MNIST Digit Data Case Study For Dimensionality Reduction.Recruitment and Factory Salary Case Study.Insurance Data And Scrap Price Regression Case Study.r2, adjusted r2, mean squared error, etc.Confusion matrix – To evaluate the true positive/negative, and false positive/negative outcomes in the model.Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.Principal Component Analysis – PCA follows the same approach in handling multidimensional data.Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.Dimensionality reduction – Handling multidimensional data and standardizing the features for easier computation.K-means – The K-means algorithm that can be used for clustering problems in an unsupervised learning approach.Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.K-Nearest Neighbors – A simple algorithm that can be used for classification problems.Gradient Descent – The gradient descent algorithm is an iterative optimization approach to finding the local minimum and maximum of a given function.Support Vector Machine – SVM or support vector machines for regression and classification problems.Random Forest – Creating random forest models for classification problems in a supervised learning approach.Decision Tree – Creating decision tree models on classification problems in a tree-like format with optimal solutions.Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.How to optimize the efficiency of the clustering model. How to evaluate the model for a clustering problem.How to train the model in a clustering problem.Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.How to optimize the efficiency of the classification model.How to evaluate the model for a classification problem.How to train the model in a classification problem.Introduction to classification problems, Identification of a classification problem, dependent and independent variables.How to optimize the efficiency of the regression model.How to evaluate the model for a regression problem.How to train the model in a regression problem.Introduction to regression problems, Identification of a regression problem, dependent and independent variables.Introduction to scikit-learn, Keras, etc.
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