Chevron Left
Machine Learning: Classification に戻る

ワシントン大学(University of Washington) による Machine Learning: Classification の受講者のレビューおよびフィードバック

4.7
2,990件の評価
493件のレビュー

コースについて

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)....

人気のレビュー

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

フィルター:

Machine Learning: Classification: 26 - 50 / 461 レビュー

by Willismar M C

Nov 19, 2016

Amazing Course Module, I learned a lot of concepts for classifications using Decision Trees, Logistic Functions, Boosting, Ensemble and way to attack problems. Also a lot of coding with Graphlab, I personally like to program by my own but I also appreciating the tool for the class and comparing my skills with other tools. Very cool ! Nice Class

by Richard N B A

Mar 09, 2016

A great course! Well presented, does not shy away from the mathematics (very nice optional units that go into more detail for the interested student!), keeps focus on the material and maintains the structure and feel of the specialization as a whole. It's great that we get to actually implement some of the algorithms. Strongly recommended!

by Muhammad W K

Aug 19, 2019

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 Shrikrishna S P

Oct 18, 2019

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 Saravanan C

Jul 08, 2017

Excellent effort by the tutors to simplify and motivate the learning process (it kept me engaged) One shouldn't forget that this is just a start NOT an end of acquiring the programming skills as it spoon feeds majority of the supportive (or) actual code!! (so please open a blank notebook and write ALL pieces of needed code as well)

by Ashish

Oct 26, 2016

I appreciate the way Emily and Carlos explain the concepts. Its very intuitive for beginners and optional sections give further details. The datasets used in programming assignments are taken from real world examples.

Overall an excellent course and really looking forward to completing the series.

Kudos to Carlos, Emily and the team.

by Rajat S B

Jun 13, 2016

Great course , It gives the idea of how we should do classification from scratch as well as understanding the concept of how to handle large dataset during training. Boosting is one of the most important technique as what I have heard in machine learning and it's great to understand the concept of it.

by Hugo L M

May 18, 2018

Very nice feelings from this course. Nice teacher, nice contents and very nice assignements, everything very well structured. As you can see the sentiment coming from my review is a clear +1, so I hope the algorithm looking for good reviews to show to other posible students chooses mine to show up!

by Abhijit P

Oct 25, 2017

Excellent course. Loved getting into the details of classification. This was a bit loaded with couple of quizzes as well as assignments in each module. Some questions were tricky and had to go through the videos again to figure out the correct answer. Carlos explained all the concepts very well

by Thomas K

Oct 29, 2018

In my opinion, so far the best part in the specialization series. The only thing, that was strange for me is that the effort required for programming varied a lot. So from week to week, it was difficult to predict how much time and effort would be needed to finish the assignments in time.

by Pardha S M

Jun 02, 2017

All the quiz and programming assignments prepared such away that student can easily get into the workflow, concentrating more on concepts without taking much overhead of programming yet need to think rigorously while writing that small portion of "YOUR CODE" parts on couple of occasions

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 Nguyen D P

Dec 20, 2017

This course is so good. I can understand the algorithm and know the way how i can apply this for real life. Thanks so much coursera.org and Washinton university made the wonderful job for everybody. After this course i changed vision, innovation and i think people like me.

by Uday A

Jun 15, 2017

Great learning experience. Thanks to Carlos and Emily! Loving every bit of this specialization. :)

It would help if there could be a small introduction to other types of classifiers (Naive Bayes, SVM etc), atleast pointing the student to external resources to try them out.

by Sundar J D

Apr 23, 2016

Overall a great course and has a very good instructor. Teaches you all the fundamentals behind classification algorithms and models. Contains very good assignments/projects that make you implement the models yourself to get a much better understanding of the concepts.

by Chintamani K

Oct 10, 2017

In detail course for understanding the various concepts of classification. Instead of relying on the libraries, the course focuses on teaching the algorithm implementation using coding language of user's choice. This helps in understanding the algorithms better.

by Rahul G

May 06, 2017

Excellent course except that week 7 th assignment based on ipynb notebook had some redundant questions. Otherwise a good course especially sheds light on Adaboost, ensemble classifiers and stochastic gradient with batch processing.

Thanks Professor Carlos.

by Sathiraju E

Nov 28, 2018

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 Tripat S

Jun 24, 2016

This is the best course ever that can happen in ML...I did not know anything, but after taking this specialization, my understanding of ML has dramatically improved

Would recommend without any reservation - Prof Gustrin and Prof Fox are the best!!!

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 Marios A

Mar 08, 2016

The course is really well structured and gives a solid understanding in the latest approaches in Machine Learning. However I would also like to see in this course more sophisticated math, because it matters and I think there are important.

by Bert B

Oct 20, 2016

Very well done course.

Would be nice to have many more very short examples during the lectures that match the formulas. This would help me understand the formulas much better since I do not have a calculus or linear algebra background.

by Akshay B

May 24, 2017

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 leonardo d

Dec 02, 2018

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 Jafed E

Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand