Chevron Left
Вернуться к Machine Learning: Classification

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

Оценки: 3,636
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!

Фильтр по:

76–100 из 570 отзывов о курсе Machine Learning: Classification


15 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 :)

автор: Matthieu L

14 мар. 2016 г.

Great course!

Personally I could use a little more on the math behind the algorithms (e.g. Adaboost, why does it work?).

Also, would be great to add SVM in next iterations of this class.


автор: sudheer n

12 июня 2019 г.

The way Carlos Guestrin explains things is exquisite. if basics is what is very important to you, and can learn code implementation and libraries from other sources, this is the go to course

автор: Prajna P

18 дек. 2017 г.

I enjoyed this course a lot. The case study approach and the optional videos are full of intuitions and I love the way instructors put across the concepts very clearly ... Thank you so much

автор: Jenny H

1 янв. 2017 г.

All courses in this series are organized and taught in an extremely efficient manner. I have learned so much out of them and they have helped me with my current job and my next job search!

автор: Joshua A

20 сент. 2016 г.

Very thorough and engaging. Optional material allowed the more curious to learn a great deal about the topics. Simple, hands-on approach to classification algorithms. Highly recommended!

автор: Ronald B

20 окт. 2020 г.

This class was very interesting. I learned a lot. I really enjoyed the way the instructor presented the information. The programming assignments were challenging learning opportunities.

автор: Renato V

13 июля 2016 г.

A very good course, with effective intuitive explanations of what the algorithms are supposed to achieve and how. The exercises in Python help understand the topic and fix it in memory.

автор: Thomas E

12 мая 2016 г.

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 :)

автор: Rehan U

12 июля 2019 г.

Best Machine Learning classification course by far....

each aspect is explained in detail..but forum responses can be improved..

Great course for machine Learning beginners... loved it.

автор: Krisda L

24 июня 2017 г.

Great course. I learned a lot about Classification theories as well as practical issues. The assignments are very informative providing complimentary understanding to the lectures.

автор: Michele P

23 авг. 2017 г.

The course starts slow, but it gets more interesting from week 2. The assignments are more challenging than in Regression, but I have really enjoyed it. I highly recommend it!

автор: Dave M

30 апр. 2020 г.

Good Class. Program assignment have a bit too much hand holding, which made them easier and less useful than they might have been if they were allowed to be more challenging.

автор: Dhritiman S

9 февр. 2017 г.

These courses have been a perfect mix of theory and practice. Looking forward to the final two courses in the specialization getting released at some point in the future :)

автор: Phil B

13 февр. 2018 г.

Excellent overview of the most commonly used Classification techniques, providing the wireframe for us to write our own algorithms from scratch. Really enjoyed this one.

автор: Kuntal G

3 нояб. 2016 г.

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

автор: Shazia B

25 мар. 2019 г.

one of the best experience about this course i gained I learned a lot about machine learning classification further machine learning regression thanks a lot Coursera :)

автор: Fakrudeen A A

15 сент. 2018 г.

Excellent course - teaches linear, logistic regression and decision trees. It also teaches the most important concept of precision-recall. Overall highly recommended.

автор: Ji H K

9 авг. 2021 г.

This is my continuous course from regression. Even now I am using Classification for Business Field, it's very useful to understand basic logic with advanced level.

автор: Cenk B

28 апр. 2020 г.

It is technically and mathematically detailed and well-organized course and the assignments are also make me understand better about the algorithms and use details

автор: Marcus V M d S

16 окт. 2017 г.

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

автор: ZHE C

26 мар. 2017 г.

effective teaching and practice about decision tree, boosting, and logistic regression. Could have a little more practice on gradient boosted tree/random forest

автор: Niyas M

29 окт. 2016 г.

Amazing course! Packed with insights, reasoning and Carlos's humor and wit. Highly recommended for novices (along with the Machine Learning Foundations course).

автор: Leon A

10 мар. 2016 г.

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

автор: lokeshkunuku

11 июня 2019 г.

its been 3 weeks I started this course it was so nice and awesome. the lectures explaination and the ppt all were well crafted and easy to pick and understand.