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15 Python Tips : How to Write Code Effectively

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 Here are some Python tips to keep in mind that will help you write clean, efficient, and bug-free code.     Python Tips for Effective Coding 1. Code Readability and PEP 8  Always aim for clean and readable code by following PEP 8 guidelines.  Use meaningful variable names, avoid excessively long lines (stick to 79 characters), and organize imports properly. 2. Use List Comprehensions List comprehensions are concise and often faster than regular for-loops. Example: squares = [x**2 for x in range(10)] instead of creating an empty list and appending each square value. 3. Take Advantage of Python’s Built-in Libraries  Libraries like itertools, collections, math, and datetime provide powerful functions and data structures that can simplify your code.   For example, collections.Counter can quickly count elements in a list, and itertools.chain can flatten nested lists. 4. Use enumerate Instead of Range     When you need both the index and the value in a loop, enumerate is a more Pyth
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20 Best Videos to Learn Machine Learning Quickly

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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 Continuous States
18. State-action Rewards
19. Advice for Applying Machine Learning
20. Partially Observable MDPs (POMDPs)

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