Posts

Showing posts with the label tutorials

Featured Post

How to Work With Tuple in Python

Image
Tuple in python is one of the streaming datasets. The other streaming datasets are List and Dictionary. Operations that you can perform on it are shown here for your reference. Writing tuple is easy. It has values of comma separated, and enclosed with parenthesis '()'. The values in the tuple are immutable, which means you cannot replace with new values. #1. How to create a tuple Code: my_tuple=(1,2,3,4,5) print(my_tuple) Output: (1, 2, 3, 4, 5) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #2. How to read tuple values Code: print(my_tuple[0]) Output: 1 ** Process exited - Return Code: 0 ** Press Enter to exit terminal #3. How to add two tuples Code: a=(1,6,7,8) c=(3,4,5,6,7,8) d=print(a+c) Output: (1, 6, 7, 8, 3, 4, 5, 6, 7, 8) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #4.  How to count tuple values Here the count is not counting values; count the repetition of a given value. Code: sample=(1, 6, 7, 8, 3, 4, 5, 6, 7, 8) print(sample

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