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Avis et commentaires pour d'étudiants pour Apprentissage mechanique pratique par Université Johns-Hopkins

4.5
étoiles
2,756 évaluations
514 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.

AS

Aug 31, 2017

Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.

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26 - 50 sur 507 Avis pour Apprentissage mechanique pratique

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 Edward R

Dec 17, 2017

Great course, but it may take you more than the allotted 4 weeks if you intend to dig a bit deeper and pursue some of the additional resources referenced throughout the course. I would definitely recommend doing that, as there is A LOT of material to cover if you, like me, just have to know the details of what's happening behind the scenes.

par Rebecca K

Sep 24, 2018

This course gave a great basic understanding of some different machine learning algorithms and what they do. I now have a great practical understanding of how to implement them, and enough understanding of theory to know what I'm talking about and to be able to learn more about them in the future.

par Nirav D

Apr 02, 2016

This is a very useful course in Machine Learning that teaches us how to use the R based packages such as CARET for applying machine learning techniques. The course project helps understand how these techniques are applied in real world applications and develop useful insights.

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 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.

par Nino P

May 24, 2019

It's good that they teach you basics of machine learing in R (caret package), but it's very introductory course. I definetly recommend this course to beginner, but I also recommend taking more courses on this topic (Andrew Ng's for example).

par Paula L

Dec 02, 2016

good course, but one who is serious about data science should view this course as a starting point since machine learning is a semester long course so I'd recommend follow up with machine learning course taught from Andrew Ng out of Stanford

par Bill K

Feb 10, 2016

Really good class. I think there were some small issues with the class project. Like all real world problems it was not entirely well specified and the data was a bit odd to use for a prediction exercise because it was time series data.

par Stephanie D

May 21, 2017

This was definitely a challenging course. I learned a lot about building and testing prediction algorithms. The course also helped me overcome the feeling of intimidation by providing excellent examples and a hands-on final assignment.

par Yusuf E

Oct 17, 2018

It would have been nice if there was an introduction to deep learning. Also, linear methods are discussed at length again which is not really necessary. Otherwise, great course to get you started on machine learning applications in R.

par Athanasios S

Aug 09, 2018

Great class! I wish you would do a little more explanation about what methods are best for which scenarios. If you did in fact explain that and it went over my head or I missed it, I apologize. Great class that I learned a lot from.

par Dave H

Feb 23, 2019

This was one of my favorite courses in the specialization as it was so easy to understand and follow. I think the basis I was given has really made me want to delve deeper into the topic and apply it to my career. Thank you!!!

par Pei-Pei L

Jul 27, 2017

This course covers a lot of information in a short time, but you'll feel very proud of yourself when you finish it! It made me feel much more comfortable with writing machine learning programs, and am ready for the next topic!

par Kristin A

Jan 09, 2018

Good intro to a topic that has a lot of power and a rich body of knowledge behind it. You can only scratch the surface in a four-week course, but I have been exposed to quite a range of tools in Practical Machine Learning.

par Samuel H

Feb 18, 2016

This was a very good introduction to machine learning and how to use machine learning packages in R. It would have been better if the class had been longer than four weeks, but I learned a lot for the length of the course.

par Mohammad A

Jan 17, 2019

Wonderful course and instructor, it was the best in the specialization courses so far.

One note is that for most of the methods the explanation was too much precise and short and needed to reinforce it by extra material

par manny d

Sep 10, 2017

Best course i have ever taken on Machine Learning! Excellent presentation and excellent reference sources. Machine Learning is not that hard as I thought it would be..please make more practical courses like this one.

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 Adam R

Nov 11, 2018

Best course in the data science series. It is practical, so if you are looking for something theoretical this will not be the course for you. Also good introduction the methods for model testing and validation.

par Massimo M

Apr 21, 2018

Very interesting course, materials are explained in an engaging manner. I would have loved to have a few more exercises to practice, but overall a good course to understand the most important concepts of ML.

par Ben H

Oct 07, 2019

Really nice introduction to machine learning in R. You wouldn't want to pack more than this in 4 weeks. Would be interested to see if this course adopts the recipes / parsnip / tidymodels in the future.

par Anuj P

Feb 21, 2019

This is the most interesting of all the courses in this specialization. Sometimes the content covered can be overwhelming. But the end result in the form of project assignment is worth all the efforts.

par Jerome C

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!

par Angel D

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.