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Simple Examples – Practical Predictive Analytics: Models and Methods

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So let me give you an example that comes from whitten that’s used pretty ubiquitously to explain some of the concepts of machine learning and we won’t use this example too much but it’s it is useful for a variety of purposes so imagine you’re trying to predict when someone is going to play golf

Or play tennis or do some other outdoor activity and we imagine that it’s a function of the weather and so we take in inputs like the outlook whether it’s sunny or overcast or rainy the temperature whether it’s hot cool mild the humidity how windy it is and

We want to learn a function that takes in those inputs and produces a yes or no answer whether or not we play say golf okay and so the simplest version of this you might do just intuitively is just try to predict it with a simple rule so you might say look

Maybe we only play when it’s sunny you know is this true or not well no it’s not true because we find we can find examples where we did indeed play when it was overcast okay you might say well maybe we don’t play if it’s rainy and windy

And that turns out to be true but there’s also other times that we don’t play as well so the pattern that you know if a function exists that can predict this output well it’s perhaps not something that we can express in just one simple rule

Which is kind of the limit of what we can do you know mentally just through intuition so the idea is we need some principled way of coming up with more complex models when when they’re needed okay so fine so this some terminology this problem is an example of a classification problem where there’s

A learned attribute and it’s categorical it can take two values in this case zero or one uh if the learned attribute is more of a numeric value for example what our score at golf was you know as a function of the weather if you want to try to predict that that’d

Be more of a regression case okay and really not just learned attributes probably when all the attributes in americans becomes more of a classical regression case although there’s ways to handle this and so we’ve talked about regression in terms of you know fitting a curve to data but when you use that

Fit curve to actually make predictions that’s when it sort of you can see that it’s analogous to the classification problem over categorical data okay so more terminology the the golf example we just gave is an example of supervised learning where you’re given examples of inputs and the desired outputs and we’re trying to

Learn the relationship between them so we train a model to do this and there’s also notion of unsupervised learning which is you’re not given any labels whatsoever you’re just given the data and you’re trying to understand the underlying structure in that data okay so this is things like clustering Algorithms or dimension reduction

Algorithms if you’ve heard those terms before let me give you an example with that can be sort of cast as either supervised learning or unsupervised learning but it’s instructive as an unsupervised learning case uh for right now so imagine you’re just giving a big corpus of documents and you’re trying to figure out

You want to make a prediction of what topic a given document pertains to and so you know if you see the phrase the falcons trounce the saints on sunday you might guess that this document is about sports while if you see that the mars rover discovered organic molecules

On sunday you might guess that this document is about science and so you know how do you set this problem up as a machine learning problem you know what are the rows and columns we just had an example where there’s sort of rows and columns we said for one for one record we’re

Trying to predict the output given the inputs what are the rows and columns here well this shouldn’t be too bad since you’ve seen this before in a couple of assignments now You
Link to this course:

