Explore
For Enterprise
Join for Free
Log In

Arts and Humanities
Business
Computer Science
Data Science
Information Technology
Health
Math and Logic
Personal Development
Physical Science and Engineering
Social Sciences
Language Learning
Macquarie University
Develop leadership abilities with Macquarie's Global MBA
Apply for Macquarie University's Global MBA by 13 December and get started sooner
Coursera for Business
World-class training to upskill your team or entire company
Transform your talent with content developed by top universities and companies.
Degrees
Certificates

Explore all of Coursera

Browse
Top Courses
For Enterprise
Log In
Join for Free

Simple Examples

Loading...

Practical Predictive Analytics: Models and Methods

University of Washington

4.1 (290 ratings) | 30K Students Enrolled

Course 2 of 4 in the 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

Skills You'll Learn

Random Forest, Predictive Analytics, Machine Learning, R Programming

Reviews

4.1 (290 ratings)

5 stars
132 ratings
4 stars
98 ratings
3 stars
31 ratings
2 stars
17 ratings
1 star
12 ratings

PV

Nov 12, 2015

The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course

CC

Jul 20, 2016

Nive that the course covered a broad range of topics.\n\nAnd good to get pushed to do some kaggle competition and peer review.

From the lesson

Supervised Learning

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.

Statistics vs. Machine Learning4:16

Simple Examples3:46

Structure of a Machine Learning Problem5:24

Classification with Simple Rules5:01

Learning Rules4:19

Rules: Sequential Covering3:10

Rules Recap2:30

Taught By

Bill Howe
Director of Research

Transcript

[MUSIC] So let me give you an example that comes from Wheaton 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 is useful for a variety of purposes. So imagine you're trying to predict when someone is gonna 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 and whether it's sunny or overcast or rainy, the temperature, whether it's hot, cool, mild, the humidity, how windy it is. And we wanna learn a function that takes in those inputs and produces a yes or no answer or 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 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. 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 kinda the limit of what we can do mentally just through intuition. And so the idea is we need some principled way of coming up with more complex models when they're needed, okay. So fine, so 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. If the learned attribute is more of a numeric value, for example, what our score in golf was, as a function of the weather, if you want to try to predict that, that would be more of a regression case, okay. And really not just learned attributes, probably all the attributes are numeric, it becomes more of a classical regression case, although there's ways to handle this. And so we've talked about regression in terms of fitting a curve to data, but when you use that fit curve to actually make predictions, that's when you can see that it's analogous to the classification problem over categorical data, okay. So more terminology, 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 an issue 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're heard those terms before. So let me give you an example that can be sort of cast as either supervised learning or unsupervised learning, but it's instructive as an unsupervised learning case for right now. So imagine you're just given a big corpus of documents, and you wanna make a prediction of what topic a given document pertains to. And so if you see the phrase the Falcons trounced 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 how do you set this problem up as as machine learning problem? What are the rows and columns? We just had a example where there are sorta rows and columns and we said 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. >> [MUSIC]

Explore our Catalog

Join for free and get personalized recommendations, updates and offers.

Coursera

About
Leadership
Careers
Catalog
Certificates
MasterTrack™ Certificates
Degrees
For Enterprise
For Government
For Campus

Community

Learners
Partners
Developers
Beta Testers
Translators
Blog
Tech Blog

More

Terms
Privacy
Help
Accessibility
Press
Contact
Directory
Affiliates

Learn Anywhere

Follow Us

© 2019 Coursera Inc. All rights reserved.