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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 ]

20 Best Videos to Learn Machine Learning Quickly

According to Coursera -Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. 1.      introduction, The Motivation Applications of Machine Learning 2.      An Application of Supervised Learning - Autonomous Deriving 3.      The Concept of Underfitting and Overfitting 4.      Newtons Method 5.      Discriminative Algorithms 6.      Multinomial Event Model 7.      Optimal Margin Classifier 8.      Kernels 9.      Bias/variance Tradeoff 10. Uniform Convergence - The Case of Infinite H 11. Bayesian Statistics and Regularization 12. The Concept of Unsupervised Learning 13. Mixture of Gaussian 14. The Factor Analysis Model 15. Latent Semantic Indexing (LSI) 16. Applications of Reinforcement Learning 17. Generalization to the Conti