5 ms·
Anyone know a good place to get started with Machine Learning?
by coss 9y ago
Anyone know a good place to get started with Machine Learning?
- j7ake 9y agoElements of statistical learning book
- cjauvin 9y agoIts companion book is also very nice (and way easier for a beginner): An Introduction to Statistical Learning with Applications in R. It is one of the only textbooks I have read from cover to cover in my life.
- j7ake 9y agoThanks for the suggestion that was the book I wanted to initially suggest I guess I remembered the names incorrectly
- j7ake 9y agoThanks for the suggestion that was the book I wanted to initially suggest I guess I remembered the names incorrectly
- j7ake 9y agoThanks for the suggestion that was the book I wanted to initially suggest I guess I remembered the names incorrectly
- save_ferris 9y agoDepends on what your goals are. I do very small projects on the side just for my benefit and I've found Siraj Raval's YouTube channel to be good for digging in without much of a knowledge barrier, as well as r/learnmachinelearning. This isn't sufficient to break into a ML job obviously, but it's helped me understand some of the basic contexts of the ML world
- _31 9y ago+1 for Siraj Raval's YouTube channel. Videos are quick, entertaining and give a good overview of a lot of ai/ml concepts. Some of the content is a little over the top (i.e. memes & breaking out into song), but overall really well put together.
- save_ferris 9y agoI agree that it's sometimes over the top, but I do prefer that over a presenter who's clearly qualified to speak on the topic but lacks the communication skills. The dude clearly cares about his productions, and I really appreciate that.
- thearn4 9y agoIt's such a broad cross-disciplinary area, it's tough to define a good entry point that is reasonable for everyone. But I'd say actually start with getting a solid foundation on statistics (point estimation, and hypothesis testing). If you're good with that, I'd learn a bit of DSP to get a feeling for how people in that world manipulate and clean digital signals. Some of that helps out later. FFT, DWT, PCA, ICA, Convolutional filtering, Kalman and PID filtering (if you're interested in online systems), things like that. They help especially in feature extraction for classification, but also for unsupervised methods. Past that, the hierarchy of methods to be learned and in what order is funny, I'm not sure there is a current consensus on that. The truth is that the best path kind of depends on your own goals. If you have a particular problem in your pocket that you want to solve, that actually helps a ton.
- drieddust 9y agoUnless you are very mathematical I will advise going through Jeremy Howard's course.[1][2] First part is available with videos and exercises. I have seen videos of second part being available on YouTube. I hope rest of the material will be available soon. [1] https://news.ycombinator.com/item?id=14640409 https://news.ycombinator.com/item?id=14640409 [2] http://course.fast.ai http://course.fast.ai
- nikivi 9y agoWe are making a user curated visual search engine to learn anything most optimally. Here is a mind map for Deep Learning for example : https://learn-anything.xyz/machine-learning/deep-learning https://learn-anything.xyz/machine-learning/deep-learning