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.

( 440 Reviews )

Created by: Carlos Guestrin

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Overall Score : 90 / 100

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).

Time: 41h

Instructor Details

Carlos Guestrin

4.5 Rating
440 Reviews

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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By Alberto J L R on 3-Nov-16

Pretty Solid!

By SAI V L on 8-Mar-16

Awesome. Waiting for the next one.

By KANDARP B S on 14-Mar-16

Great course!Personally I could use a little more on the math behind the algorithms (e.g. Adaboost, why does it work?).Also, would be great to add SVM in next iterations of this class.Thanks!

By Hanqiao L on 24-May-17

Excellent and intuitive introduction to classification.Certainly a lighthouse in a rather overwhelming and chaotic learning scenario of machine learning we have now a days(Highly recommended for both mathematics and programming student)

By Abhishek T G on 22-Jun-16

The quizzes can be a bit more challenging

By clark.bourne on 9-May-16

Professional, comprehensive, worth to learn

By Francesco on 15-Nov-19

The material is good, but the choice of using GraphLab Create is a poor one. It's not used in the industry and it's poorly supported. I had issues installing it both via command line and via the installer, so I ended up using the AWS machine. But that has it's own drawbacks, such as the slowness and the setup time.

By Martin B on 11-Apr-19

As with all the courses in this specialization: great production values, excellent tuition. Useful assignments, even though the reliance of Graphlab Create is a bit of a drag. I also would have liked to see some discussion of Support Vector Machines.

By Sahil M on 10-Jul-18

Was a good course with some in-depth topics covered!

By MARIANA L J on 12-Aug-16

The good:-Good examples to learn the concepts-Good organization of the material-The assignments were well-explained and easy to follow-up-The good humor and attitude of the professor makes the lectures very engaging-All videolectures are small and this makes them easy to digest and follow (optional videos were large compared with the rest of the lectures but the material covered on those was pretty advanced and its length is justifiable)Things that can be improved:-In some of the videos the professor seemed to cruise through some of the concepts. I understand that it is recommended to take the series of courses in certain order but sometimes I felt we were rushing through the material covered-I may be nitpicking here but I wish the professor used a different color to write on the slides (the red he used clashed horribly with some of the slides' backgrounds and made it difficult to read his observations)Overall, a good course to take and very easy to follow if taken together with the other courses in the series.

By Jaiyam S on 24-Apr-16

Thank you Prof. Carlos for this amazing course. You covered the topics in a very easy to understand way and the course was full of cool applications and humor! The only downside that I felt was that the programming assignments sometimes felt too easy. Even as a complete Python novice (I started learning Python with the first course), I felt the programming assignments could have been made more interesting. But in the larger scheme of things it doesn't matter because the course was really well taught and easy to understand. I'm really looking forward to the next course! :)

By Ning A on 16-Sep-16