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Вернуться к Machine Learning: Classification

Отзывы учащихся о курсе Machine Learning: Classification от партнера Вашингтонский университет

Оценки: 3,667
Рецензии: 605

О курсе

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)....

Лучшие рецензии


14 июня 2020 г.

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 :)


15 окт. 2016 г.

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!

Фильтр по:

526–550 из 574 отзывов о курсе Machine Learning: Classification

автор: Nitzan O

25 апр. 2016 г.

The course is interesting and well taught. The professor is very enthusiastic and it makes the course fun to watch. The problem in my opinion is that the content is too superficial. It's completely lack of mathematical background and the programming exercises are sometimes no more than copy paste.

автор: ANIMESH M

4 сент. 2020 г.

The course is up to the mark but what i felt missing is about the coding . They didn't focus on implementation tasks simply gave the notebooks for the assignments.

Also S.V.M and random forest classifiers are missing.

From my side concluding all the experience , i will give a 6.5 out of 10.

автор: Kumar B

4 окт. 2017 г.

This course covers the basics of classification very well, but I would have liked optional sections on more advanced topics. Some of the quiz questions were a bit confusing. It would have been good if the exercises also dealt with unbalanced data sets in more detail.

автор: Neelkanth S M

8 апр. 2019 г.

The content is good but completing assignments is a real pain because they choose to deploy a unstable proprietary python library, which gives hard time installing and running (as of Q1 2019). The entire learning experience is marred by this Graphlab python library.

автор: D B

13 июня 2018 г.

Pros: Absolutely fantastic theory explanations. Establishes solid fundamentals. Cons: The bugs in test/notebooks could have not been rectified with new ones. Demands searching in discussion forum every time. Would highly recommend for starters!

автор: Eric A J C

5 авг. 2021 г.

The videos were excellent, and the extra material to delve in deeper in the subject were very nice. However, the programming assignments were mostly chunks of ready-made code, so not much is left to the learner.


22 сент. 2020 г.

Finalizo siendo muy confuso. El conocimiento de los videos opcionales no se le daba seguimiento, hasta el final en las tareas es cuando se usaba pero ya estaba fuera de contexto y era difícil entender.

автор: Supharerk T

6 июля 2016 г.

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.

автор: nazar p

29 июня 2017 г.

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.

автор: Rohit J

12 мая 2016 г.

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. :/

автор: Ilan S

23 нояб. 2016 г.

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

автор: Rahul S

17 июня 2020 г.

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

автор: Lawrence G

19 мая 2016 г.

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

автор: Tu L

27 июня 2018 г.

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

автор: Mounir

19 июня 2016 г.

Exercises for Scikit-learn users were not organised.

Course took too long to start

автор: Pier L L

26 мар. 2017 г.

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

автор: Dmitri B

6 июня 2017 г.

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

автор: Ashish C

31 мар. 2019 г.

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

автор: Matt T

12 апр. 2016 г.

Good, but overemphasizes niche software product (graphlab).

автор: Virgil P

18 февр. 2018 г.

The exercises/assignments are far too simple

автор: 陈弘毅

3 февр. 2018 г.

too simple

автор: Deleted A

13 авг. 2020 г.


автор: Omkar v D

14 авг. 2018 г.


автор: Rohan G L

29 авг. 2020 г.

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.

автор: Amit K

20 янв. 2018 г.

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.