Machine Learning is the process of teaching a computer how perform a task with out explicitly programming it. The process feeds algorithms with large amounts of data to gradually improve predictive performance. 

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🔗 Resources

Machine Learning  Tutorials https://fireship.io/tags/machine-learning/
What is ML https://www.ibm.com/cloud/learn/machine-learning
Neural Networks https://towardsdatascience.com/a-beginners-guide-to-convolutional-neural-networks-cnns-14649dbddce8
ML Wiki https://en.wikipedia.org/wiki/Machine_learning

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Topics Covered

- Convolutional Neural Networks
- Machine Learning Basics
- How Data Science Works
- Big Data and Feature Engineering
- Artificial Intelligence History
- Supervised Machine Learning
2024-02-04T05:45:31.403Z
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deprecated
Machine learning.
Teach a computer how to perform a task without explicitly programming it to perform said task.
Instead, feed data into an algorithm to gradually improve outcomes with experience, similar to how organic life learns.
The term was coined in 1959 by Arthur Samuel at IBM, who is developing artificial intelligence that could play checkers.
Half a century later, and predictive models are embedded in many of the products we use every day, which perform 2 fundamental jobs.
1 is to classify data, like is there another car on the road, or does this patient have cancer?
The other is to make predictions about future outcomes like will the stock go up or which YouTube video do you wanna watch next?
The first step in the process is to acquire and clean up data.
Lots and lots of data.
The better the data represents the problem, the better the results.
Garbage in, garbage out.
The data needs to have some kind of signal to valuable to the algorithm for making predictions.
And data scientists perform a job called feature engineering to transform raw data into features that better represent the underlying problem.
The next step is to separate the data into a training set and testing set.
The training data is fed into an algorithm to build a model, then the testing data is used to validate the accuracy or error of the model.
The next step is to choose an algorithm, which might be a simple statistical model like linear or logistic regression, or a decision tree that assigns different weights to features in the data.
Up.
Or you might get fancy with a convolutional neural network, which is an algorithm that also assigns weights to features, but also takes the input data and creates additional features automatically.
And that's extremely useful for datasets that contain things like images or natural language where manual feature engineering is virtually possible.
Every one of these algorithms learns to get better by comparing its predictions to an error function.
If it's a classification problem, like is this animal a cat or a dog.
The error function might be accuracy.
If it's a regression problem, like how much will a loaf of bread cost next year, then it might be mean absolute error.
Python is the language of choice among data scientists, but R and Julia are also popular options, and there are many supporting frameworks out there to make the process approachable.
The end result of the machine learning process is a model, which is just a file that takes some input data in the same shape that it was trained on, then spits out a prediction that tries to minimize the error that it was optimized for.
It can then be embedded on an actual device or deploy to the cloud to build a real world product.
This has been Machine Learning in 100 seconds.
Like and subscribe describe if you wanna see more short videos like this, and leave a comment if you wanna see more machine learning content on this channel.
Thanks for watching, and I will see you in the next one.

Speaker 0: Machine learning. Teach a computer how to perform a task without explicitly programming it to perform said task. Instead, feed data into an algorithm to gradually improve outcomes with experience, similar to how organic life learns. The term was coined in 1959 by Arthur Samuel at IBM, who is developing artificial intelligence that could play checkers. Half a century later, and predictive models are embedded in many of the products we use every day, which perform 2 fundamental jobs.

1 is to classify data, like is there another car on the road, or does this patient have cancer? The other is to make predictions about future outcomes like will the stock go up or which YouTube video do you wanna watch next? The first step in the process is to acquire and clean up data. Lots and lots of data. The better the data represents the problem, the better the results.

Garbage in, garbage out. The data needs to have some kind of signal to valuable to the algorithm for making predictions. And data scientists perform a job called feature engineering to transform raw data into features that better represent the underlying problem. The next step is to separate the data into a training set and testing set. The training data is fed into an algorithm to build a model, then the testing data is used to validate the accuracy or error of the model.

The next step is to choose an algorithm, which might be a simple statistical model like linear or logistic regression, or a decision tree that assigns different weights to features in the data. Up. Or you might get fancy with a convolutional neural network, which is an algorithm that also assigns weights to features, but also takes the input data and creates additional features automatically. And that's extremely useful for datasets that contain things like images or natural language where manual feature engineering is virtually possible. Every one of these algorithms learns to get better by comparing its predictions to an error function.

If it's a classification problem, like is this animal a cat or a dog. The error function might be accuracy. If it's a regression problem, like how much will a loaf of bread cost next year, then it might be mean absolute error. Python is the language of choice among data scientists, but R and Julia are also popular options, and there are many supporting frameworks out there to make the process approachable. The end result of the machine learning process is a model, which is just a file that takes some input data in the same shape that it was trained on, then spits out a prediction that tries to minimize the error that it was optimized for.

