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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 4.0 based on 409 ratings
Length 5 weeks
Starts Oct 5 (3 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.

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practical machine learning

Real practical machine learning!

Grat course =) im really happy about it The practical machine learning course is a booster for the data science aspirant.The concept taught by the Prof Jeff Leek is easily understandable.

ok I realise that the course is practical machine learning, however I find myself wondering more about the 'whys' than the 'hows' after the course!

good Good class to get the basics of Practical Machine Learning.

I did fine on the quizzes and assignments, but I only feel like I learned a minimal amount of machine learning, even practical machine learning.

It's an interesting topic, but without independent study I would have learned almost nothing due to the lack of any "practicals" in this "Practical Machine Learning".

For the same price, Analytics Edge at EdX is far better choice for practical machine learning.

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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.

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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.

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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.

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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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Rating 4.0 based on 409 ratings
Length 5 weeks
Starts Oct 5 (3 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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