Heads up! To view this whole video, sign in with your Courses account or enroll in your free 7-day trial. Sign In Enroll
Preview
Start a free Courses trial
to watch this video
Most approaches to machine learning can be described using statistical analysis and math, which means that some of the components for a spam filter that classifies something as spam or not spam can be reapplied to another similar problem, like classifying different types of zoo animals based on their physical appearance. For this reason, there are many high level machine learning frameworks that already have all the low level code written, so you can just plug your code and your data into pre-existing models.
Machine Learning Frameworks
Machine learning approaches like
classifiers, regressions, and
0:00
cluster analysis can help you navigate raw
data sets that need customized solutions.
0:04
In computer science and
0:09
software engineering, doing custom work
like this is often called low level
0:10
because you're working with the most
basic pieces of the problem.
0:15
But most approaches to machine learning
can be described using statistical
0:20
analysis and math.
0:24
Which means that some of the components
for a spam filter that classifies
0:26
something as spam or not spam can be
reapplied to another similar problem, like
0:31
classifying different types of zoo animals
based on their physical appearance.
0:37
For this reason, there are many high
level machine learning frameworks
0:42
that already have all
the low level code written so
0:47
you can just plug your code and
your data into pre-existing models.
0:51
Later in this course we're going to
use a free one called scikit-learn.
0:56
Let's hop into a web browser and
take a quick look at the big
1:01
machine learning frameworks
that are already out there.
1:05
First up is scikit-learn.
1:09
Scikit-learn is free to use and
it's written in Python.
1:12
It allows you to utilize some of
the models we've already discussed
1:17
in just a few lines of code.
1:21
Right on the home page,
you can see it features tools for
1:24
classification, regression,
clustering and more.
1:28
This is a good choice for
many different types of projects,
1:34
especially on very customized problem
domains, because it's all open-source.
1:37
Next is TensorFlow.
1:43
TensorFlow is another free
open-source Python project with
1:45
a different approach to machine learning
that uses what are called Tensors,
1:50
which can be thought of as
multi-dimensional arrays.
1:55
Then operations can be
performed on Tensors, and
1:59
the results can be visualized in graphs.
2:02
This is somewhat of a departure from the
machine learning approaches I mentioned
2:05
previously.
2:09
But it's worth exploring the website and
2:10
understanding what
TensorFlow has to offer.
2:13
Next, is Amazon Web Services and
their machine learning products,
2:16
which is more of a cloud based
platform than a framework.
2:21
If we scroll down a bit there's
a section that lists some
2:25
API services and
that includes image recognition,
2:30
language translation,
tools to create chat bots and more.
2:35
This can be a good choice for many
different problems, but especially if your
2:41
problem domain can be neatly categorized
into one of these high level APIs.
2:47
IBM Watson offers many high level products
and services for machine learning.
2:54
They include common problem domains
like chat bots and imagine recognition.
2:59
But also includes some others
3:04
that focus on natural language
processing and emotional intent.
3:07
Similar to the others,
this is a great choice for
3:12
many different types of problems.
3:15
And it can give you a big
head start when you need to
3:17
utilize their higher level features that
would be difficult to create on your own.
3:21
There are many more
frameworks out there so
3:27
check the notes associated with
this video for more resources.
3:30
As you've probably gathered, not all
machine learning frameworks are the same.
3:34
And in fact, you could argue that not
all of these are really frameworks,
3:38
but also software libraries or
cloud based platforms.
3:43
The semantics and scopes of these tools
varies but as you gain more experience and
3:48
encounter different types of
machine learning problems,
3:53
it's important to know that there
are many resources available to you.
3:56
You need to sign up for Treehouse in order to download course files.
Sign up