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Python map() and lambda() Use Cases and Examples

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 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]

Machine Learning Tutorial - Part:2

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Machine learning is a branch of artificial intelligence. Using computing, you will design systems. These systems to behave with AI features, from your end, you need to train them. This process is called Machine Learning. Read my  part-1 if you miss it. The life cycle of machine learning Acquisition - Collect the data  Prepare - Data Cleaning and Quality  Process- Run Machine Tools  Report- Present the Results Acquire Data You can acquire data from many sources; it might be data that are held by your organization or open data from the Internet. There might be one data set, or there could be ten or more. Cleaning of Data You must come to accept that data will need to be cleaned and checked for quality before any processing can take place. These processes occur during the prepare phase. Running Machine Learning Scripts The processing phase is where the work gets done. The machine learning routines that you have created perform this phase. Reporting Finally, the