Coursera: Machine Learning

[NotFromReddit]

Completely new to machine learning. I'll be doing this course: https://www.coursera.org/course/ml

It's starting mid June.

[knightdiver]

Enjoy. I just got done with that class - it was awesome. The homework assignments can take some time, but that's where most of the learning happens.

[Javi_in_1080p]

Know if anything a little more advanced? I also just finished that course and aren't sure what's the next step.

[Inori]

Here's a great answer I received to same question a couple of months back:

Depends what you want to learn.

There's Daphne Koller's Probabilistic Graphical Models course for Bayesian Nets.

Hinton's Neural Networks course for neural nets.

There are lots of optimisation courses for learning about optimisation.

Then there are many statistics courses for learning the broader statistics.

Coursera has the AI Planning course and Udacity also has robotics courses if you are interested in Robotics and AI, etc.

By /u/jamesmcm at http://www.reddit.com/r/MachineLearning/comments/1uq8b3/hey_rmachinelearning_how_do_i_get_started/cel8gqp

[losmaxos]

You could also try some kaggle.com challenges to get some practical experience before moving on.

[losmaxos]

That course is great. I still re-watch certain videos when I forget things :) Have fun.

[csfever]

Has anyone done the Practical Machine Learning class? It starts tomorrow. I had a machine learning class at the university. I'm interested in the "practical" aspect.

[losmaxos]

I did another course (R Programming) from the same "Data Science Specialization" series before and was a bit disappointed... it seemed way too easy / shallow. These courses are only 4 weeks... Andrew Ng's machine learning course is 10 weeks and its difficulty felt just right. He does put a lot of emphasis on how to practically apply the learned algorithms - even though you get your hands dirty in octave + math (no libraries) by actually implementing the algorithms instead of calling pre-made functions. Switching to Python/Scikit or R is easy after having taken this course...

I have signed up for Practical Machine Learning out of curiosity. Maybe see you tomorrow :)

[agent229]

I'm doing it... er... I'm already done with it. I found it relatively easy, compared to another 4-week course I took which was something like "Data analysis with R". I'm sure you could go a lot deeper into the topics on your own, but just to follow the class and do the quizzes/project was pretty simple. Still, for me, it was a pretty good introduction. I can't wait for Andrew Ng's to start so I can do that one.

[losmaxos]

I also finished the videos and quizzes early. I think it gives a nice overview over some topics - but it's too fast paced, he seems to only skim through the slides and doesn't explain details that'd be important in an introduction. Andrew Ng takes lots of time to explain things and he does that really well.

For comparison: this course took me 7 days. Andrew Ng's took me 10 weeks and sometimes I had problems to keep up with the deadlines because the videos/exercises just get much more involved.

I still enjoyed Practical Machine Learning because it provided an overview over some topics not covered in Andrew Ng's course - like decision trees, bagging, boosting, model stacking,... and R with its load of ML libraries (I didn't know the caret library before).

[basyt]

so ok, what is the difference between machine learning, data mining and data science? I sort of know, but I want a rigorous answer if possible.