It can then be embedded on an actual device or deploy to the cloud to build a real world product. This has been Machine Learning in 100 seconds. Like and subscribe describe if you wanna see more short videos like this, and leave a comment if you wanna see more machine learning content on this channel. Thanks for watching, and I will see you in the next one.
Machine learning. Teach a computer how to perform a task without explicitly programming it to perform said task. Instead, feed data into an algorithm to gradually improve outcomes with experience, similar to how organic life learns. The term was coined in 1959 by Arthur Samuel at IBM, who is developing artificial intelligence that could play checkers. Half a century later, and predictive models are embedded in many of the products we use every day, which perform 2 fundamental jobs. 1 is to classify data, like is there another car on the road, or does this patient have cancer? The other is to make predictions about future outcomes like will the stock go up or which YouTube video do you wanna watch next? The first step in the process is to acquire and clean up data. Lots and lots of data. The better the data represents the problem, the better the results. Garbage in, garbage out. The data needs to have some kind of signal to valuable to the algorithm for making predictions. And data scientists perform a job called feature engineering to transform raw data into features that better represent the underlying problem. The next step is to separate the data into a training set and testing set. The training data is fed into an algorithm to build a model, then the testing data is used to validate the accuracy or error of the model. The next step is to choose an algorithm, which might be a simple statistical model like linear or logistic regression, or a decision tree that assigns different weights to features in the data. Up. Or you might get fancy with a convolutional neural network, which is an algorithm that also assigns weights to features, but also takes the input data and creates additional features automatically. And that's extremely useful for datasets that contain things like images or natural language where manual feature engineering is virtually possible. Every one of these algorithms learns to get better by comparing its predictions to an error function. If it's a classification problem, like is this animal a cat or a dog.
machine learning
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data analysis
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neural networks
data management
deep learning
robotics
cancer
modeling
computer science
The error function might be accuracy. If it's a regression problem, like how much will a loaf of bread cost next year, then it might be mean absolute error.
Python is the language of choice among data scientists, but R and Julia are also popular options, and there are many supporting frameworks out there to make the process approachable. The end result of the machine learning process is a model, which is just a file that takes some input data in the same shape that it was trained on, then spits out a prediction that tries to minimize the error that it was optimized for. It can then be embedded on an actual device or deploy to the cloud to build a real world product. This has been Machine Learning in 100 seconds. Like and subscribe describe if you wanna see more short videos like this, and leave a comment if you wanna see more machine learning content on this channel. Thanks for watching, and I will see you in the next one.
machine learning
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Machine learning. Teach a computer how to perform a task without explicitly programming it to perform said task. Instead, feed data into an algorithm to gradually improve outcomes with experience, similar to how organic life learns. The term was coined in 1959 by Arthur Samuel at IBM, who is developing artificial intelligence that could play checkers. Half a century later, and predictive models are embedded in many of the products we use every day, which perform 2 fundamental jobs. 1 is to classify data, like is there another car on the road, or does this patient have cancer? The other is to make predictions about future outcomes like will the stock go up or which YouTube video do you wanna watch next? The first step in the process is to acquire and clean up data. Lots and lots of data. The better the data represents the problem, the better the results. Garbage in, garbage out. The data needs to have some kind of signal to valuable to the algorithm for making predictions. And data scientists perform a job called feature engineering to transform raw data into features that better represent the underlying problem. The next step is to separate the data into a training set and testing set. The training data is fed into an algorithm to build a model, then the testing data is used to validate the accuracy or error of the model. The next step is to choose an algorithm, which might be a simple statistical model like linear or logistic regression, or a decision tree that assigns different weights to features in the data. Up. Or you might get fancy with a convolutional neural network, which is an algorithm that also assigns weights to features, but also takes the input data and creates additional features automatically. And that's extremely useful for datasets that contain