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Voltar para Aprendizagem Automática na Prática

Comentários e feedback de alunos de Aprendizagem Automática na Prática da instituição Universidade Johns Hopkins

4.5
2,679 classificações
501 avaliações

Sobre o curso

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....

Melhores avaliações

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 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

por Ramiro A

Aug 31, 2016

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

por 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.

por Guilherme C

May 18, 2016

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

por Rui W

Aug 27, 2017

know some packages of machine learning using R

por Alfredo M

Aug 22, 2016

Excelente curso. Ótimo conhecimento dos instrutores.

por Marcus S S

Feb 25, 2017

Great course! The hands-on approach make it very useful for one to start doing some very interesting analysis in real life! Thanks a lot! You guys could only make some efforts in updating some classes and packages used in quizzes. But the rest was great!

por 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.

por Piyush P

Jul 13, 2017

good context

por Pieter v d V

Jun 28, 2018

Very quick overview. If you really want to know something about it read the reference books.

por Sabawoon S

Sep 14, 2017

Excellent course, very practical. Found the project challenging as preprocessing data required some knowledge of the limitation of the RandomForest method i.e. both train and test needs to have same classes of data with similar levels.

por Sheila B

Aug 09, 2018

I've been working my way through the whole track, and this was by far the most complex material--but it was easy to understand because the videos were so clear.

I do have one bone to pick, though: the quiz material relies on very old packages. Again and again I had to finegle something so I could answer a quiz question. That makes you guys look like you are lazily sitting back collecting money but not really doing your job as far as teaching goes. It's time for an update. How hard is it to run your quizzes on updated packages and offer answers that are current?

Aside from that, I find that you explain material very clearly and you are my first choice for picking up a new data science skill.

por Jorge E M O

Sep 07, 2018

The course rushes over a lot of concepts and it already shows its age - however, it's a pretty solid introduction to machine learning from a practical perspective. It will provide you with a lot of ideas for further investigation and exploration and in the end you'll end up with a wide vision of the machine learning process.

por Grigory S

Aug 28, 2018

A bit short on practical aspects of different models

por Qian W

Sep 09, 2018

need eva on my project

por Orest

Jan 22, 2018

It needs more mathematical detail. Otherwise is a fairly comprehensive class, and a great tutorial on the caret package. I recommend it, if you need to refresh concepts and get some practical exposure to caret.

por PATEL N P

Oct 07, 2016

Nice Course for every New candidate

por 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.

por 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

por alon c

Mar 10, 2016

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

por 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.

por Carlos C

Aug 12, 2017

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

por Anant S

Jun 30, 2017

good course for initial understanding of machine learning. SVM can also be included.

por Kevin S

Mar 03, 2016

Good introduction to machine learning, might suffer a bit from trying to cover too much ground in such a short time.

por Christian W

Jan 31, 2017

First 3 weeks are manageable and the final project is great! I had a lot of fun :)

por S M H R

Feb 10, 2016

A good course where you can learn how ML algorithms work practically.