Simple Examples – Practical Predictive Analytics: Models and Methods
Data Science at Scale Specialization
Statistical experiment design and Analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Learning Goals: After completing this course, you will be able to:
1. Design effective experiments and analyze the results
2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation
3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants)
4. Explain and apply a set of unsupervised learning concepts and methods
5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection
Random Forest, Predictive Analytics, Machine Learning, R Programming
The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course,Nive that the course covered a broad range of topics. And good to get pushed to do some kaggle competition and peer review.
Follow a tour through the important methods, algorithms, and techniques in machine learning. You will learn how these methods build upon each other and can be combined into practical algorithms that perform well on a variety of tasks. Learn how to evaluate machine learning methods and the pitfalls to avoid.
Simple Examples – Practical Predictive Analytics: Models and Methods
Copyright Disclaimer under Section 107 of the copyright act 1976, allowance is made for fair use for purposes such as criticism, comment, news reporting, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favour of fair use.
00:03 so let me give you an example that comes
00:05 from
00:05 whitten that’s used pretty ubiquitously
00:07 to explain some of the concepts of
00:09 machine learning and
00:10 we won’t use this example too much but
00:12 it’s it is useful for a variety of
00:14 purposes
00:15 so imagine you’re trying to predict when
00:17 someone is going to play golf
00:18 or play tennis or do some other outdoor
00:20 activity and we imagine that it’s a
00:22 function of the weather and so we take
00:24 in inputs like
00:25 the outlook whether it’s sunny or
00:27 overcast or rainy the temperature
00:28 whether it’s hot cool mild
00:30 the humidity how windy it is and
00:34 we want to learn a function that takes
00:36 in those inputs and produces
00:37 a yes or no answer whether or not we
00:39 play say golf
00:41 okay and so the simplest version of this
00:44 you might do just intuitively is just
00:46 try to predict it with a
00:48 simple rule so you might say look
00:52 maybe we only play when it’s sunny you
00:54 know is this true or not
00:56 well no it’s not true because we find we
00:58 can find examples where we did indeed
00:59 play when it was overcast
01:01 okay you might say well maybe we don’t
01:03 play if it’s rainy and windy
01:04 and that turns out to be true but
01:06 there’s also other times that we don’t
01:07 play as well so
01:09 the pattern that you know if a function
01:11 exists that can predict this output
01:13 well it’s perhaps not something that we
01:16 can express in just one simple rule
01:17 which is kind of the limit of what we
01:19 can do
01:20 you know mentally just through intuition
01:22 so the idea is we need some
01:24 principled way of coming up with more
01:26 complex models
01:27 when when they’re needed okay
01:31 so fine so this some terminology this
01:34 problem is an example of a
01:35 classification problem where there’s
01:37 a learned attribute and it’s categorical
01:40 it can take two values in this case zero
01:41 or one
01:42 uh if the learned attribute is more of a
01:44 numeric value for example what our score
01:47 at golf was
01:48 you know as a function of the weather if
01:50 you want to try to predict that that’d
01:51 be more of a regression
01:53 case okay and really not just learned
01:56 attributes probably when all the
01:57 attributes in americans becomes more of
01:58 a classical regression case although
02:00 there’s ways to
02:01 handle this and so we’ve talked about
02:02 regression in terms of you know fitting
02:04 a curve to data but when you use that
02:06 fit curve to actually make predictions
02:08 that’s when it sort of you can see that
02:10 it’s
02:13 analogous to the classification problem
02:14 over categorical data
02:16 okay so more terminology the
02:20 the golf example we just gave is an
02:22 example of supervised learning where
02:23 you’re given examples of inputs and the
02:25 desired outputs and we’re trying to
02:26 learn the relationship between them so
02:28 we train a model to do this
02:30 and there’s also notion of unsupervised
02:31 learning which is you’re not given any
02:33 labels whatsoever you’re just given the
02:35 data and you’re trying to
02:36 understand the underlying structure in
02:39 that data
02:40 okay so this is things like clustering
02:42 algorithms or dimension reduction
02:44 algorithms if
02:44 you’ve heard those terms before let me
02:46 give you an example
02:48 with that can be sort of cast as either
02:50 supervised learning or unsupervised
02:52 learning but
02:53 it’s instructive as an unsupervised
02:54 learning case uh
02:56 for right now so imagine you’re just
02:58 giving a big corpus of documents
03:00 and you’re trying to figure out
03:04 you want to make a prediction of what
03:07 topic a given document pertains to and
03:10 so
03:10 you know if you see the phrase the
03:12 falcons trounce the saints on sunday you
03:13 might
03:15 guess that this document is about sports
03:17 while if you see that the
03:18 mars rover discovered organic molecules
03:20 on sunday you might guess that this
03:22 document is about
03:24 science and so you know how do you set
03:25 this problem up as a machine learning
03:27 problem
03:28 you know what are the rows and columns
03:29 we just had an example where there’s
03:31 sort of rows and columns
03:32 we said for one for one record we’re
03:33 trying to predict the output given the
03:35 inputs
03:35 what are the rows and columns here well
03:37 this shouldn’t be
03:39 too bad since you’ve seen this before in
03:41 a couple of assignments now
03:46 [Music]
03:46 you

PredictiveAnalyticsModels

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