Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems. At the end of the course, you will be able to: • Design an approach to leverage data using the steps in the machine learning process. • Apply machine learning techniques to explore and prepare data for modeling. • Identify the type of machine learning problem in order to apply the appropriate set of techniques. • Construct models that learn from data using widely available open source tools. • Analyze big data problems using scalable machine learning algorithms on Spark. Software Requirements: Cloudera VM, KNIME, Spark
Categories Of Machine Learning Techniques
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From the course by University of California San Diego
Machine Learning With Big Data
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From the lesson
Introduction to Machine Learning with Big Data
Meet the Instructors
Mai NguyenLead for Data Analytics
San Diego Supercomputer Center
Ilkay AltintasChief Data Science Officer
San Diego Supercomputer Center
Now that we know what machine learning is and have seen some examples of it,
let's talk about how we do machine learning.
In this lecture we will get an overview of the main categories of machine learning
techniques.
After this video you will be able to
describe the main categories of machine learning techniques and
summarize how supervised learning differs from unsupervised learning.
There are different categories of machine learning techniques for
different types of problems.
The main categories are listed here.
They are classification, regression, cluster analysis, and
association analysis.
We will cover each one in detail in the following slides.
In classification, the goal is to predict the category of the input data.
An example of this is predicting the weather as being sunny, rainy, windy, or
cloudy.
The input data in this case would be sensor data specifying the temperature,
relative humidity, atmospheric pressure, wind speed, wind direction, etc.
The target or what you're trying to predict would be the different weather
categories, sunny, windy, rainy, and cloudy.
Another example is to classify a tumor as either benign or malignant.
In this case, the classification is referred to as
binary classification since there are only two categories.
But you can have many categories as well.
As the weather prediction problem shown here.
Another example is to identify hand written digits as being in one of ten
categories, zero to nine.
Some more examples of classification are classifying a tumor from a medical
image as being benign or malignant.
Predicting whether it will rain the next day.
Determining if a loan application is high-risk, medium-risk or low-risk.
Identifying the sentiment of a tweet or
review as being positive, negative, or neutral.
When your model has to predict a numeric value instead of a category,
then the task becomes a regression problem.
An example of regression is to predict the price of a stock.
The stock price is a numeric value, not a category.
So this is a regression task instead of a classification task.
If you were to predict whether the stock price will rise or
fall, then that would be a classification problem.
But if you're predicting the actual price of the stock,
then that is a regression problem.
That is the main difference between classification and regression.
In classification, you're predicting a category and
in regression, you're predicting a numeric value.
Some other examples of regression are estimating the demand of a product
based on time or season of the year.
Predicting a score on a test,
determining the likelihood of how effective a drug will be for
a particular patient, predicting the amount of rain for a region.
In cluster analysis,
the goal is to organize similar items in your data set into groups.
A very common application of cluster analysis is referred to as
customer segmentation.
This means that you're separating your customer base into different groups or
segments based on customer types.
For example it would be very beneficial to segment your customers into seniors,
adults and teenagers.
These groups have different likes and dislikes and
have different purchasing behaviors.
By segmenting your customers to different groups you can more effectively provide
marketing adds targeted for each groups particular interests.
Note that cluster analysis is also referred to as clustering.
Some other examples of cluster analysis are, identifying areas of
similar topography, such as desert region, grassy areas, mountains etc.
Categorizing different types of tissues from medical images.
Determining different groups of weather patterns, such as snowy, dry, monsoon.
And discovering hot spots for different types of crime from police reports.
The goal in association analysis is to come up with a set of rules to
capture associations between items or events.
The rules are used to determine when items or events occur together.
A common application of association analysis is known as market
basket analysis.
Which is used to understand customer purchasing behavior.
For example, association analysis can reveal that banking customers who have
CDs, or Certificates of Deposits, also tend to be interested in
other investment vehicles such as money market accounts.
This information can be used for cross selling.
If you advertise money market accounts to your customers with CDs they are likely to
open such an account.
According to data mining folklore a supermarket chain used association
analysis to discover a connection between two seemingly unrelated products.
They discovered that many customers who go to the store
late on Sunday night to buy diapers also tend to buy beer.
This information was then used to place beer and
diapers close together and they saw a jump in sales of both items.
This is the famous diaper, beer connection.
Some other applications of association analysis are recommending similar
items based on the purchasing behavior or browsing histories of customers.
Finding items that are often purchased together, such as garden hose and
potting soil, and
offer sales on these related items at the same time to drive sales of both items.
Identifying web pages that are often accessed together so
that you can more efficiently offer up these related web pages at the same time.
We have now looked at the different categories of machine learning techniques.
They are classification, regression, cluster analysis, and
association analysis.
We have also seen some examples of each category
There is also another categorization of machine learning techniques, and
that is supervised versus unsupervised approaches.
In supervised approaches the target,
which is what the model is predicting, is provided.
This is referred to as having labeled data because the target is labeled for
every sample that you have in your data set.
Referring back to our example of predicting a weather category of sunny,
windy, rainy or cloudy,
every sample in the data set is labeled as being one of these four categories.
So the data is labeled and predicting the weather categories is a supervised task.
In general, classification and regression are supervised approaches.
In unsupervised approaches on the other hand,
the target that the model is predicting is unknown or unavailable.
This means that you have unlabeled data.
Going back to our cluster analysis example of segmenting customers into
different groups.
The samples in your data are not labeled with the correct group.
Instead, the segmentation is performed using a clustering technique to group
items based on characteristics that they have in common.
Thus, the data is unlabeled and
the task of grouping customers into different segments is an unsupervised one.
In general, cluster analysis and
association analysis are unsupervised approaches.
In summary,
in this lecture we looked at the different categories of machine learning techniques.
We discussed classification, regression, cluster analysis and association analysis.
We also defined what supervised and
unsupervised approaches are, in machine learning.
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