Learn Machine Learning vs Art of Stat

Learn Machine Learning – Guide Unlock the world of Artificial Intelligence and Machine Learning with a complete learning companion designed for everyone, from absolute beginners to tech enthusiasts. Learn Machine Learning – Guide helps you understand complex ML concepts through simple explanations, visual examples, and structured lessons. Explore everything from the basics of data science to advanced AI models in one clear and organized app. Key Features Step-by-step tutorials on Machine Learning and AI concepts Simplified explanations with practical examples Learn about data preprocessing, algorithms, and neural networks Understand how AI and ML are transforming modern technology Ideal for students, developers, and beginners Regularly updated content to keep learning up to date Why You’ll Love It Whether you’re starting your Machine Learning journey or refreshing your knowledge, this guide provides clear, concise, and structured lessons that build confidence and real understanding. No coding experience is required — everything is explained in a way that’s easy to follow and practical to apply.

The Art of Stat: Machine Learning app includes methods for supervised and unsupervised learning, lets you split data into training and test sets, visualizes all methods, including predictions and heatmaps, and lets you assess the accuracy of your algorithm by showing the confusion matrix and more. Included ML algorithms so far include: - Multiple Linear Regression (including categorical predictors and interactions interactions) - Multiple Logistic Regression (including categorical predictors and interactions interactions) - Discriminant Analysis (Linear & Quadratic) - Naive Bayes - K-Means Clustering Functionality: - Provides various datasets (Palmer Penguins, Wine Quality, Heart Disease, Iris Flowers, Credit Card Defaults, ...) or lets user upload their own CSV file - Split data into training and test sets - Standardize Features - Select continuous and/or categorical features (where appropriate) - Visualize all methods (Scatterplots, Heatmaps), predicted labels or posterior probabilities - Assess accuracy by displaying Confusion Matrix and accuracy statistics (including precision and recall) in multiple ways - Make predictions for new observations Modules in preparation: - Decision Trees & Random Forests - Nearest Neighbor