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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.

4.5
440 reviews

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).
Read More product length iconTime: 41h

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Instructor Details

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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

440 reviews in total

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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 Nitin K M on 7-Nov-19

The course is perfect for people who want to gain in-depth knowledge of classification algorithms but exercise descriptions are vague. I found trouble understanding the flow of assignments. Also, Bagging and Gradient Boosting techniques were not covered under ensembles. Overall, the course is awesome.

By Neemesh J on 28-Oct-19

Awesome learning experience.

By Shrikrishna S P on 18-Oct-19

The course is very well structured. It starts from the basic classifiers, further moving on to more complex ones. The instructors teach how to implement each mentioned algorithm from scratch, this really makes the course above par.I loved the course and it helped me to become a good machine learning practitioner.Thanks Emily and Carlos.

By AJAY K on 14-Oct-19

Excellent tutorials

By Parab N S on 13-Oct-19

Excellent course on Classification by University of Washington

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 Muhammad Z H on 30-Aug-19

I have learned alot

By Gareth J on 26-Aug-19

A good course to teach the key points.

By VIGNESHKUMAR R on 23-Aug-19

good

By Muhammad W K on 19-Aug-19

A great course. Starting from very simple and easy-to-understand concepts of classification, it takes us through very important grass-root concepts and algorithms necessary not only in classification but in better general machine learning understanding too. Like Precision and Recall, Boosting, Scalability and Online machine learning etc.

By Hanna L on 12-Aug-19

Great class!

Showing 12 of 440 reviews

Machine Learning: Classification
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  • Video Duration: icon 41h
  • Price: Free
  • Provider: icon Coursera
  • University: icon University of Washington
  • Language: icon English
  • Certificate: icon Yes

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