Jeff Leek, PhD
8.7/10
(Our Score)
Product is rated as #84 in category Machine Learning
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.
Instructor Details
Votes: 0
Courses : 8
Specification: Practical Machine Learning
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49 reviews for Practical Machine Learning
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| Price | Free |
|---|---|
| Provider | |
| Duration | 14 hours |
| Year | 2015 |
| Language | English ... |
| Certificate | Yes |
| Quizzes | Yes |
Jerome S P –
Very good explanation! Trying to do the examples help me understand more plus the explanation which is not on the slide helps a lot. Thank you
Jorge B S –
I have passed 5 courses of this specialization and I am not fully satisfied with this one. The course is a very brief introduction to practical machine learning, as the concepts are explained very fast and without a minimum level of detail. Then, most importantly, there are no swirl exercises, so it is quite difficult to put the acquired knowledge into practice. The other 4 courses I took, they all had swirl and that was great. Nevertheless, the course project is quite nice in order to face a real machine learning problem.
Erik K –
Very good. Learned a lot
John D M –
A fast–paced course that got me going in building models and understanding the pitfalls. I felt the directions for the final project were somewhat poorly worded and vague (and calling one of the files test when it was not to be used for testing the model was initially confusing), but overall it was good. I would have liked to have seen the final project uploaded as a secure file as has been done in other courses, and Github was a poor platform for viewing html files. Additionally, the question about out of sample error caused many people problems in the projects as they confused it with with Accuracy, yet it was weighted heavily in the rubric: I’d like the instructors to review the materials how that material is presented in terms of models. I got 100%, but as always you have to pay very close attention to the rubric. As always with this specialization, you are really just given a taste and there is no way you can fully explore all the material and references presented., but it is enough to get you going and wanting to come back and explore the material more.
Andrew –
Great intro to machine learning. Covers the basics to allow you to being using ML concepts on your own.
Oliver S –
A reference solution for the quiz questions as there are in some other courses in this specialization would have been nice, since I got sometimes very different results using the newest versions of the libraries and I’d really like to know, if I made any big mistakes and it’s not only because of my setup.
Klever M –
It was a great overview of the fascinating word of ML.
Gustavo C G –
Excellent introduction to machine learning. Great examples and detailed explanations, as usual
Manuel E –
Good course, but either explanations are too fast paced for the level of difficulty, or my neurons have began to decay with age.
Rok B –
The material is well choosem but poorly explained. This course among all would need swirl excercises, or just more excercises in any form. Instead the lecturer rushes through the material. So in the end you do have some overview about machine learning in R but not enough hands on experie
Martin G –
Excellent course
Deogratias K –
I liked everything abt it
Khalid S A –
excellent course and very beneficial
Mary –
Very informational with good variety of code to take back and apply to projects.
Caio H –
I learned a lot in this course, but I would recommend taking the courses in order.
Davin G –
It’s an excellent crash course to machine learning but the stats part was rushed. Had to look up external resources to understand what was going on.
Andreas P –
Thank you for helpful learning.
Umair R –
Brian Caffo’s courses are, as always brilliant.
June K –
This course does not have the depth it needs, but I do learn a few valuable things. I suggest breaking this course into 2 courses and give more lectures on using caret package and other packages as well. Another thing is I could not ever find the correct answers for the quizzes, and most of the time has to guess and take the quizzes 3 times to get things right. I invested time and effort in doing the last project; but got a not so good grade due to peer review process. I got every requirement done and even have a direct link to my HTML final report but 2 out of my 4 my peer reviewers have limited knowledge of GitHub could not find my link to HTML file. That said with a higher level courses, peer review process has to be different.
Yohan A H –
I think it was a very fast course and I feel more real examples would have been useful,
Charbel L –
Excellent course. Shows how simple it is to start running models with machine learning…! Well done
Muhammad Z H –
learning alot
Robert S –
The lecture material is great, but the quiz material is in need of updating. R and it’s packages have gone through many updates since the problems were written so it is sometimes difficult to reproduce their results even with running the sample codes given after getting the answer correct.
Weiqun T –
This is a very good basic course for machine learning. I got the basic ideas and skills for it.
Connor B –
Really good exposure to machine learning and builds on the previous course in regression
Michael R –
It’s a mediocre intro to some machine learning tools. I think the course materials could be drastically improved.
Ben H –
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.
Ashwin V –
Best course
Rizwan M –
great course. could have explained more techniques in caret package with coding examples
Robert J C –
It gets harder but fun…R, as well Python and Matlab, can do AI well.
Yadder A G –
It’s the best course I’ve taken. It has all the basics about machine learning algorithms and more.
Chris R –
Excellent course for the basics of ML
Mehrdad P –
Overall was a great course for an overview of the techniques available.
Siying R –
This instruction is better than the last one because he can use examples that people from outside the medical world can understand. The quiz is harder than the final project. It requires students to do extra work to figure things out. I see the pattern where the instruction really is the door holder to you and you need to walk in the room and find what you need.
Diandian Y –
a broad coverage of content and very intuitive explanation for different algorithm. Good start point to learn machine learning.
Claudio F S –
The course is amazing. The use of training and testing to predict data analysis made me more fascinated and interested in Data Science. Very nice!
Abhilash R N –
This course is NOT for the beginner. Take time to finish all the beginner and foundation courses and then take time to learn R
Charles W –
I think some material might need to be revised, but I thought it was very interesting to see everybody’s model building code (and perhaps that can also help me in the future). While it is mixed with other notes, I have more detailed thoughts in this blog post: http://cdwscience.blogspot.com/2019/12/experiences–with–on–line–courses.html
Aki T –
Unfortunately, I didn’t think this topic was as good as the other courses in the Specialisation. Quizzes often references aspect that haven’t been discussed during the lessons, and the lessons itselves are often too high–level (although I reckon this is why the course is called “Practical”, and we might need several courses to thorough fully understand how each algorithm works).
BOUZENNOUNE Z E –
A Great course that should be taken along other books, tutorials, and papers, in order to get the most out of it.
gerson d o –
Perfect!
Tristan F –
Lectures were very clear and helpful! Professor Leek was great at breaking down the topics.
Camila M F F –
Great material and exercises.
Michael O D –
This is a great course, but it would be good to see it updated to use the newer evolution of the caret package, parsnip.
Pedro M –
Great!
Alex s –
Great course to learn the basics
Eduardo S B –
They explain nothing on the fundamentals of the machine–learning methods, nor how to know which method apply to a given problem.
Amanyiraho R –
The real understanding of Machine learning
Joseph I –
Material was very interesting but was covered at a very high level and a lot of additional learning was required.