Hi, I'm Ankit currently I enrolled for AWS Training and Certification.
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Hi. I'm Kirsten Dupart with
AWS Training and Certification.
Welcome to this introductory course
on Amazon Machine Learning.
I've been with Amazon for a year and a half,
and I'm currently responsible
for curriculum development within
the AWS Training and Certification organization.
I'll begin this course with
an overview of machine learning,
and we'll talk about how data plays an important role.
After the overview, we'll
discuss an innovative way to build
smart applications and walk through a few use cases,
and then we'll wrap up with a discussion of
the AWS frameworks and services
you can use for machine learning applications.
Machine learning is a subset of artificial intelligence.
It helps you use historical data
to make better business decisions.
Machine learning is also
a process where machines take data,
analyze it to generate predictions,
and use those predictions to make decisions.
Those predictions generate results,
and those results are used to improve future predictions.
Machine learning can make predictions from huge datasets.
It can also optimize utility functions and extract
hidden patterns and structures from
those datasets by classifying data.
This enables a software program to
learn and make predictions in the future.
Machine learning enables you to
establish a cycle of improvement
using the data you collect from things like
clickstreams, purchases, and likes.
Machine learning is used in a number of
ways across a number of industries.
For example, it can be used
to detect fraudulent transactions,
filter spam emails, flag suspicious reviews, and so on.
You begin by mining large amounts of data to
identify patterns among the card transactions.
With these patterns, you can train the machine learning
model to flag fraudulent transactions.
It can also be used to personalize content for users
by recommending content and predictive content loading.
Machine learning can be used for targeted marketing,
matching customers with offers they might like,
choosing marketing campaigns, and
cross-selling or upselling items.
Machine learning can also be used to
automate categorisation of documents
such as matching hiring managers and
resumes by learning to understand written content.
It can be used in customer service to provide
predictive routing of customer emails
based on the content and the sender,
as well as social media listening capabilities.
Machine Learning systems discover
hidden patterns in data,
and use these patterns to predict patterns in the future.
For example, if you're analyzing retail data and
a product name contains words like jeans or jacket,
then this product category likely belongs to a parallel.
Machine learning systems learn from examples in
the same way that children learn language or patterns.
It can group data into a summary,
and it can also define data in
a more granular concise way.
Think of machine learning as
a combination of methods and systems.
These methods in systems
predict new data based on observed data,
extract hidden structure from the data,
summarize data into concise descriptions,
optimize an action given
a cost function and observed data,
and adapt based on observed data.
The field of machine learning is often classified into
the following broad categories: supervised learning,
unsupervised learning, and reinforcement learning.
In supervised learning, the inputs to the model
including the example outputs also known as labels,
are known and the model learns to
generalize the outputs from these examples.
In unsupervised learning, the labels aren't known.
The model finds patterns in
structure from the data without any help.
In reinforcement learning,
the model learns by interacting with
its environment and learns to take
action to maximize the total reward.
In supervised learning, the inputs to
the model in the example outputs are provided,
and the model learns to generalize
the outputs from these examples.
The human teachers experience is used to tell
the model which outputs are correct and which are not.
This doesn't mean that the teacher
has to be physically present,
only that the teachers' classifications must be present.
With the help of a large training dataset,
the model learns from its error and
changes its weight to reduce its prediction error.
In classification,
the output variable is a category like color,
which could be red, blue, or green,
and it results in true or
false for a particular question.
In regression, the output variable is a number
or a value like weight, dollars, or temperature.
In unsupervised learning which is also called
self-organization, there's no teacher.
It's based solely on local information.
Here, the model uses only the data
presented to the network without any labels,
and it detects the emerging properties
of the whole dataset.
The model then constructs patterns from
the available information without any pre-trained data.
In clustering, the model discovers groupings in
the data like grouping
customers based on their purchasing behavior.
In association, the model discovers
rules that govern large chunks of data,
for example customers who buy product A,
also tend to buy product B.
In reinforcement learning, a software agent determines
the ideal behavior within
a specific context for a particular problem.
The agent takes the input and
decides the best action for the problem,
and then based on the results of the action,
the agent than receives
simple reward feedback to
allow it to learn from its behavior.
The agent is encouraged to select an action
that maximizes the reward in the long-term.
This type of machine learning algorithm is
inspired by behavioral psychology.
Part of getting useful information out of
your Machine Learning system
is having a smart application.
Your smart application will use machine learning to
analyze your data and predict future outcomes,
which are necessary to make business decisions.
This can include using machine learning
for business questions such as predicting
customer trends like whether
customers will use a particular product of yours,
or to determine if an order is fraudulent.
Based on the customer data you already have,
you can find patterns in the data and then generate
predictions to drive your product
features and improvements.
While machine learning is a rapidly growing field
with an enormous upside for companies to use,
there are some challenges to take into consideration
when building your machine learning
based smart application.
For instance, some machine learning technology can
be complex to use and implement appropriately,
requiring high levels of
expertise that can take time to hire or develop.
Another challenge is finding
the right technology that
scales to the needs of customers.
Finally, being able to tie
machine learning to a business application can take time.
In other words, refining
your models so that your product app
can use that model
productively can require a lot of time.
These are the three primary
considerations you should take
into account when building
your machine learning application.
One way to help address these challenges
could be to use Amazon Machine Learning.
We have offerings in Amazon Machine Learning and
Spark on Amazon Elastic MapReduce or
Amazon EMR for customers who want to fully
manage platform for building models using their own data.
For developers and data
scientists who want to focus on building models,
the platform services
remove the undifferentiated overhead
associated with deploying and
managing infrastructure for training and hosting.
Amazon Machine Learning support
supervised machine learning approaches.
These enable you to predict
specific machine learning tasks
such as binary classification,
multiclass classification, and regression.
Binary classification predicts the answer
to a yes or no question.
For example, is this email spam or not spam?
Is this product a book or a toy,
or is this review written by a customer or a robot?
Multiclass classification
predicts the correct category from a list.
For example, is this product a movie or clothing?
Is this movie a romantic, comedy, documentary,
or thriller, or which category
of products is most interesting to this customer?
Regression predicts the value of a numeric variable.
For example, what will
the temperature be in Seattle tomorrow?
Or for this product,
how many units will sell?
Lastly, how many days before
the customer stops using the application?
At a broad level, these are the steps involved in
building a smart application
using Amazon Machine Learning.
To train a model, you need to create
a data source object pointing to your data,
explore and understand your data,
and transform data and train your model.
To then evaluate and optimize the model,
you need to understand model quality
and adjust model interpretation.
After that, you can retrieve
batch and real-time predictions.
Let's take a quick look at
a case study for using Amazon Machine Learning.
Zillow is a company that provides
home valuations online in the United States.
When the company needed to provide
more timely home valuations for customers,
they decided to run their home valuation tool using
Amazon Kinesis for data ingestion,
and Apache Spark on
Amazon EMR for data processing and analysis.
Now, Zillow runs its machine learning
tasks in hours instead of days,
and it provides more accurate valuation data too.
I hope you learned a little something and we'll
continue to explore our other courses.
Again, I'm Kirsten Dupart with
AWS Training and Certification, thanks for watching.
Hi. I'm Kirsten Dupart with AWS Training and Certification. Welcome to this introductory course
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