Showing posts with the label tutorials

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How to Check Column Nulls and Replace: Pandas

Here is a post that shows how to count Nulls and replace them with the value you want in the Pandas Dataframe. We have explained the process in two steps - Counting and Replacing the Null values. Count null values (column-wise) in Pandas ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1    1 Column2    1 Column3    5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Panda

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