Welcome to the introduction to Machine Learning.
We have talked a lot about real-time, high performance platforms.
We've talked about Hadoop and Spark.
We've learned so far how to bring the data into these platforms.
How to do basic analytics.
Some basic summaries.
And now, we're gonna look into additional layers to these big data analytics.
We've talked that data is growing very fast, that it's going from terabytes and
petabytes to zettabytes and exabyte.
We've talked about the speed that it's coming at and the platforms and
techniques that can help us tame this notion of big data.
In our previous classes, we have spent time learning on how to organize,
query and they're both important pieces of the big data.
And now we're gonna add another layer on top of that, called Advanced Analytics.
We're gonna start talking about big data mining, and classification, regression,
rule mining, clustering different techniques that we can apply to this data.
To try and gain some additional insight.
So we have mention in our previous courses
couple of different things about big data challenges.
So what are some of the big data challenges?
Well first of all, it's finding talent.
And hopefully,
we're addressing this by taking this class in big data specialization.
The second one was gathering the data from different sources.
We have been bringing these different data sets from different sources like
Twitter and Yelp and others throughout this course's specialization.
And we're gonna continue doing that and
especially they will culminate in the capstone as well.
And the third one was understanding the tools and platforms.
In this class, we're gonna learn about specific tools and specific platforms for
finding insight in the data by using data mining, predictive and
all that machine learning techniques.
So transforming data into insight for making better decisions is our goal today.
So we're gonna start from data, we're gonna apply some kind of analytics,
we're gonna look for Insight, and
our main goal is to make this data driven decisions that will lead us to action.
So we've talked a little bit about this in one of our earlier classes and
we've mentioned traditional analytics approaches.
Traditional analytics approach is typically have businesses who have a lot
of data in a number of different forms and
they try to make sense out of it to see how can they aid their business.
You often requires many different approaches to analysis depending on
the problem that's being trying to be solved.
Some analysis will use traditional warehouse.
Maybe some other analysis will require many different approaches
to bring in different data sets.
But we typically take a large amount of data, take some subsets of the data,
and we try and see if we can reveal some patterns.
And can we take those patterns and bring them back into the business
enabling some new functionalities, or providing some additional insight.
Now if we start talking about the big data analytics and the big data approach,
things are changing, we have a little bit of a paradigm shift.
The capability to manage and analyze petabytes of data enables companies
now to deal with clusters of information that could have an impact on the business.
And now we are requiring these new analytical engines that can manage this
highly distributed data.
They can provide results in order to optimize and
solve many different business challenges.
So now analytics can get quite complex with larger data.
For example, many organizations are using predictive models that couple structured
and unstructured data together to predict fraud.
Social media analytics, text analytics, all these new kinds of
analytics are being utilized because of the presence of the big data,
because of all these new technologies and platforms that enabling us to
gain access to distributed processing of this large data set.
So we have seen this slide before.
We have talked about these different levels of data and
bringing the data into the decisions and then into the action.
What we're gonna focus on in this specific class is this third level of prediction.
What will happen next?
How can we take the predictions turn them into decisions and then actions?
So this is what we're gonna focus on in this class, mostly.
In one of our earlier classes,
we mentioned how that all fits into this analytics maturity levels.
So we've talked about analyzing the data we have,
being able to ask the questions why things happen, what happened.
And now we're talking about predictive models that will help us answer questions
what might happen.
And maybe why and how it's happening.
And then, we can get into this even higher level of optimization.
What we call prescriptive or optimized predictive analysis.
So how does machine learning fit in the whole big scheme of big data?
Well, so far we have learned a lot about big data.
We know there's a lot of structured data,
a lot of unstructured data, we may know there's a lot of social media data and
we're gonna bring all of this data together.
We're gonna apply a number of different machine learning algorithms,
in order to find Insight.
There is many different kinds of big data applications.
Traditionally, the business are expected the data would be used to answer
certain kinds of questions about what to do, maybe when to do it, how data is.
Integrated is often done in many different ways and
it's coming across many different fields and it really depends whether this is
a general purpose business application or a very specific business application.
With the advent of big data, this is changing.
Now we are seeing the development of applications that are designed
specifically to take advantage of big data.
To take advantage of this specific characteristics of big data.
And we see more and more of these in many areas.
More specifically, healthcare, manufacturing,
marketing are taking off and bring specific big data focused applications.
What all of these applications have in common is that, if you remember we talked
about three Vs, they're all very large volumes, velocity, and varieties of data.
And being able to transform that data and
the behavior they learn from that data into the the market advantage.
So, the one thing that most of these applications will have
underneath is built in machine learning algorithms.
So let's see what these machine learning algorithms look like and
how we can utilize them.