Think Different

  Photo by Vanessa Loring at pexel Different Types of Machine Learning Algorithms: 1. Supervised Learning - Classification: - Use Cases: Classification tasks where the output is a category or label. - Algorithms: - Logistic Regression - Support Vector Machines (SVM) - Decision Trees - Random Forests - Libraries: Scikit-learn, TensorFlow, Keras, PyTorch Regression: - Use Cases: Regression tasks where the output is a continuous value. - Algorithms: - Linear Regression - Libraries: Scikit-learn, TensorFlow, Keras, PyTorch 2. Unsupervised Learning - Clustering: - Use Cases: Grouping data into clusters based on similarity. - Algorithms: - K-Means Clustering - Hierarchical Clustering - K-Median Clustering - DBScan - Libraries: Scikit-learn, TensorFlow, PyTorch Dimensionality Reduction: - Use Cases: Reducing the number of features while retaining important information. - Algorithms: - Principal Component Analysis (PCA) - Singular Value Decom...

  1. How do you handle missing or corrupted data in a data set? There are a number of ways to handle missing or corrupted data in a data set. The best approach will depend on the specific data set and the problem you are trying to solve. Here are some common methods for handling missing or corrupted data: Remove the rows or columns with missing or corrupted data. This is a simple approach, but it can lead to a loss of data. Impute the missing or corrupted data. This involves using statistical methods to estimate the missing values. There are a number of different imputation methods available, such as mean imputation, median imputation, and k-nearest neighbors imputation. Use algorithms that can handle missing or corrupted data. Some machine learning algorithms are able to handle missing or corrupted data without any preprocessing. These algorithms are often referred to as "robust algorithms." Here is an example of how to impute missing values using the Python programming lang...