things like images or natural language where manual feature engineering is virtually possible. Every one of these algorithms learns to get better by comparing its predictions to an error function. If it's a classification problem, like is this animal a cat or a dog. The error function might be accuracy. If it's a regression problem, like how much will a loaf of bread cost next year, then it might be mean absolute error. Python is the language of choice among data scientists, but R and Julia are also popular options, and there are many supporting frameworks out there to make the process approachable. The end result of the machine learning process is a model, which is just a file that takes some input data in the same shape that it was trained on, then spits out a prediction that tries to minimize the error that it was optimized for. It can then be embedded on an actual device or deploy to the cloud to build a real world product. This has been Machine Learning in 100 seconds. Like and subscribe describe if you wanna see more short videos like this, and leave a comment if you wanna see more machine learning content on this channel. Thanks for watching, and I will see you in the next one.
Machine
machine
learning.
learning
Teach
teach
a
a
computer
computer
how
how
to
to
perform
perform
a
a
task
task
without
without
explicitly
explicitly
programming
programming
it
it
to
to
perform
perform
said
said
task.
task
Instead,
instead
feed
feed
data
data
into
into
an
an
algorithm
algorithm
to
to
gradually
gradually
improve
improve
outcomes
outcomes
with
with
experience,
experience
similar
similar
to
to
how
how
organic
organic
life
life
learns.
learns
The
the
term
term
was
was
coined
coined
in
in
1959
1959
by
by
Arthur
arthur
Samuel
samuel
at
at
IBM,
ibm
who
who
is
is
developing
developing
artificial
artificial
intelligence
intelligence
that
that
could
could
play
play
checkers.
checkers
Half
half
a
a
century
century
later,
later
and
and
predictive
predictive
models
models
are
are
embedded
embedded
in
in
many
many
of
of
the
the
products
products
we
we
use
use
every
every
day,
day
which
which
perform
perform
2
2
fundamental
fundamental
jobs.
jobs
1
1
is
is
to
to
classify
classify
data,
data
like
like
is
is
there
there
another
another
car
car
on
on
the
the
road,
road
or
or
does
does
this
this
patient
patient
have
have
cancer?
cancer
The
the
other
other
is
is
to
to
make
make
predictions
predictions
about
about
future
future
outcomes
outcomes
like
like
will
will
the
the
stock
stock
go
go
up
up
or
or
which
which
YouTube
youtube
video
video
do
do
you
you
wanna
wanna
watch
watch
next?
next
The
the
first
first
step
step
in
in
the
the
process
process
is
is
to
to
acquire
acquire
and
and
clean
clean
up
up
data.
data
Lots
lots
and
and
lots
lots
of
of
data.
data
The
the
better
better
the
the
data
data
represents
represents
the
the
problem,
problem
the
the
better
better
the
the
results.
results
Garbage
garbage
in,
in
garbage
garbage
out.
out
The
the
data
data
needs
needs
to
to
have
have
some
some
kind
kind
of
of
signal
signal
to
to
valuable
valuable
to
to
the
the
algorithm
algorithm
for
for
making
making
predictions.
predictions
And
and
data
data
scientists
scientists
perform
perform
a
a
job
job
called
called
feature
feature
engineering
engineering
to
to
transform
transform
raw
raw
data
data
into
into
features
features
that
that
better
better
represent
represent
the
the
underlying
underlying
problem.
problem
The
the
next
next
step
step
is
is
to
to
separate
separate
the
the
data
data
into
into
a
a
training
training
set
set
and
and
testing
testing
set.
set
The
the
training
training
data
data
is
is
fed
fed
into
into
an
an
algorithm
algorithm
to
to
build
build
a
a
model,
model
then
then
the
the
testing
testing
data
data
is
is
used
used
to
to
validate
validate
the
the
accuracy
accuracy
or
or
error
error
of
of
the
the
model.
model
The
the
next
next
step
step
is
is
to
to
choose
choose
an
an
algorithm,
algorithm
which
which
might
might
be
be
a
a
simple
simple
statistical
statistical
model
model
like
like
linear
linear
or
or
logistic
logistic
regression,
regression
or
or
a
a
decision
decision
tree
tree
that
that
assigns
assigns
different
different
weights
weights
to
to
features
features
in
in
the
the
data.
data
Up.
up
Or
or
you
you
might
might
get
get
fancy
fancy
with
with
a
a
convolutional
convolutional
neural
neural
network,
network
which
which
is
is
an
an
algorithm
algorithm
that
that
also
also
assigns
assigns
weights
weights
to
to
features,
features
but
but
also
also
takes
takes
the
the
input
input
data
data
and
and
creates
creates
additional
additional
features
features
automatically.
automatically
And
and
that's
that's
extremely
extremely
useful
useful
for
for
datasets
datasets
that
that
contain
contain
things
things
like
like
images
images
or
or
natural
natural
language
language
where
where
manual
manual
feature
feature
engineering
engineering
is
is
virtually
virtually
possible.
possible
Every
every
one
one
of
of
these
these
algorithms
algorithms
learns
learns
to
to
get
get
better
better
by
by
comparing
comparing
its
its
predictions
predictions
to
to
an
an
error
error
function.
function
If
if
it's
it's
a
a
classification
classification
problem,
problem
like
like
is
is
this
this
animal
animal
a
a
cat
cat
or
or
a
a
dog.
dog
The
the
error
error
function
function
might
might
be
be
accuracy.
accuracy
If
if
it's
it's
a
a
regression
regression
problem,
problem
like
like
how
how
