OREILLY_MACHINE_LEARNING_USING_PYTHON_TUTORIAL-OXBRiDGE

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Appz
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OXBRiDGE
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Date
2017-05-25

NFO

                                           
░ ░▒▒▒▓▓█████▌░░ University of OXford & University of camBRiDGE ░░▐████▓▓▒▒▒░ ░
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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.                                           
     

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