Machine Learning
Classification
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Rating | 4.5★ based on 466 ratings |
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Length | 8 weeks |
Effort | 7 weeks of study, 5-8 hours/week |
Starts | Jun 26 (40 weeks ago) |
Cost | $79 |
From | University of Washington via Coursera |
Instructors | Carlos Guestrin, Emily Fox |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Programming |
Tags | Data Science Machine Learning |
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What people are saying
machine learning
This continues UWash's outstanding Machine Learning series of classes, and is equally as impressive, if not moreso, then the Regression class it follows.
Excellent c The best course I could find to get a strong hold of the basics of machine learning.
I enrolled in this specialization to learn machine learning using GraphLab Create.
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 The course starts slow, but it gets more interesting from week 2.
Very nice course with good mix of machine learning concepts with maths, programming.
Highly recommend for anyone interested in machine learning.
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) really good course.
Fantastic Lecturers and very interesting and informative course Great beginner/advanced course for Machine Learning Classification!
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.
Before this course, I looked at machine learning as a difficult field which can't be understood no matter what.
This series is the series for someone who really wants to get a hold of what machine learning really is.
Highly recommended for novices (along with the Machine Learning Foundations course).
If you're entirely new to machine learning, you could find some value in this course.
This Machine Learning class and the rest of the Machine Learning series from the University of Washington is the best material on the subject matter.
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programming assignment
I am left wondering if the programming assignments were made easier over time given all of the hints and "checkpoints" for code that was already supplied.
Some instructions in programming assignments are not clear.
It's a great course, but the programming assignments are a little too guided.
It even explains precision recall and boosting which could be confusing in an easy to digest way.4/5 stars because the course could include multiple levels of difficulty for the programming assignment tasks.
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.
Using discontinued Graphlab in the programming assignment is a minus and low activities in the forum makes hard to find assistance from the communities or mentors but the course material itself is just great.
Great Information and organised course great course Theory Quizes are good, but programming assignment not so good for me.
All the quiz and programming assignments prepared such away that student can easily get into the workflow, concentrating more on concepts without taking much overhead of programming yet need to think rigorously while writing that small portion of "YOUR CODE" parts on couple of occasions One of the best courses i've ever tried It is really engaging and well explained.
Expect some difficulty if you use other tools like pandas - the programming assignment completely assumes you use SFrame.
Anyway, the programming assignments were terrific.
The programming assignments were fine, more focused on teaching the algorithms than trapping someone in the coding part.
The programming assignments are mostly pointless.
The lectures and programming assignments have been extremely beneficial to help me get a basic foundation of ML classification.
All of the courses lecture are great until it reaches week 5 where it's really hard to catch, the programming assignment doesn't give enough hints and lecture in this topic doesn't help much.
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learned a lot
I learned a lot about Classification theories as well as practical issues.
Learned a lot and enjoyed even more.
Learned a lot recommend!
Learned a lot.
Very pragmatic and interesting Learned a lot, great course!
I really enjoyed it and learned a lot.
Not as good as the Regression Course, but still very good.While I appreciate prof Guestrin's enthusiasm, I missed a little rigor and mathematical depth of the Regression's course by prof. Fox.I learned a lot, but I feel that regression clicked with me a little better than classification.But that's probably me.In either case, the whole series are awesome so far, better, in my opinion, than Anrdrew Ng's ML course on coursera,A small suggestion would be to switch the main toolset from the Graphlab to something more common, like Sci-kit learn and Pandas.
Learned a lot!
more topics like deep learning, neural networks need to be introduced 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 :) Very good course for classification in machine learning - top presentation documents - very well structured and practical good Brilliant course!
It doesn't count when the answers rely precisely on anomalies.I learned a lot, but only because I wrote my own code and was able to think more clearly about it, but that was somewhat of a side effect.All in all, a disappointing somewhat out of date class.
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decision trees
regularization) and then makes a very good introduction to decision trees and boosting.
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.
Better than the regression course The course walk through (and work through) concepts of linear classifier, logistic regression, decision trees, boosting, etc.
The insights into decision trees and precision-recall couldn't have been any better!
This course is very nice and covers some of the very important concepts like decision trees, boosting, and online learning apart form logistic regression.
Highly recommended course, looking under the hood to examine how popular ML algorithms like decision trees and boosting are actually implemented.
Mentioning the many different algorithms for learning decision trees would have been nice, without necessarily focusing on each of them in depth.
Decision trees and boosting were great.
Excellent course - teaches linear, logistic regression and decision trees.
The latter is more robust and applicable to nearly every classification problem (except decision trees), and so is a more versatile formulation.
As neither actually plays any roll in the training algorithm except as guidance for the gradient and epsilon formulas and as a diagnostic, the more versatile and robust approach should be preferred.The professors seem very focused on decision trees.
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easy to follow
Presented in very easy to follow steps with thorough coverage of all the concepts necessary to understand the big picture of each algorithm.
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.
Could have a little more practice on gradient boosted tree/random forest Presented content is rather clear and instructors are rather easy to follow.
The lectures are slow, clear, and easy to follow.
This is a perfect course Great course, easy to follow, higly recommended!
