Machine learning is a field of computer science that uses statistical techniques to progressively improve the performance of the computer program in detecting patterns in data. The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.
To begin using this project, follow the following options to get started:
- Clone the repo:
git clone https://github.com/codexponent/machinelearningprerequisite101.git - Fork, or Download on GitHub
- Fork or clone this repository and run on your IPython Notebook
- Easy to learn topics with enough descriptions
- Clean code
- The Full Documentation is should be searched by each topics respectively.
- Issue Tracker: https://github.com/codexponent/machinelearningprerequisite101/issues
- Source Code: https://github.com/codexponent/machinelearningprerequisite101
- Contributors: https://github.com/codexponent/machinelearningprerequisite101/contributors.txt
If you are having issues, please let us know. I have a mailing list located at: sulabh4@hotmail.com
- https://en.wikipedia.org/wiki/Machine_learning
- https://courses.edx.org/courses/course-v1:UCSanDiegoX+DSE200x+1T2018/course/
- http://aidevnepal.org
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