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How do I get started with machine learning? I have a couple of applications in mind, mostly time series predictions. But the machine learning field seems to be

by cstuder 10y ago

How do I get started with machine learning? I have a couple of applications in mind, mostly time series predictions. But the machine learning field seems to be vast and I don't know where to start.

anhari 10y ago
The r/MachineLearning subreddit has a pretty good wiki to get started. https://www.reddit.com/r/MachineLearning/wiki/index https://www.reddit.com/r/MachineLearning/wiki/index
sputknick 10y ago
I would recommend starting with a spreadsheet-sized dataset (no more than a few thousand records) where you want to predict one of the columns, use binary decision trees to try to predict it's value. Use either Azure ML Studio, or Jupyter with the Sci-Kit Learn library, depending on your comfort level with programming.
gregatragenet3 10y ago
This is a good introduction focused on tensorflow. https://www.youtube.com/watch?v=vq2nnJ4g6N0 https://www.youtube.com/watch?v=vq2nnJ4g6N0 (Tensorflow and deep learning - without a PhD by Martin Görner) The ML/DNN rabbit-hole goes deep. If the video above leaves you wanting more, http://www.deeplearningbook.org/ http://www.deeplearningbook.org/ does a good job on drilling into more specifics for the various techniques used. The examples on the tensorflow webpage are also very good.
syntaxing 10y ago
http://cs231n.github.io/ http://cs231n.github.io/ is a great site for beginners. I've been following the site along the Udacity Self Driving Car nanodegree. The CS231 material has helped me understand the concepts significantly. Edit: I should mention that the class mainly focuses on neural networks and image recognition. However, once you have the foundation, you can apply your skillset to a vast range of applications.
- timdorr 10y ago
  The Udacity ML course is gradual enough to avoid overwhelming you, but really in-depth: https://www.udacity.com/course/machine-learning--ud262 https://www.udacity.com/course/machine-learning--ud262 Definitely recommend that as a good starting point. Isbell and Littman can be a bit cheesy at points, but they're very clear and thorough.
jray 10y ago
The next course: Deep learning is primarily a study of multi-layered neural networks, spanning over a great range of model architectures. This course is taught in the MSc program in Artificial Intelligence of the University of Amsterdam. In this course we study the theory of deep learning, namely of modern, multi-layered neural networks trained on big data. The course focuses particularly on computer vision and language modelling, which are perhaps two of the most recognizable and impressive applications of the deep learning theory. http://uvadlc.github.io/ http://uvadlc.github.io/
imh 10y ago
Start with statistics. Seriously, just google time series modeling (this seems ok for a beginner https://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/ https://www.analyticsvidhya.com/blog/2015/12/complete-tutori...). Learn ARMA/ARIMA/etc. Don't worry that just because it isn't using deep nets that it isn't state of the art or won't get the job done well. That would be like thinking python's built-in sort function isn't sufficient because it doesn't use Spark.
ghego 10y ago
if you are based in the SF Bay Area you can come to Data Weekends (www.dataweekends.com). They are 2-day workshops to get started with Machine Learning and Deep Learning (full disclosure: I run them)

by cstuder 10y ago

How do I get started with machine learning? I have a couple of applications in mind, mostly time series predictions. But the machine learning field seems to be vast and I don't know where to start.

anhari 10y ago

The r/MachineLearning subreddit has a pretty good wiki to get started. https://www.reddit.com/r/MachineLearning/wiki/index https://www.reddit.com/r/MachineLearning/wiki/index

sputknick 10y ago

I would recommend starting with a spreadsheet-sized dataset (no more than a few thousand records) where you want to predict one of the columns, use binary decision trees to try to predict it's value. Use either Azure ML Studio, or Jupyter with the Sci-Kit Learn library, depending on your comfort level with programming.

gregatragenet3 10y ago

This is a good introduction focused on tensorflow. https://www.youtube.com/watch?v=vq2nnJ4g6N0 https://www.youtube.com/watch?v=vq2nnJ4g6N0 (Tensorflow and deep learning - without a PhD by Martin Görner) The ML/DNN rabbit-hole goes deep. If the video above leaves you wanting more, http://www.deeplearningbook.org/ http://www.deeplearningbook.org/ does a good job on drilling into more specifics for the various techniques used. The examples on the tensorflow webpage are also very good.

syntaxing 10y ago

http://cs231n.github.io/ http://cs231n.github.io/ is a great site for beginners. I've been following the site along the Udacity Self Driving Car nanodegree. The CS231 material has helped me understand the concepts significantly. Edit: I should mention that the class mainly focuses on neural networks and image recognition. However, once you have the foundation, you can apply your skillset to a vast range of applications.

timdorr 10y ago
The Udacity ML course is gradual enough to avoid overwhelming you, but really in-depth: https://www.udacity.com/course/machine-learning--ud262 https://www.udacity.com/course/machine-learning--ud262 Definitely recommend that as a good starting point. Isbell and Littman can be a bit cheesy at points, but they're very clear and thorough.

jray 10y ago

The next course: Deep learning is primarily a study of multi-layered neural networks, spanning over a great range of model architectures. This course is taught in the MSc program in Artificial Intelligence of the University of Amsterdam. In this course we study the theory of deep learning, namely of modern, multi-layered neural networks trained on big data. The course focuses particularly on computer vision and language modelling, which are perhaps two of the most recognizable and impressive applications of the deep learning theory. http://uvadlc.github.io/ http://uvadlc.github.io/

imh 10y ago

Start with statistics. Seriously, just google time series modeling (this seems ok for a beginner https://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/ https://www.analyticsvidhya.com/blog/2015/12/complete-tutori...). Learn ARMA/ARIMA/etc. Don't worry that just because it isn't using deep nets that it isn't state of the art or won't get the job done well. That would be like thinking python's built-in sort function isn't sufficient because it doesn't use Spark.

ghego 10y ago

if you are based in the SF Bay Area you can come to Data Weekends (www.dataweekends.com). They are 2-day workshops to get started with Machine Learning and Deep Learning (full disclosure: I run them)