The good:-Good examples to learn the concepts-Good organization of the material-The assignments were well-explained and easy to follow-up-The good humor and attitude of the professor makes the lectures very engaging-All videolectures are small and this makes them easy to digest and follow (optional videos were large compared with the rest of the lectures but the material covered on those was pretty advanced and its length is justifiable)Things that can be improved:-In some of the videos the professor seemed to cruise through some of the concepts.
I understand that it is recommended to take the series of courses in certain order but sometimes I felt we were rushing through the material covered-I may be nitpicking here but I wish the professor used a different color to write on the slides (the red he used clashed horribly with some of the slides' backgrounds and made it difficult to read his observations)Overall, a good course to take and very easy to follow if taken together with the other courses in the series.
The quizzes can be a bit more challenging Very easy to follow and didactic.
A very good introduce machine learning course, it's clear and easy to follow.
Entertaining and the lectures are quite easy to follow.
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logistic regression
Starts with logistic regression (w. and wo.
Nice course but I would have expected more techniques (SVM for instance) effective teaching and practice about decision tree, boosting, and logistic regression.
This course teaches you how to implement logistic regression, decision tree, AdaBoost algorithm, and stochastic approach from scratch!
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carlos and emily
Thanks to Carlos and Emily!
Carlos and Emily are incredible teachers and the course contents are truly informative and well-paced for beginners.
Love Carlos and Emily!
Thank you Carlos and Emily.
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take this course
Anyway, take this course by any means if you have some programming experience and have little to no machine learning knowledge.
Don't take this course, and if you take it then only use GraphLab create when the authors give you no other option.
the person who wants to start career in machine learning must take this course!
I would recommend any aspiring data scientists to take this course.
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real world
And, of course, in the real world, computing resources (though plentiful) aren't infinite.
Carlos explained all the concepts very well It explains nicely a lot of useful topics and gives you the tools to build real world applications.
The datasets used in programming assignments are taken from real world examples.Overall an excellent course and really looking forward to completing the series.Kudos to Carlos, Emily and the team.
They are pretty close to covering just what you need to actually do machine learning in the real world and not dive too deep into topics that have no practical value.However:This course was a bit too thin, the last 4 weeks of the course contained little in depth informations and seemed to brush over allot of different topics that could have contained more information.
It really shows how to use machine learning in the real world.
I come to know how can i applym machine learning conceps i real world scenarios .
I also deeply appreciated the real-world examples in the lectures and real world datasets of assignments.Some may regret the absence of a few "classic" algorithms like SVM but Carlos definitely made his point about it in the forum and did not exclude the addition of an optional module about it.I found some of the assignments less challenging than during the Regression Course, but maybe I'm just getting better at Machine-Learning and Python ^^.Thanks again to Emily and Carlos for the brilliant work at this very promising specialization.
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well structured
Nice teacher, nice contents and very nice assignements, everything very well structured.
Very well structured.
The course is well structured and very well explained.
The programming assignments are well structured but if api's like pandas, numpy, scikit learn were used it would have made my life easy.
The course itself is well structured and introduce gradually the complexity.
The course is very well structured.
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really enjoyed
Really enjoyed this one.
The assignments are more challenging than in Regression, but I have really enjoyed it.
Very good, sometimes is a little hard, but is very helpful and have a lot of practical exercises I really enjoyed the topics presented and the fluid way to present them.
I really enjoyed diving in depths of the algorithms' mechanics (like Emily did in the Regression Course).
I really enjoyed learning this course on Machine Learning Classification!
But all in all I really enjoyed this course.
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stochastic gradient
New things for me like boosting (ensemble models), decision trees, stochastic gradient descent, online learning (which is not used much by big systems, instead they tend to do something different for incoming new data) and much more are introduced and explained in this course.
Also has a very good explanation about stochastic gradient descent.
Lots of ML concepts (Decision Trees, AdaBoost, Ensembles, Stochastic gradient, loglikelyhood etc. )
Some highlights stochastic gradient, boosting, and precision-recall trade offs.
While it is mildly interesting to learn about stochastic gradient descent, I think it would have been more interesting to have a discussion about how classifiers work in a parallelized computing environment or actually to try one out using Spark.
This is a very good course on classification as previous two.Good explanation on topics like logistic regression, stochastic gradient descent.
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Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Compensation and Classification Analyst $44k
Position Classification Specialist (Federal) $54k
DSHS Classification and Compensation Specialist $55k
Classification and Compensation Analyst $61k
Position Classification Specialist $62k
CLASSIFICATION COUNSELOR $64k
Classification Consultation $66k
Freight Classification Specialist $66k
Classification and Compensation Analyst 2 $74k
Customs Classification Specialist $75k
Trade Classification Analyst $106k
Senior Software Engineer - Classification $187k
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Rating | 4.5★ based on 466 ratings |
---|---|
Length | 8 weeks |
Effort | 7 weeks of study, 5-8 hours/week |
Starts | Jun 26 (40 weeks ago) |
Cost | $79 |
From | University of Washington via Coursera |
Instructors | Carlos Guestrin, Emily Fox |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Programming |
Tags | Data Science Machine Learning |
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