Machine Learning: Classification

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

Created by: Carlos Guestrin

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Overall Score : 90 / 100

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Course Description

Case Studies: Analyzing Sentiment & Loan Default PredictionIn our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to:-Describe the input and output of a classification model.-Tackle both binary and multiclass classification problems.-Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees.-Improve the performance of any model using boosting.-Scale your methods with stochastic gradient ascent.-Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults.-Use techniques for handling missing data.-Evaluate your models using precision-recall metrics.-Implement these techniques in Python (or in the language of your choice, though Python is highly recommended).

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Instructor Details

Carlos Guestrin

Carlos Guestrin is the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington. He is also a co-founder and CEO of Dato, Inc., focusing on making it easy to build intelligent applications that use large-scale machine learning at their core. His previous positions include the Finmeccanica Associate Professor at Carnegie Mellon University and senior researcher at the Intel Research Lab in Berkeley. Carlos is a recipient of a National Science Foundation CAREER Award, an Alfred P. Sloan Fellowship, and the Stanford Centennial Teaching Assistant Award. Carlos was also named one of the 2008 `Brilliant 10' by Popular Science Magazine, received the IJCAI Computers and Thought Award from the top AI conference, and the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Obama.

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Reviews

4.5

114 total reviews

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By Lewis C L on 13-Jun-19

First, coursera is a ghost town. There is no activity on the forum. Real responses stopped a year ago. Most of the activity is from 3 years ago. This course is dead.Two, this course seems to approach the topic as teaching inadequate ways to perform various tasks to show the inadequacies. You can learn from that; we will make mistakes or use approaches that are less than ideal. But, that should be a quick "don't do this," while moving on to better approachesThree, the professors seem to dismiss batch learning as a "dodgy" technique. If Hinton, Bengio, and other intellectual leaders of the field recommend it as the preferred technique, then it probably is.Four, the professors emphasize log likelihood. Mathematically, minus the log likelihood is the same as cross-entropy cost. The latter is more robust and applicable to nearly every classification problem (except decision trees), and so is a more versatile formulation. As neither actually plays any roll in the training algorithm except as guidance for the gradient and epsilon formulas and as a diagnostic, the more versatile and robust approach should be preferred.The professors seem very focused on decision trees. Despite the "apparent" intuitive appeal and computational tractability, the technique seems to be eclipsed by other methods. Worth teaching and occasionally using to be sure, but not for 3/4 of the course.There are many mechanical problems that remain in the material. At least 6 errors in formulas or instructions remain. Most can be searched for on the forum to find some resolution, through a lot of noise. Since the last corrections were made 3 years ago, the UW or Coursera's lack of interest shows.It was a bit unnecessary to use a huge dataset that resulted in a training matrix or over 10 billion cells. Sure, if you wanted to focus on methods for scaling--very valuable indeed--go for it. But, this lead to unnecessary long training times and data issues that were, at best, orthogonal to the overall purpose of highlighting classification techniques and encouraging good insights about how classification techniques work.The best thing about the course was the willingness to allow various technologies to be used. The developers went to some lengths to make this possible. It was far more work to stray outside the velvet ropes of the Jupiter notebooks, but it was very rewarding.Finally, the quizzes were dependent on numerical point answers that could often be matched only by using the same exact technology and somewhat sloppy approaches (no lowercase for word sentiment analysis, etc.). It does take some cleverness to think of questions that lead to the right answer if the concepts are implemented properly. It doesn't count when the answers rely precisely on anomalies.I learned a lot, but only because I wrote my own code and was able to think more clearly about it, but that was somewhat of a side effect.All in all, a disappointing somewhat out of date class.

By Christian J on 25-Jan-17

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses

By Saqib N S on 16-Oct-16

Hats off to the team who put the course together! Prof Guestrin is a great teacher. The course gave me in-depth knowledge regarding classification and the math and intuition behind it. It was fun!

By RAJKUMAR R V on 2-Oct-19

It will definitely help you in understanding the basics to dept of most of the algorithms. Even though you are already aware of most of the things covered elsewhere related to Classification, this course will add up up a considerable amount of extra inputs which will help to understand and explore more things in Machine learning.

By Feng G on 12-Jul-18

Very helpful. Many ThanksSome suggestions:1.Please add LDA into the module.2.It is really important if you guys can provide more examples for pandas and scikit-learn users in programming assignments like you do in regression module.

By Alex H on 8-Feb-18

Relying on a non-open source library for all of the code examples vitiates the value of this course. It should use Pandas and sklearn.

By Javier A on 25-Nov-18

Quite Interesting. Entertaining and the lectures are quite easy to follow.

By Xue on 15-Dec-18

Very good lessons on classification.

By Sathiraju E on 28-Nov-18

It's such a well organized course. Concepts are taught in an interesting way and made simple to understand through examples that thread along the course. I would recommend any aspiring data scientists to take this course. Thank you Carlos and Emily.

By Nitin D on 18-Dec-18

Excellent lessons on this important topic Classification. I think all major areas were explained quite nicely, with proper examples.

By leonardo d on 2-Dec-18

This course covered very interesting aspects of real-world applications for machine learning. From my point of view, the theory was very clear an valuable, until that point that the programming assignments closed the cycle beautifully.

By Nidal M G on 4-Dec-18

very good