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Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Classification von University of Washington

4.7
2,875 Bewertungen
479 Bewertungen

Über den Kurs

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)....

Top-Bewertungen

SS

Oct 16, 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!

CJ

Jan 25, 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses

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76 - 100 von 447 Bewertungen für Machine Learning: Classification

von Kumiko K

Jun 05, 2016

Fun!

von ramesh

Mar 31, 2016

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.

von Kan C Y

Mar 19, 2017

Really a good course, succinct and concise.

von Thomas E

May 12, 2016

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 :)

von Muhammad H S

Nov 02, 2016

Excellent

von Sergio D H

Jul 22, 2016

AWESOME COURSE!! Carlos and Emily are incredible teachers and the course contents are truly informative and well-paced for beginners.

von Darryl L

Oct 27, 2016

they do a good job explaining concepts in great detail so everyone can learn it.

von Norberto S

Oct 09, 2016

Excellent course with lots of practical exercises.

von 嵇昊雨

Apr 26, 2017

Great materials for learning Classification

von Filipe P L

Oct 03, 2016

Very good, sometimes is a little hard, but is very helpful and have a lot of practical exercises

von Jifu Z

Jul 23, 2016

Good class, But it would be much better if the quiz is open to those who doesn't pay.

von Manuel I C M

May 30, 2017

One of the best courses i've ever tried

von Shanchuan L

Dec 07, 2016

This is a perfect course

von LIU Y

Mar 22, 2016

best of the best, theoretically and practically

von B M K

Oct 16, 2016

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.

von Alexandre N

Dec 20, 2016

Excellent course with plenty of intuition and practical experiments.

von Kurt K

Apr 16, 2016

Excellent course !

von Yifei L

Mar 27, 2016

This is a very good course on classification as previous two.

Good explanation on topics like logistic regression, stochastic gradient descent. The assignments are well designed.

However the decision tree part should introduce entropy and gini which are mainly used for choosing the splitting feature. Also the random forest is worth discussing.

Overall, this is a good course which contains a handful of knowledge.

von Joseph F

Apr 05, 2018

Good course with many assignment to design the algorithm with your own code. But I think this course last a little bit too long.

von Ridhwanul H

Oct 16, 2017

As usual this was also a great course, except

⊃゜Д゜)⊃ decision trees ⊂(゜Д゜⊂

I am not saying presently anythings bad or incorrect, but I just dont feel familiar with this. It is one tough topic to understand. I think it would have been great if there were some videos and lectures where some programming example were also given, this would have helped out a lot in programming assignments.

Also there is another thing that I think should have been addressed (at least in one of the courses, unless you did it in course 4 the last one which I havent done yet) : vectorisation - instead of looping through each weight how it could be achieved at once through vectorisation.

von Leon A

Mar 10, 2016

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

von Igor K

Mar 16, 2016

very interesting and novice friendly, however some math (basic matrix calculus and derivatives) review worth doing

von Vladimir V

Jun 14, 2017

Awesome course! Highly recommend for anyone interested in machine learning.

von kazi n h

Jun 23, 2016

One of the awesome course on classification. Just so perfect for learning.

von Kaixiang Y

Jun 27, 2017

Very good instructors