models. We 'll also see how we can program it on Google CoLab. In
order to import the data set, NumPy will import NumPy, pandas and
matplotlib. And then we 'll import other things as we go. So basically,
this command here just reads some CSV file that you pass in CSV has
come about comma separated values. So now if I pass in a names here
, then it basically assigns these labels to the columns of this data set.
So I 'm just going to take these values and make that the column
names. We 're going to use this example to try to predict for future
samples. Each of these samples have one quality for each or one value
for each of these labels up here. And that is something known as
classification. So for you know , sample zero, I have 10 different
features. So I have. 10 different values that I can pass into some
model. And I can spit out.
Machine learning is a sub domain of computer science that focuses on
certain algorithms that might help a computer learn from data without
a programmer telling the computer exactly what to do. That 's what we
call explicit programming. So AI is artificial intelligence. Data science
is a field that attempts to find patterns and draw insights from data.
All of these fields kind of overlap and all of them might use machine
learning. A machine learning model looks like you have a bunch of
inputs that are going into some model. And then the model is spitting
out an output , which is our prediction. In both of these, there 's no
inherent order built into either of these categorical data sets. That 's
not like we can rate us one and France to Japan three , etc. One hot
encoding is saying if it matches some category, make that a one. And
if it does n't just make that zero, that's one hot encoding. There are
also a different type of qualitative feature. These are known as ordinal
pieces of data because they have some sort of inherent order. So for
these types of data sets we can just mark them from one to five.
There are some different tasks in supervised learning that there are
different tasks. In supervised learning, there is one classification, and
basically classification. But there is also binary classification. And
binary classification, you might have hot dog , or not hot dog. So
there's only two categories that you 're working with something that is
something and something that's is n't binary. Each row is a different
sample in the data. So each row of this will be fed into our model. And
our model will make some sort of prediction. And what we do is we
compare that prediction to the actual value of y. So I 've condensed
this to a chocolate bar to kind of talk about some of the other concepts
in machine learning. We break up our whole data set that we have into
three different types of data sets. We call it the training data set, the
validation data set and the testing data set. The validation set is kind
of used as a reality check during or after training to ensure that the
model can handle unseen data still.
