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Understand the basic concepts and importance of clustering, data mining and visualization
Learn to apply Hierarchical Clustering and k-Means Clustering algorithms for data grouping
Understand how to use Orange software for data analysis
Gain hands-on experience with regression models for predicting numerical values
Learn to evaluate classification model performance using metrics such as AUC, F1 Score, Precision, and Recall
Explore different classification models like Logistic Regression, k-nearest neighbors, SVM, and Neural Networks
Understand dimensionality reduction techniques like PCA, MDS, and t-SNE
Prepare and clean datasets for analysis using tools like Excel and Orange
Conduct extensive data processing and visualization, including practical exercises with COVID data and image classification
Utilize Orange’s workflows and widgets for advanced data analytics.
This course covers a comprehensive range of topics in data analysis, clustering, and visualization techniques using Orange software. It begins with an introduction to data mining with Orange, which requires no programming knowledge. Participants will learn clustering algorithms such as Hierarchical and k-Means Clustering, and practical applications with tools like dendrograms and box plots. The course delves into regression models for numerical predictions and evaluates classification models through various metrics like AUC, F1 Score, and Precision. It also explores other classification models including Logistic Regression, kNN, SVM, and neural networks. Dimensionality reduction techniques like PCA, MDS, and t-SNE are discussed to aid in data visualization. Comprehensive data preparation is taught using tools like Microsoft Excel and Orange, covering practical exercises on datasets like the Starbucks Survey, Titanic dataset, and Student Exam Performance dataset. The course concludes with a study on COVID data processing and visualization and image classification tasks using Orange.
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