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Practical Machine Learning

Data Science,

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.
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Rating 4.1 based on 546 ratings
Length 5 weeks
Starts Dec 7 (6 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Data Analysis Machine Learning

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What people are saying

caret package

You will learn how to use the caret package and learn how to implement ML algorithms.

Will leave with an understanding of a few ways to use the caret package.

Otherwise is a fairly comprehensive class, and a great tutorial on the caret package.

You will be able to use the Caret package in R to simplify your application, simplify pre-processing, perform automatic cross-validation/model tuning and generate various statistics about the model used by your ML algorithm.

Good Good in introducing caret package and getting some experience in running algorithms.

Jeff Leek is a great professor .The delivery of the course material is very clear and covers a lot of predictive methods by using mainly R's caret package.

Rather basic, nevertheless a good introduction to the topic of machine learning with R. Mostly concentrated on applications of the R caret package.

Just the right level of detail Good course for learning the basics of the caret package.

Very good content for beginner, lot of learning in machine learning special caret package in R. great course Extremely useful class!

This course mostly teaches the basic usage of the caret package.

Good overview of available techniques and the Caret package.

The use of the caret package in R is emphasized.

Feels like everything is solved using a caret package, while the back-end theory is only slightly touched.

The course focuses on using the Caret package in R to apply machine learning algorithms.

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introduction to machine learning

Good introduction to machine learning Extremely useful.

A great introduction to machine learning and it does a good job building on the material from the previous classes.

Quizzes were good and challenging, but too many times the results didn't match the answers even when the random seed was set rightFinal project should have been more challenging with more models to build and compare Good content as an introduction to Machine learning!

Nice introduction to machine learning in R. It is rather basic level, so it not for people that already know some basics related to regression and classification.

Very enjoyable and generally quite understandable introduction to machine learnings with hands-on approach through the course project.

A good introduction to machine learning.

I enjoyed a lot this module, I'll use at my daily work some of the features I learned Excellent introduction to machine learning.

Hands-on training, practical introduction to machine learning using R!

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

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.

could have explained more techniques in caret package with coding examples Best course Really nice introduction to machine learning in R. You wouldn't want to pack more than this in 4 weeks.

So in the end you do have some overview about machine learning in R but not enough hands on experie Excellent introduction to machine learning.

Good introduction to machine learning.

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

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data science specialization

This is the second-to-last course in the Data Science specialization from Johns Hopkins, and the final of three courses covering actual data analysis techniques (preceded by Statistical Inference and Regression Models).

Of all the JHU Data Science specialization courses I've had, this was by far the most enjoyable.

In my opinion, the best course of the entire Data Science Specialization Great course The videos are really tutorials on R functions for machine learning and data wrangling.

Nice One of the best courses in the Data Science Specialization, Superfluous but the existence of the package "caret" covers the gap of other libraries like "skilearn" of python By using the caret package, this course took a very pragmatic approach towards machine learning.

It is too short to cover more fundamental topics in machine learning, like how to choose an algorithm based on the problem and the data.I took this class just because I was engaged in the Data Science specialization.

I wanted to clear the Capstone project and get the Data Science specialization certificate.

You can expect to learn a bit about what machine learning is and how to to do it using the caret package in R. Of all the JHU Data Science specialization courses I've had, this was by far the most enjoyable.

A great Course, my favorite into the Data Science Specialization Enjoyed without reservation This is by far the most enlightening class in the whole specialization.

However, like those in the other courses in the Data Science Specialization, this course covers a wide range of subjects but tends not to have much depth.

When I compare this and other courses in the specialization to other moocs that I have taken including Machine Learning with Andrew Ng and the Stanford Online EdX Course Statistical Learning with Trevor Hastie and Rob Tibshirani, the somewhat cursory treatment of the topics in the Data Science Specialization becomes more noticeable.

So they are good for me, but I wonder to what degree do the courses in the Data Science Specialization actually make a person a "data scientist?"2.

Overall, I appreciate the courses in the Data Science Specialization and specifically this course.

Practical machine learning is the 8th course in the 9-part data science specialization offered by John Hopkins on Coursera.

Similar to other courses in the data science specialization, the course content is mainly static slides with voice- overs, but thankfully the slides are generally not overly cluttered and the voice-overs are of decent quality.

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andrew ng

if you really want to learn these concepts take Andrew Ng's class or read The Elements of Statistical Learning.

Should look at Prof. Andrew Ng's machine learning course for how to clearly convey an idea.

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 The best course of the specialization along with the statistical inference one - the final assignment is very fun to do, pretty much like a Kaggle competition.

As as standalone course on machine learning, it's probably best to take Andrew Ng's class on Coursera.

For that, learners will find Machine Learning by Andrew Ng a better alternative.

If you have taken Andrew Ng's machine learning class, it's not necessary to take this one.

For now, I think all other courses of the specialization were much more valuable for me than this one.I've also took Andrew Ng course on Machine Learning in the past, and my learning experience was much better.

