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Avis et commentaires pour l'étudiant pour Apprentissage mechanique pratique par Université Johns-Hopkins

4.5
2,671 notes
500 avis

À propos du cours

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....

Meilleurs avis

JC

Jan 17, 2017

excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!

AD

Mar 01, 2017

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.

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326 - 350 sur 492 Examens pour Apprentissage mechanique pratique

par Jikke R

Aug 11, 2016

Very enjoyable and generally quite understandable introduction to machine learnings with hands-on approach through the course project. It was a bit too fast-paced and generic for my liking, but many options were offered and highlighted for finding additional learning documents and courses to be able to deepen the knowledge acquired in this course.

par Craig S

Feb 12, 2018

Not as detailed as some others in the specialization which is a shame but good none the less. The videos go through the info quickly so be prepared to go back over.

par Robert R

Jul 20, 2016

Just the right level of detail

par Ramiro A

Aug 31, 2016

Nice course, Gives a god insight on what can me done with R and Predictions

par Jason M C

Mar 29, 2016

Of all the JHU Data Science specialization courses I've had, this was by far the most enjoyable. I really liked how the class was more in the style of 'here's some techniques, now do whatever you want on the project.' Prior courses are, and understandably so, more constrained in the assignments. It's not until here that the student really has the tools to be able to flex their analytical muscles, and it pays off.

Also, of the three instructors, I am most favorable to Jeff Leek, who teaches this class. He communicates much clearer than Roger Peng or Brian Caffo. I find I learn more from his content than the others.

Lastly, I will say that this class doesn't hold a torch to University of Washington's Machine Learning specialization. That's expected since this is one class and that's a whole series of classes. If you're hungry for more after this one, I highly recommend UWash's Machine Learning specialization.

par Alfredo M

Aug 22, 2016

Excelente curso. Ótimo conhecimento dos instrutores.

par PATEL N P

Oct 07, 2016

Nice Course for every New candidate

par Kamran H

Feb 18, 2016

Pretty good overview of how to build some types of machine learning models through the caret library in R, but not much in terms of the theoretical underpinnings or why one method is better than the other or where it is most suitable.

par Yuriy V

Mar 10, 2016

I liked the course and found it informative, but wish there were more stuff on unsupervised learning neural network algorithms (SOMs). Learning about most used algos are great, but would also like to know other machine learning algos that are used concurrently.

par Brandon K

Mar 30, 2016

The lectures were great and engaging. I felt like they went too fast. Jeff says at the beginning that this is just an overview and points to some other resources. As an overview, this class works well. You can expect to learn a bit about what machine learning is and how to to do it using the caret package in R.

par Robert K

Nov 14, 2017

I realise that the course is practical machine learning, however I find myself wondering more about the 'whys' than the 'hows' after the course! Still, much benefit and many useful concepts covered which can be revisited in greater detail down the track.

I would also like to see the final assignment change subtly every so often as there are existing completions on the web and it's too easy/tempting for some to simply copy and paste.

par Daniel R

May 14, 2016

The course is really great, however it should last a little longer, 4 weeks is hard to accomplish

par Guilherme C

May 18, 2016

Title says everything. Practically and basically no theory explained. Good course though.

par Rui W

Aug 27, 2017

know some packages of machine learning using R

par Robert W S

Nov 22, 2016

Great intro to machine learning. Several algorithms with some ideas on sampling and pre-processing techniques are covered. Adding a textbook as done with some of the other data science classes would help, but other resources are referenced.

par Jeffrey E T

Mar 28, 2016

Good overview of available techniques and the Caret package. Will get you started in machine learning.

par Bassey O

May 03, 2016

Very informative course.

par Steve d P

Mar 20, 2016

Nice, other courses will go more in depth though.

par Chris M

Aug 14, 2016

Unlike the rest of the modules in this specialisation, this one was well taught, a good blend of theory and practice and well paced.

There were still a few issues with wording in quizzes (and some where there seemed to be two identical answers to one question, where one would be considered right and the other wrong - purely chance). In addition, the lack of consistency in how to submit assignments across the specialisation is frustrating, I'm not sure if it's supposed to be a way to show how to use github or something like that, but it shouldn't be the case.

par bhawani p

Jan 07, 2017

briefly summarised the machine learning algorithms. Good place to start!

par Robert O

Jul 27, 2017

The course subject matter was great but like the course 6 & 7 scenarios i found the lectures didn't reiterate or reinforce key takeaways that are easily confused. For example is cross validation when you split the data into a training and testing, when you have a separate unknown results set to test final training model on. Or does it require doing folds and then breaking each of those up into training and testing chunks. Or why is it not okay to use a model training function that internally does cross validation similar like randomForest documentation suggests. Also things like what the prediction accuracy implies in contrast to the model oob [ in ] sample error estimate and if that estimate is akin to the 1 - prediction accuracy on test data set, i.e. out of sample error estimate. Seems like liitle coverage was given to whether or not there are well known training models to use or if you literally need to try and compare the 1/2 dozen or so common ones out there every time to find out which one to use for a given dataset. Also left confused about overlapping use of words classification model training, i.e. are they synonyms for the machine learning based functions we use to try and fit models to data.

par Rohit P

Nov 13, 2016

Lectures were not very detailed.

Quizzes were good and challenging, but too many times the results didn't match the answers even when the random seed was set right

Final project should have been more challenging with more models to build and compare

par Carlos C

Aug 12, 2017

Excellent content so I give 4 starts. I stat less because the trainer speaks too fast.

par Romain F

Mar 22, 2017

Good course on the whole, learned a lot and enjoyed it, but it would need to be updated and corrected (certain bits of code don't work as they did when the course was produced, which can be pretty confusing). Would be nice also to add some more content at the end of the course : the lecture about unsupervised prediction felt rushed, and a proper conclusion opening up to the rest of the field would be useful. Anyway thanks again for this wonderful learning opportunity, keep it up ! Cheers

par alon c

Mar 10, 2016

Great Course, will be nice to have more projects to see how it goes with different data