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Вернуться к Machine Learning: Classification

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

Оценки: 3,632
Рецензии: 599

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!

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51–75 из 568 отзывов о курсе Machine Learning: Classification

автор: Tripat S

24 июня 2016 г.

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!!!

автор: Bhavesh G

5 мая 2020 г.

During this course, I learned many new things like logistic regression, decision tree algorithm, boosting algorithm, etc. I would like to suggest this course to all those who want to learn about machine learning and make a career in data science.

автор: Prashant K

16 нояб. 2020 г.

Amazingly structured course. When I started the course, I did not realise that there would be so much content like Boosting and Stochastic Gradient Descent. It is truly a fantastic course with hands on learning. Cheers to Prof Carlos and team

автор: ramesh

31 мар. 2016 г.

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.

автор: Marios A

8 мар. 2016 г.

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.

автор: Bert B

20 окт. 2016 г.

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.

автор: akshay b

23 мая 2017 г.

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)

автор: leonardo d

2 дек. 2018 г.

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.

автор: Jafed E G

6 июля 2019 г.

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

автор: Manuel G

1 янв. 2019 г.

Really awesome course. Nice balance between practical uses, theory, and implementation projects. It's good they kept the "optional" videos for the more detailed discussion instead of just removing that material. Totally recommend it.

автор: Theodore G

21 окт. 2016 г.

An interesting series of Lectures in the important topic of Classification. The business case approach followed by the instructors provides great help to apply the required theoretical knowledge and further elaborate these methods.

автор: B M K

16 окт. 2016 г.

Challenging and Exciting Course. Lots of ML concepts (Decision Trees, AdaBoost, Ensembles, Stochastic gradient, loglikelyhood etc. ) are introduced and i believe this course is of extreme importance in laying the fundamentals of ML.

автор: Nitish V

6 июля 2017 г.

The course is well designed for both beginners and experts . The concepts are well explained and the assignments are really challenging. Best thing is , it talks more from practical aspects . The optional sections are really good.

автор: Vladimir B

2 янв. 2022 г.

Another incredible course from these two professors. They are incredible at explaining complex concepts. The videos and slides are incredibly helpful. And the exercises are challenging. I just wish Pandas was used as default.

автор: Ahmed N A

4 мая 2018 г.

The best course I could find to get a strong hold of the basics of machine learning. Presented in very easy to follow steps with thorough coverage of all the concepts necessary to understand the big picture of each algorithm.

автор: David E

21 авг. 2016 г.

very useful course : covers a range of very practical and useful topics I had heard about but didn't fully understand until taking this course. Some highlights stochastic gradient, boosting, and precision-recall trade offs.


12 мая 2020 г.

After watching Emily's Fox Regression wondered how much better could this professor be...But Mr Guestrin is truly great. Fantastic job, well explained difficult concepts. Probably the best classification course out there

автор: Shingala J J

13 июня 2020 г.

very good course

very good and intelligent instructor

overall very good content and passion course i had learned a lot

very good and awesome efforts by instructor carlose guestin well done

very happy with this course :)

автор: Iñaki D R

3 авг. 2020 г.

Excelent course !! The perfect choice if you want to discover for the first time basic classification algorithms, get some practice and knowledge with them, and at the end dive into more complex classification analisys

автор: Wenxin X

26 мар. 2016 г.

This specialization overall is pretty good. Personally I feel like Classification talks more about concepts and important ideas and requires less on coding comparing to Regression. Learned a lot! Love Carlos and Emily!

автор: Punam P

16 апр. 2020 г.

Very nice course for beginners to enhance the knowledge.. Thanks to Resp. Sir for your clear view for each concept which helps me lot. Thanks to Team & university of Washington.. Really enjoy the study in each module.

автор: Vishwajeet U O

1 окт. 2020 г.

Carlos is just an amazing teacher. I love his style of teaching, and his case-study approach is simply fantastic. I also liked the little jokes he made to make the harder parts of the course a bit more bearable. :)

автор: Maria C

7 мар. 2016 г.

One of the best online machine learning courses I have taken. Excellent explanation of many techniques on Classification. A great combination of theory and hands-on examples. Thank you, Professors Fox & Guestrin.

автор: Marcio R

14 июня 2016 г.

Curso excelente, desde o material, as atividades práticas e aulas. O fórum de discussões é repleto de pessoas interessadas em ajudar. Essa é a especialização a longa distância definitiva de Machine Learning.

автор: Sam P

14 нояб. 2016 г.

Informative with useful assignments and optional lectures that provide a deeper mathematical understanding. Great for newbies as well as more seasoned computer scientists looking to expand into new material.