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

4.7
2,866 ratings
477 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

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!

CJ

Jan 25, 2017

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

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51 - 75 of 446 Reviews for Machine Learning: Classification

By Joseph F

Apr 05, 2018

Good course with many assignment to design the algorithm with your own code. But I think this course last a little bit too long.

By Kurt K

Apr 16, 2016

Excellent course !

By Yifei L

Mar 27, 2016

This is a very good course on classification as previous two.

Good explanation on topics like logistic regression, stochastic gradient descent. The assignments are well designed.

However the decision tree part should introduce entropy and gini which are mainly used for choosing the splitting feature. Also the random forest is worth discussing.

Overall, this is a good course which contains a handful of knowledge.

By Marcus V M d S

Oct 16, 2017

Another great course from this specialization. Tremendous effort in making the notebooks and assignments. I just think there could be recommended readings also.

By Ridhwanul H

Oct 16, 2017

As usual this was also a great course, except

⊃゜Д゜)⊃ decision trees ⊂(゜Д゜⊂

I am not saying presently anythings bad or incorrect, but I just dont feel familiar with this. It is one tough topic to understand. I think it would have been great if there were some videos and lectures where some programming example were also given, this would have helped out a lot in programming assignments.

Also there is another thing that I think should have been addressed (at least in one of the courses, unless you did it in course 4 the last one which I havent done yet) : vectorisation - instead of looping through each weight how it could be achieved at once through vectorisation.

By Andre J

Mar 18, 2016

These Machine Learning classes have been fantastic so far, really enjoying them. Very good coverage of topics and challenging exercises to drive home the learning. The effort put into developing the classes has been superb and I look forward to the rest of the specialization.

By ramesh

Mar 31, 2016

I come to know how can i applym machine learning conceps i real world scenarios . The instructors are so nice and always explaining in simple methods. Nice teaching abilities.. Glad to guided under this kind of instructors. Nice experience.

By Kan C Y

Mar 19, 2017

Really a good course, succinct and concise.

By Darryl L

Oct 27, 2016

they do a good job explaining concepts in great detail so everyone can learn it.

By Manuel I C M

May 30, 2017

One of the best courses i've ever tried

By Luis E T N

Jul 04, 2017

Excelent! Congrats!

By Dwayne E

Dec 21, 2016

Awesome course learned alot

By Igor K

Mar 16, 2016

very interesting and novice friendly, however some math (basic matrix calculus and derivatives) review worth doing

By Thomas E

May 12, 2016

A bit easy to get through the exercises bur otherwise a very enlightening and inspiring course. - This is btw a positive review if anybody should be in doubt after taking this course :)

By Jinho L

Jul 20, 2016

Very pragmatic and interesting

By Ornella G

Oct 01, 2016

I really enjoyed the topics presented and the fluid way to present them. It's a very well done summary of the classification models.

By Sudip C

May 03, 2016

Very detailed, Liked optional sections also. Loved it.

By Leon A

Mar 10, 2016

Course material selection, pace and presentation are all well thought out. This sequence of courses in the Machine Learning specialization is truly exceptional.

By Omar B

Feb 09, 2017

Great course.

By clara c

Jun 11, 2016

This course was great! I really enjoyed it and learned a lot.

By Kuntal G

Nov 03, 2016

Great course with detail explanation ,hands-on lab along with some advance topic. Really a great course for anyone interested in the field of real world machine learning

By Edward F

Jun 25, 2017

I took the 4 (formerly 6) courses that comprised this certification, so I'm going to provide the same review for all of them.

This course and the specialization are fantastic. The subject matter is very interesting, at least to me, and the professors are excellent, conveying what could be considered advanced material in a very down-to-Earth way. The tools they provide to examine the material are useful and they stretch you out just far enough.

My only regret/negative is that they were unable to complete the full syllabus promised for this specialization, which included recommender systems and deep learning. I hope they get to do that some day.

By Michael P

Dec 06, 2016

Awesome, not awful;)

By Sean L

Aug 31, 2016

wonderful course for beginner of ML

By Roger S

Sep 04, 2016

This course is COOL