much
much
will
will
a
a
loaf
loaf
of
of
bread
bread
cost
cost
next
next
year,
year
then
then
it
it
might
might
be
be
mean
mean
absolute
absolute
error.
error
Python
python
is
is
the
the
language
language
of
of
choice
choice
among
among
data
data
scientists,
scientists
but
but
R
r
and
and
Julia
julia
are
are
also
also
popular
popular
options,
options
and
and
there
there
are
are
many
many
supporting
supporting
frameworks
frameworks
out
out
there
there
to
to
make
make
the
the
process
process
approachable.
approachable
The
the
end
end
result
result
of
of
the
the
machine
machine
learning
learning
process
process
is
is
a
a
model,
model
which
which
is
is
just
just
a
a
file
file
that
that
takes
takes
some
some
input
input
data
data
in
in
the
the
same
same
shape
shape
that
that
it
it
was
was
trained
trained
on,
on
then
then
spits
spits
out
out
a
a
prediction
prediction
that
that
tries
tries
to
to
minimize
minimize
the
the
error
error
that
that
it
it
was
was
optimized
optimized
for.
for
It
it
can
can
then
then
be
be
embedded
embedded
on
on
an
an
actual
actual
device
device
or
or
deploy
deploy
to
to
the
the
cloud
cloud
to
to
build
build
a
a
real
real
world
world
product.
product
This
this
has
has
been
been
Machine
machine
Learning
learning
in
in
100
100
seconds.
seconds
Like
like
and
and
subscribe
subscribe
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learning
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on
on
this
this
channel.
channel
Thanks
thanks
for
for
watching,
watching
and
and
I
i
will
will
see
see
you
you
in
in
the
the
next
next
one.
one
success
Speaker 0 explains the process of machine learning, which involves teaching a computer how to perform a task without explicitly programming it. The goal is to acquire and clean up data, separate the data into a training and testing set, and choose an algorithm. The machine learning process uses the same method as humans, where the algorithm learns to get better by comparing predictions to error functions.
Machine Learning Explained in 100 Seconds
Machine learning. Teach a computer how to perform a task without explicitly programming it to perform said task. Instead, feed data into an algorithm to gradually improve outcomes with experience, similar to how organic life learns. The term was coined in 1959 by Arthur Samuel at IBM, who is developing artificial intelligence that could play checkers. Half a century later, and predictive models are embedded in many of the products we use every day, which perform 2 fundamental jobs. 1 is to classify data, like is there another car on the road, or does this patient have cancer? The other is to make predictions about future outcomes like will the stock go up or which YouTube video do you wanna watch next? The first step in the process is to acquire and clean up data. Lots and lots of data. The better the data represents the problem, the better the results. Garbage in, garbage out. The data needs to have some kind of signal to valuable to the algorithm for making predictions. And data scientists perform a job called feature engineering to transform raw data into features that better represent the underlying problem. The next step is to separate the data into a training set and testing set. The training data is fed into an algorithm to build a model, then the testing data is used to validate the accuracy or error of the model. The next step is to choose an algorithm, which might be a simple statistical model like linear or logistic regression, or a decision tree that assigns different weights to features in the data. Up. Or you might get fancy with a convolutional neural network, which is an algorithm that also assigns weights to features, but also takes the input data and creates additional features automatically. And that's extremely useful for datasets that contain things like images or natural language where manual feature engineering is virtually possible. Every one of these algorithms learns to get better by comparing its predictions to an error function. If it's a classification problem, like is this animal a cat or a dog. The error function might be accuracy. If it's a regression problem, like how much will a loaf of bread cost next year, then it might be mean absolute error. Python is the language of choice among data scientists, but R and Julia are also popular options, and there are many supporting frameworks out there to make the process approachable. The end result of the machine learning process is a model, which is just a file that takes some input data in the same shape that it was trained on, then spits out a prediction that tries to minimize the error that it was optimized for. It can then be embedded on an actual device or deploy to the cloud to build a real world product. This has been Machine Learning in 100 seconds. Like and subscribe describe if you wanna see more short videos like this, and leave a comment if you wanna see more machine learning content on this channel. Thanks for watching, and I will see you in the next one.
Speaker 0 explains the process of machine learning, which involves teaching a computer how to perform a task without explicitly programming it. The goal is to acquire and clean up data, separate the data into a training and testing set, and choose an algorithm. The machine learning process uses the same method as humans, where the algorithm learns to get better by comparing predictions to error functions.