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

4.5
2,642 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.

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26 - 50 sur 489 Examens pour Apprentissage mechanique pratique

par Yap Y A

Mar 11, 2019

Instructor was clear in his explanation. Would prefer to have more hands on exercise for practice

par Premkumar S

Mar 16, 2019

Great course and farily challenging exercises! Thank You for putting this together!!

par Carlo G I

Dec 04, 2018

thank you

par Gaurav B

Dec 22, 2018

Loved the course

par Luis M M R

Dec 24, 2018

very good

par Dora M

Mar 30, 2019

Really enjoyed this class and learned a lot!

par David R

Jan 14, 2019

Great introduction to Machine Learning in R. Concepts explained very clearly and project gave opportunity to test out the concepts introduced to real data.

par André C L

Dec 13, 2018

very good practical experience using machine learning models, especially regarding PCA usage

par Keidzh S

Jul 15, 2018

Practical Machine learning helped me to achieve my personal goals. Algorithm of prediction became clear, that gives the understanding of main point of the data science.

par Oleksandr K

Jul 11, 2018

Great course! However, it would be good to learn about artificial neural networks as well.

par Laro N P

Jul 22, 2018

Good course, I miss more practice exercise because theory is always welcome but when we are capable to understand is doing real practice.

par Ivan Y

Mar 06, 2018

great intro to machine learning!

par sampath

Oct 13, 2017

Tough but very good course

par Raju G

Nov 26, 2017

Extremely useful.

par Evgeniy Z

Apr 12, 2016

Nice course.

par MD A

Jan 12, 2017

Excellent and useful course.

Some of the materials covered in Week 4 should be distributed to earlier week(s). The current Week 4 video coverage, quizzes, and the course project on accelerometer data is too much for the week, esp. if the student has lookup and review some key concepts from the resource links in the video slides. Video lectures are informative and easy to follow, although somewhat rushed in Week 4.

par Douglas M

Feb 01, 2016

Great practical whirlwind tour. Light on theory, however, but it's a good entry point to the field. Thumbs up!

par Alejandro B G

Feb 09, 2016

A great Course, my favorite into the Data Science Specialization

par Joseph

Dec 13, 2016

Awesome course. Jeff Leek does a truly amazing job at explaining very complicated concepts thoroughly and quickly. I'm surprised we went through as much material as we did. Out of the 9, this is one my favorites.

par Jair G

Jan 20, 2017

Top 3 course of this specialization.

par Javier A D

May 27, 2018

References were very usefull for doing deep analisys in the thems

Quices were challenge.I learn a lot solving them.

I mis the swirl sessions

par Camilla J

May 12, 2018

This course was really informative and extremely efficient by letting you know just the few basics needed to build some quite advanced models such as random forest..

par Ilia

Apr 27, 2016

One of the most valuable courses in the specialization!

par Aleksey K

Feb 10, 2016

Made many things clear. Perhaps, the best class in the series.

par Martin S

Feb 26, 2016

Very practical tools and also very encouraging!!