Chevron Left
Retour à Apprentissage mechanique pratique

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

4.5
2,637 notes
498 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

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.

DH

Jun 18, 2018

Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.

Filtrer par :

226 - 250 sur 489 Examens pour Apprentissage mechanique pratique

par Jan K

Aug 02, 2017

A nice overview of the most popular Machine Learning algorithms. Also very thorough, given the limited amount of time. I recommend anyone interested to take it!

par Simeon E

Aug 02, 2017

Great Course. No so easy, as I expected, but, definitely, it worth all the time I've spent on it. Be careful: it requires a lot of self-studying and don't forget to read the Course Forum.

par Fernando M

Sep 04, 2017

Great material. Really enjoyed it

par VENKATESH G S

Oct 30, 2017

Good Approach......Valuable Course......!!!

par Matthew W

Mar 01, 2016

High level and brief overview but found it informative introduction into machine learning with R. The final project is fun and interesting.

par Rui R

Feb 06, 2017

One of the best courses in the Data Science Specialization,

par Kevin W

Feb 11, 2017

Great course

par Yi-Yang L

May 19, 2017

Good

par antonio q

Feb 27, 2018

it was great, simply though exhaustive, thanks a lot

par David Y

Feb 09, 2016

Enjoyed without reservation

par FARZAD R

Jun 16, 2017

Really wonderful and very practical course .

par Peter T

Feb 29, 2016

Love it.

par Triston C

May 27, 2017

This course really demystified machine learning, and provided practical steps and guidance on how to create predictive models. While I do wish there were more resources on how to tune models and investigate specific model parameters, I understand that there just wasn't enough time. I couldn't imagine a better course for a solid foundation in this skill.

par Vinicio D S

May 22, 2018

You will learn how to use the caret package and learn how to implement ML algorithms. If you want the theory behind it, you need to go to other courses

par YOGESH C

Jun 18, 2016

great course

par Rodrigo C

Oct 14, 2017

Great course.

par Artem A

Apr 14, 2016

Noiiice!

par Tasif A

Jun 16, 2017

Great Course. Must do it.

par HIN-WENG W

Feb 07, 2017

PML is a deep subject and this course is an excellent foundation for further studies. Prof Leek has taught brilliantly on the basic concepts of PML given the short time of 4 weeks. You need college level statistics to fully appreciate the theories of the PML's lectures.

par Philippine R

May 22, 2017

I learned so much in such a short period of time. Challenging, very hands on, great theoretical foundations!

par Mertz

Mar 20, 2018

Real practical machine learning!

par Albert C G

Sep 04, 2016

Fun course, also practical and useful

par yefu w

May 30, 2017

Great Course!

par Sarah S

May 31, 2017

I enjoyed detailed information and was very straight forward to understand.

par Rishabh J

Aug 22, 2017

All the major machine learning algorithms and techniques are provided in a way that you can begin using them right away. The course project also provides an opportunity to apply the different techniques learnt in class to a rather messy dataset.