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I just read this blog post about Machine Learning, Clustering and Cluster Computing.
My question is, how do I get started in Machine Learning ?
You need to have basic knowledge of Calculus, Statistics, Probability, and Linear Algebra.
After that, you can build a roadmap for your career, or maybe you can find some Machine Learning roadmaps on google.
Machine Learning by Andrew NG is also a good course to start.
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I'm interested in applying machine learning algorithms for SAT solving procedures. Current trends on SAT solvers seems they use CDCL procedures.
Specifically, is there any small example to illustrate the idea?
The closest work I'm aware of is by Selsam et al., "Learning a SAT Solver from Single-Bit Supervision." See https://arxiv.org/abs/1802.03685
You should also look into Selsam's PhD thesis on this topic as well https://searchworks.stanford.edu/view/13250178, which has lots of other references.
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I am looking for beginner Machine Learning Linear Regression problems. I searched in Kaggle, but couldn't find a proper one. Can you please suggest me a beginner problem from Kaggle, or from any other platform?
Thanks in advance.
Kaggle has tons of linear regression notebooks and datasets to learn from, most popular ones are probably about house pricing (given certain house features predict it's price).
Here's a new one I'm looking forward to solve:
Ben & Jerry's flavours and ratings ---> products.csv
The main goal would be predict wich ice cream flavours are better accepted based on it's ingredients.
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I am trying to develop a system(machine learning model) to identify whether the land is agricultural or paddy (dry land or wet-land) and has undergone changes over a period of time.
Can anyone suggest suitable dataset which I can use for training purposes?
after a few research i have got some datasets websites Please do check it:
https://data.world/datasets/land
https://www.researchgate.net/publication/317914265_A_global_dataset_of_crowdsourced_land_cover_and_land_use_reference_data
https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/gdj3.32
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I am keen in learning machining learning. I know programming, just want to know some useful sites which will help in understanding the concepts of machine learning with simple examples.
As a beginner in Machine Learning you should start with the book
Pattern Recognition and Machine Learning, by Christopher M. Bishop
There are some prerequisites other than programming are Linear Algebra, Probability theory, etc. i.e. you should have a strong background in Mathematics. Although the book I suggested covers the common mathematical frameworks needed for understanding Machine Learning in its introductory chapters.
Moreover, you should practice implementing different learning algorithms (start from smaller ones) to grab the concepts well. As Andrej Karpathy said,
...everything became much clearer when I started writing code.
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I was curious if anyone knew of something like this flowchart but for Computer Vision tasks? Specifically for OpenCV would be most ideal.
Or any references with best practices, and common patterns for Computer Vision problems?
That's a monumental task. The best I could find is from this article and it's a little bit old:
Maybe it's a good time to commit to FlexCV on Kickstarter.com, a GUI for OpenCV that allows you to create complex algorithms in a matter of minutes by connecting graphical elements together. It's an alternative for Adaptive Vision, but purely based on OpenCV features.