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

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574 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

SM

Jun 15, 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 :)

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!

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101 - 125 von 542 Bewertungen für Machine Learning: Classification

von Farrukh N A

Feb 10, 2017

I found carols to be the best instructor in machine learning domain, he presented the algorithms and all core machine learning concepts in really great way.

von OG

Aug 03, 2016

A great combination between down to earth concepts and their implementations in python. Implementation of topics in plain python is what I enjoyed the most.

von Jane z

Jan 26, 2020

The hands-on approach is excellent. Not only I learned ML / Classification, I was able to practice Python skills and statistical skills as well.

THANK YOU!

von Nikolay C

Mar 16, 2016

Excellent course! I've learned these topics before, but many things were not clear enough. While learning this course my knowledge really improved a lot.

von Usman

Nov 13, 2016

I think support vector machines is an important topic which is missing. Anyway, the programming assignments were terrific. I really enjoyed this course!

von Andrea C

Sep 07, 2016

The course covers most important topics in depth and exercises are very interesting, them helps you to reason about some important theoretical concepts.

von Youssef R

Aug 23, 2017

This is really a wonderfull course, and i recommend it to anyone who want to master some important techniques in the trending field of machine learning

von Josef H

Nov 27, 2016

I like the detailed comparison between choosing different parameters for creating the classification model. I learn a lot of tricks for creating plots.

von Suoyuan S

Apr 21, 2016

This course is friendly to machine learning beginners for the learning material is easy to understand as well as the assignment is easy to accomplish.

von Sara E E

Mar 29, 2018

It is very intuitive and easy to follow.

I hope you add SVM and talk about linear/nonlinear decision boundaries in the next enhancement to the course.

von m w

Dec 24, 2017

While I enjoyed most of the exercises, I found some of the implementations to be more puzzle solving rather than deeply understanding the algorithms.

von Gunjari B

May 21, 2018

An absolute marvel of a course! In depth explanation to everything, detailed and important concepts explained so much at ease with Carlos' humour!

von RAMESH K M

Aug 01, 2016

The course has be described in a very precise manner. The instructor takes time to clearly explain the concepts and the importance of the same.

von Filipe G

Apr 02, 2016

The best machine learning course I took online. I've taken other coursera courses, and this is the most complete, comprehensive, and well made.

von Richard L

Oct 15, 2016

Great course. The lectures and programming assignments have been extremely beneficial to help me get a basic foundation of ML classification.

von Fan D

Feb 02, 2017

This course is alright. For some reason I liked the regression course more as this one was a little to simple in terms of the practical.

von venkatpullela

Nov 17, 2016

Course is really good. Assignments are taking too much time if you want to do the course rally fast, with questionable learning value.

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 Nitin D

Dec 18, 2018

Excellent lessons on this important topic Classification. I think all major areas were explained quite nicely, with proper examples.

von Dongliang Z

Mar 22, 2018

Excellent course! The teacher explained a lot of intuitions during the course. The optional part s are very interesting and helpful.

von Ornella G

Oct 01, 2016

I really enjoyed the topics presented and the fluid way to present them. It's a very well done summary of the classification models.

von Siddharth S

Jan 09, 2018

Excellent course and all the concepts have been explained very simply and with an element of fun.

Many thanks to Emily and Carlos...

von Alvin B K

Sep 28, 2020

This was a very great course. I got the confidence to use ML algorithms and concepts efficiently and also write my own algorithms.

von Gaurav c

May 22, 2019

Would have loved even more had Carlos explained his students gradient boosting as well. I liked the way of his taught in lectures.

von Ankur P

May 29, 2018

Loved the way our tutor (Carlos) explained the concepts to us. Things are getting clearer with each course in ML :) Many thanks :)