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Learner Reviews & Feedback for Machine Learning: Classification by University of Washington

4.7
stars
3,433 ratings
572 reviews

About the Course

Case Studies: Analyzing Sentiment & Loan Default Prediction In 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)....

Top reviews

SM

Jun 15, 2020

A very deep and comprehensive course for learning some of the core fundamentals of Machine Learning. Can get a bit frustrating at times because of numerous assignments :P but a fun thing overall :)

SS

Oct 16, 2016

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!

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501 - 525 of 542 Reviews for Machine Learning: Classification

By Supharerk T

Jul 06, 2016

All of the courses lecture are great until it reaches week 5 where it's really hard to catch, the programming assignment doesn't give enough hints and lecture in this topic doesn't help much.

By nazar p

Jun 30, 2017

While courses 1 and 2 of this specialization were quite good, I find this one a bit sparse on content. I think this course could be easily compressed into 2-3 weeks instead of 7.

By Rohit J

May 12, 2016

A lot of interesting parts of the course are available as optional and a lot of the difficult parts of the coding exercises are provided to you - the challenge is not there. :/

By Ilan S

Nov 23, 2016

The videos were pretty goods. But a bit too slow and easy. The assigments were ok, but too guiding. Also there were too much reimplementation of algorithm

By Rahul S

Jun 17, 2020

Too much confusion, I face too much problem with this course. much confusion if you use different packages like sklearn.

By Fengchen G

May 19, 2016

The course content seemed to be rushed out, as a result, the quality is not as good as the first two.

By Tu L P H

Jun 28, 2018

Why don't you guys talk about ID3 or CART algorithm at all? This one is too basic.

By Mounir

Jun 19, 2016

Exercises for Scikit-learn users were not organised.

Course took too long to start

By Pier L L

Mar 26, 2017

Nice course but I would have expected more techniques (SVM for instance)

By Dmitri B

Jun 06, 2017

Theory Quizes are good, but programming assignment not so good for me.

By Ashish C

Mar 31, 2019

more topics like deep learning, neural networks need to be introduced

By Matt T

Apr 12, 2016

Good, but overemphasizes niche software product (graphlab).

By Virgil P

Feb 18, 2018

The exercises/assignments are far too simple

By 陈弘毅

Feb 03, 2018

too simple

By Akash K S

Aug 13, 2020

good

By Omkar v D

Aug 14, 2018

.

By Rohan G L

Aug 29, 2020

I leave 2 stars as I learned a lot of new information and methods, and the theory and math behind them.

You will learn about Data Science and Machine Learning, but not much about Python.

The course is pretty much abandoned and outdated. Sframes and Turicreate packages (instructor's creations) are used instead of more universal packages. Installation in the beginning took some time and research. Many of the assignments have errors and bugs in the code that have not been updated. Forum assistance is abysmal for clarification or deeper questions. Many links are dead.

There are many times in the lectures where the instructors are writing several sentences in their handwriting on their notes instead of having the text ready to appear.

I would suggest using this course and series as a supplement to other information one as learned, not as an introduction for initial understanding. I found myself frustrated too many times.

By Amit K

Jan 20, 2018

The video content is awesome. Important concepts are being clarified in a very simple manner. However the evaluation method really sucks. First, there is too much spoon feeding in the programming assignments, which was not the case in earlier courses in the same specialisation. Secondly, in a few assignments, the answer to the quiz questions are sensitive to the platform we are using (like PC vs AWS instance). This was really frustrating given that the issue is known for a long time and has not been fixed yet. At the very least, there should be a warning on the quiz page itself.

By Yaron K

Sep 30, 2016

The assignments are well thought out and explain the algorithms step-by-step. The subtitles/transcripts are a disappointment :( . Full of mistakes. Sometimes to the point of being useless or even worse - saying the exact of opposite of what the lecturer says. Since the lecturer sometimes is unclear - this is problematic. As usual - Graphlab Create sometimes crashes, however there are explanations how to run the assignments using Scikit-Learn.

By Matt B

Apr 04, 2016

The content seems rather thinner than that of earlier courses in the specialization, and seems to get more so as the course progresses. (Week 6 is entirely spent on Precision and Recall, with only about 30 min of lecture.) It feels like there was a rush to get the course out and that corners may have been cut at the end.

And as others have mentioned, several very important classification topics are conspicuously missing.

By Alois H

Sep 23, 2017

Overall good explanations in the videos; however, too much reliance on GraphLab, so that it seems more like promotional course for the instructor's own software and company. Also, the course is generally a bit light on content - the only algorithms discussed are Logistic Regression, Decision Trees and AdaBoost. Spending a full week on precision & recall is way too much time.

By john f

Jul 20, 2020

This course needs to be re-created using new professors.

Way too lazy IMO.

Too many "trick questions", total confusion between Python 2.x and 3.x

Too theoretical, almost no practical examples

Quizes are very poor and give no "hints" or true workhtrough examples pror to test.

This is a problem with all Coursera, though.

By Vasilios D

Oct 05, 2016

I am afraid that this course is, to a large extend, a marketing tool for promoting the instructors' proprietary product. Its use is therefore limited for the practitioners that want a foundation on the free Python data/ML capabilities.

I would not recommend this course to my colleagues.

By Keith L

Nov 25, 2016

Not as polished/comprehensive as the previous courses (especially week1, week5 and week6). But useful techniques nevertheless.

By Stefan W

Oct 28, 2018

The speaker is very difficult to understand, and the environment for writing code is awful (web browser).