Francisco Juretig
This course explores several modern machine learning and data science techniques in R. As you probably know, R is one of the most used tools among data scientists. We showcase a wide array of statistical and machine learning techniques. In particular:
Most of the examples presented in this course come from real datasets collected from the web such as Kaggle, the US Census Bureau, etc. All the lectures can be downloaded and come with the corresponding material. The teaching approach is to briefly introduce each technique, and focus on the computational aspect. The mathematical formulas are avoided as much as possible, so as to concentrate on the practical implementations.
This course covers most of what you would need to work as a data scientist, or compete in Kaggle competitions. It is assumed that you already have some exposure to data science / statistics.
Instructor Details
Courses : 2
Specification: 24h Pro data science in R
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6 reviews for 24h Pro data science in R
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| Price | $12.99 |
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| Provider | |
| Duration | 18.5 hours |
| Year | 2017 |
| Level | All |
| Language | English |
| Certificate | Yes |
| Quizzes | Yes |
Danijel Kop inovi –
It covers a lot of materials, but the exposition is sometimes too slow, sometimes too quick, almost all courses are very long (20 minutes) because there is a lot of fixing and tweaking on the go etc. If one practically knows the stuff taught here, it might be useful to get some additional ideas, but if you’re seeing something for the 1st time, it will be really hard to get heads and tails due to a quite confused presentation.
Friedrich von Recklinghausen –
Good basic content
Alexandre VASSILTCHENKO –
I see a lot of topics that interest me, but the explanation are … so … (i do exactly as he explain, i mean start a sentence, stop, think what i could say…). There’s is obviously a lack of preparation, looks like this morning he wake up and told himself, ok let’s do a course today. NO! you need prepare it, structure it. I’m very disapointed cause there’s knowledge and there’s lot of good topics, but these explanations are rubbish.
Prashanth –
The course is too long. But practical implementation is really good!
Neil Dunlop –
Practical applications of techniques are shown with real data and the examples are end to end. This really helps to understand why things are done and crucially, how they are done.
David Rebolo –
## Good: The course gives a good overview. The statistics part is very clear and complete. Many different data sets and real world examples were exposed. ## Should improve: The theory in machine learning is not deep enough (I had to compliment the course with youtube videos, papers, blogs…). There are not examples of ML for regression problems. Sometimes the course seems a little bit improvised. I think some power point presentation could help the student.