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...is a collective term for characteristics that the two institutions share.
·-- PUBLISHER -----·> Technics Publications
·-- LECTURESHIP ---·> Machine Learning Using Python
·-- LECTURE DATE --·> 05-2017
04-2017 {Published}
·-- PERIOD in HRS--·> 02+
·-- SCALE ---------·> 10x50mb
·-- LECTURE LINK --·> https://goo.gl/L4xHiK
These videos cover the basics of machine learning, using Python. We
explain machine learning and its many uses, and then continue with
creating models and predicting data using several supervised learning
algorithms. You will master:
Concepts of machine learning, including the types of machine learning
models such as Linear Regression, Decision Tree, and Nearest-Neighbors.
Start-to-end Machine Learning, including loading raw data from external
sources, cleaning and converting data into desired formats, slicing the
data into features and labels, slicing the data into training and
testing datasets, instantiating machine learning models, fitting and
transforming data into the models, testing the models against testing
data, predicting values for new data, checking accuracy of the models,
understanding and testing precision and recall, tuning the models,
exporting fitted models, and importing them in other files.
Programming language, data structures and libraries, including Python
3+, Pandas, and Scikit-Learn.