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Opiniones y comentarios de aprendices correspondientes a Machine Learning: Classification por parte de Universidad de Washington

3,667 calificaciones
605 reseña

Acerca del Curso

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)....

Principales reseñas


14 de jun. de 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 :)


15 de oct. de 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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26 - 50 de 574 revisiones para Machine Learning: Classification

por Dauren

27 de ene. de 2018

I loved this course! It is designed in a way that both beginner and more advanced student can grasp knowledge. New things for me like boosting (ensemble models), decision trees, stochastic gradient descent, online learning (which is not used much by big systems, instead they tend to do something different for incoming new data) and much more are introduced and explained in this course. Recommend 10/10.

por Raj

1 de abr. de 2017

This is a great course on ML - Classification that introduces one to the various techniques available in classification and to understand the algorithm under the hood. The course also explains the process, approach for each technique along with the methods to evaluate the results. Overall this takes the student through the next steps of learning classification algorithm from the foundational courses.

por Norman O

19 de feb. de 2018

I really liked this section on classification. Like with the regression course, complex concepts were explained well with nice examples and assignments. The only issue I had was that some of the coursework can be computing intensive (no surprise there). On the other hand, you really do learn by doing. And, of course, in the real world, computing resources (though plentiful) aren't infinite.

por Kevin

7 de ago. de 2019

Great course for beginner to intermediate data science enthusiast! This course teaches you how to implement logistic regression, decision tree, AdaBoost algorithm, and stochastic approach from scratch! There's also some assignment to learn how to implement those algorithms in our preferred library. Would be great if Carlos & Emily can bring another advanced machine learning course!

por Anwarvic

4 de dic. de 2016

This course is awesome, specially the assignments. In this course, I've implemented most of the famous ML algorithms that our world is now using.

I can't describe how happy I am. Before this course, I looked at machine learning as a difficult field which can't be understood no matter what. Today, I'm capable of doing some great effort.

Thank You so much :)

por Mansoor A B

2 de may. de 2016

I think this is an excellent course to give an idea about the machine learning concept of classification. I felt the lectures were to the point, straight forward and more importantly dealt with practical issues and solutions. The assignments are pretty cool, though large amount of code is written at a few points - I still found them pretty engaging.

por Willismar M C

19 de nov. de 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

por Richard N B A

9 de mar. de 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!

por Muhammad W K

19 de ago. de 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.

por Shrikrishna S P

17 de oct. de 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.

por Saravanan C

8 de jul. de 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)

por Ashish

25 de oct. de 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.

por Rajat S B

13 de jun. de 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.

por Hugo L M

18 de may. de 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!

por Abhijit P

25 de oct. de 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

por Thomas K R

29 de oct. de 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.

por Pardha S M

2 de jun. de 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

por Andre J

18 de mar. de 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.

por Phuong N

19 de dic. de 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 and Washinton university made the wonderful job for everybody. After this course i changed vision, innovation and i think people like me.

por Uday A

14 de jun. de 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.


29 de ago. de 2020

Very informative and understandable course. Teaches the crucial basics behind the machine learning algorithms and introduces many techniques as well. The self learning that comes with the programming assignment tasks also improves one's bug handling skills. Loved it!

por Sundar J D

23 de abr. de 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.

por Chintamani K

10 de oct. de 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.

por Rahul G

6 de may. de 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.

por Sathiraju E

28 de nov. de 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.