MachineLearning: Difference between revisions
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This page is for people who are interested in machine learning. If you are interested in learning more, feel free to stop by Noisebridge on Wednesdays from 6:30 - 7:30 p.m. for a | This page is for people who are interested in machine learning. If you are interested in learning more, feel free to stop by Noisebridge on Wednesdays from 6:30 - 7:30 p.m. for a weekly talk on machine learning. | ||
Each week someone from the group discusses a new topic along with a selection of implementation notes that can help you apply machine learning to your projects of choice. | |||
For a selection of references, resources, and materials feel free to visit: | |||
https://github.com/AlephTaw/Hackers_Introduction_to_Machine_Learning | https://github.com/AlephTaw/Hackers_Introduction_to_Machine_Learning | ||
Talk Schedule: | |||
October 26, 2016 (Wednesday: 6:30 pm): Ross Story - Convolution Neural Networks and Deep Learning | |||
November 2, 2016 (Wednesday: 6:30 pm): (Open Slot) | |||
November 9, 2016 (Wednesday: 6:30 pm): (Open Slot) | |||
November 16, 2016 (Wednesday: 6:30 pm): (Open Slot) | |||
November 23, 2016 (Wednesday: 6:30 pm): (Open Slot) | |||
November 30, 2016 (Wednesday: 6:30 pm): (Open Slot) | |||
If you are interested in jumping in and are new to python, the scikit-learn ecosystem, or if you would like to review some of the mathematical foundations of machine learning, here are some materials which you may find help you get the most out of the class discussions and activities: | If you are interested in jumping in and are new to python, the scikit-learn ecosystem, or if you would like to review some of the mathematical foundations of machine learning, here are some materials which you may find help you get the most out of the class discussions and activities: |
Revision as of 16:47, 22 October 2016
This page is for people who are interested in machine learning. If you are interested in learning more, feel free to stop by Noisebridge on Wednesdays from 6:30 - 7:30 p.m. for a weekly talk on machine learning.
Each week someone from the group discusses a new topic along with a selection of implementation notes that can help you apply machine learning to your projects of choice.
For a selection of references, resources, and materials feel free to visit:
https://github.com/AlephTaw/Hackers_Introduction_to_Machine_Learning
Talk Schedule:
October 26, 2016 (Wednesday: 6:30 pm): Ross Story - Convolution Neural Networks and Deep Learning November 2, 2016 (Wednesday: 6:30 pm): (Open Slot) November 9, 2016 (Wednesday: 6:30 pm): (Open Slot) November 16, 2016 (Wednesday: 6:30 pm): (Open Slot) November 23, 2016 (Wednesday: 6:30 pm): (Open Slot) November 30, 2016 (Wednesday: 6:30 pm): (Open Slot)
If you are interested in jumping in and are new to python, the scikit-learn ecosystem, or if you would like to review some of the mathematical foundations of machine learning, here are some materials which you may find help you get the most out of the class discussions and activities:
Python 3:
https://docs.python.org/3/tutorial/
Pandas:
https://www.youtube.com/watch?v=5JnMutdy6Fw
Scitkit-Learn:
https://www.youtube.com/watch?v=L7R4HUQ-eQ0
Mathematical Foundations:
Linear Algebra:
https://www.khanacademy.org/math/linear-algebra/eola-topic
Calculus:
https://www.youtube.com/watch?v=wOHrNt9ScYs&list=PL590CCC2BC5AF3BC1&index=34
Probability:
https://www.khanacademy.org/math/probability
If you have questions about the course which cannot be answered here, feel free to stop by noisebridge or make an inquiry at: noisebridge.ml@gmail.com.