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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
3,565件の評価
589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

SM
2020年6月14日

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 :)

SS
2016年10月15日

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!

フィルター:

Machine Learning: Classification: 76 - 100 / 557 レビュー

by Prajna P

2017年12月18日

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

by Jenny H

2017年1月1日

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!

by Joshua A

2016年9月20日

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!

by Ronald B

2020年10月20日

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.

by Renato V

2016年7月13日

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.

by Thomas E

2016年5月12日

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 :)

by Rehanuddin S

2019年7月12日

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.

by Krisda L

2017年6月24日

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.

by Michele P

2017年8月23日

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!

by Dave M

2020年4月30日

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.

by Dhritiman S

2017年2月9日

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 :)

by Phil B

2018年2月13日

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.

by Kuntal G

2016年11月3日

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

by Shazia B

2019年3月25日

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 :)

by Fakrudeen A A

2018年9月15日

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

by Cenk B

2020年4月28日

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

by Marcus V M d S

2017年10月16日

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

by ZHE C

2017年3月26日

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

by Niyas M

2016年10月29日

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

by Leon A

2016年3月10日

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

by lokeshkunuku

2019年6月11日

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.

by Leonardo T L

2020年10月7日

Great course about general classification approaches and techniques. The pace of the classes is great. One more time the professors surpassed my expectations!

by Bharat J

2018年1月19日

I wish we had 5th course too,All courses are well organized and can be completed with other tool.

Hope they also include SVM and start courses on deep learning

by Ganesan P

2017年2月6日

A very good course - understood a lot about classification and the understanding gained will help in reading text books like Ian Good Fellow for deep learning

by Alex L

2016年3月7日

Great courses as usual like the previous courses in this specialization. Cater for beginners who want to gain a strong foundation and practical usages for ML.