Remember that it is a 4 week course and you cannot expect to learn the wide variety of concepts of Machine Learning and it cannot replace machine Learning by Andrew Ng which is far better in concepts.

My recommendation is taking (or auditing) the Andrew Ng's course (you have to work with MATLAB or Octave which I did not like) and this course as a complementary to learn how to work with some R packages and how to map ML concept to R programming language.

Intro to Statistical Learning, The Analytics Edge, Andrew Ng's Machine Learning, Learning from Data and others provide a deeper insight into Machine Learning.

I definetly recommend this course to beginner, but I also recommend taking more courses on this topic (Andrew Ng's for example).

I am planning [as per Dr. Leek hint] to take Dr. Andrew Ng course on Machine Learning by Stanford as I watched couple lectures on YouTube and they are just great and simple.

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training and testing

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.

This course introduces machine learning in R, including the basics of prediction, splitting data into training and testing sets, regression, trees, random forests and boosting all in the span of 4 weeks.

And these students had very little discussion to add beyond just code, which is strange since you would think anyone who spent time assessing four or five different models, with training and testing data sets, would have had a lot more material to discuss, and some specific accuracy numbers to share, but no.

The use of training and testing to predict data analysis made me more fascinated and interested in Data Science.

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high level

Using the caret package is too high level for a learner.

So why four stars vs five stars, of all the Data Science Certification courses that I have taken: i) some of the examples and quiz challenges don't work as they should, ii) Machine Learning is rapidly changing area - should be updated to reflect this and perhaps a high level taste of Deep Learning, iii) posting the Final Project is overly complicated relative to methods of the other courses - this should be cleaned up - still not clear how point to a github repo link and also have a rendered html page working from that same link - requires two links to present materials and must use default names like index vs. a project name.

The mathematics in this course are at a high level (similar to Statistical Inference) - and are presented at a pace that is challenging without significant background in the field.

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

This is too high level for a machine learning course.

The lectures are too fast and high level, with no allowance given for people who are unfamiliar with this area and attempting to learn it.

Material was very interesting but was covered at a very high level and a lot of additional learning was required.

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time and effort

Pure waste of time and effort.

I recommend that you put a little time and effort into writing all new items.

As it is, it appears that the good professors put a lot of time and effort into creating what are indeed a worthwhile set of classes.

I know that these class materials took considerable time and efforts to create.

I invested time and effort in doing the last project; but got a not so good grade due to peer review process.

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final assignment

The way that the final assignment had to be submitted on Github resulted in me spending 8 times longer on learning how to post my results than actually building the model - some more guidance here would have helped a lot as the process was very frustrating.

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.

Interesting the final assignment.

The course also helped me overcome the feeling of intimidation by providing excellent examples and a hands-on final assignment.

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swirl exercises

I definitively missed some swirl exercises and more flow diagrams in the slides.

swirl exercises, but last project totally worth my time.

course textbook, and some swirl exercises would have helped.

While the overview of the content seemed very reasonable both in scope and pacing, the lack of swirl exercises meant that the final project for the course was a bit jostling.

Then, most importantly, there are no swirl exercises, so it is quite difficult to put the acquired knowledge into practice.

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cross validation

Got confused how to perform cross validation and when.

Or why is it not okay to use a model training function that internally does cross validation similar like randomForest documentation suggests.

For example: When would I use which method (for example rf versus naive base), the last exercise about cross validation was not fully clear.

I know it worked well because I verified its accuracy with cross validation and out of sample error measurement, as well as on predictions using the 20 graded input observations.

Two or three of the papers seemed to be suspiciously near-identical copies of each other in terms of showing the same background, nearly the same data filtering steps, same data partitioning, nearly the same variety of models claimed to be evaluated, but were not actually explicitly evaluated, same omission to compute the error on in-sample (training) data, same choice of winning model of random forest, same list of non-selected models that were claimed to have been assessed but for which no code evidence was shown nor accuracy percentages discussed, same claim of use of cross validation, and the same omission of actually showing the use of such cross validation code despite the claim of its existence.

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real world

The course project helps understand how these techniques are applied in real world applications and develop useful insights.

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.

There's also very little about how to apply machine learning algorithms in real problems or advice about how to use caret for real world projects.

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johns hopkins

johns hopkins' courses very helped me In my view the course was useful but not as good as the previus ones I followed in the specializacion (such as regression models and stat.

The Johns Hopkins Data Science series is a mish-mash of often poorly designed assignments and typical last-century style lectures.

I would think their administration (and marketing people) would not be happy if they saw the Johns Hopkins brand being sullied with such poor courses that are widely available because of the Internet.

I give a sincere and big thank you to the instructors at Johns Hopkins for creating this course and the course sequence it is a part of, and letting me study your materials.

Also the course is about the 8th in the sequence of Johns Hopkins Data Science Specialization.

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Rating 4.1 based on 546 ratings
Length 5 weeks
Starts Dec 7 (6 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Data Analysis Machine Learning

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