Learn jQuery Coding With AI vs Art of Stat

Master jQuery, JavaScript’s most powerful utility library, with 90+ lessons and AI-powered help. Whether you're just starting or brushing up on DOM traversal, Learn jQuery guides you from basic setup to advanced selectors, event handling, and DOM manipulation. - What You'll Learn: Introduction to jQuery and Setting It Up Understanding jQuery Syntax and Script Usage jQuery Selectors including IDs, classes, and element targeting Deep-dive into DOM Traversal and DOM Manipulation Working with Forms using jQuery Capturing mouse, keyboard, and form input with Event Handling Adding, Removing, and Editing Elements dynamically Real-world jQuery use in JavaScript and front-end development Built-in AI Assistant — jQuery Help On Demand Stuck on a concept like $(document).ready() or .on()? - Use the in-app AI Assistant to: Explain a jQuery function Show the syntax Debug errors Give examples for selectors, events, DOM manipulation Find the best practices in frontend JavaScript - Progress Tracking Stay focused and motivated with streak tracking, lesson completion, and visual progress. See your growth through the Learn jQuery roadmap, including topics like: jQuery Selectors Events DOM Manipulation Form Handling jQuery Syntax and Setup - Find Anything Fast Use the search bar to instantly return lessons on: form, selector, class, script, css, html, DOM, element, input, events, functions - Start learning jQuery now — with built-in AI assistance, interactive breakdowns, and a structured learning map. Whether you're learning JavaScript for web development or need a shortcut to master jQuery events, this is your complete learning toolkit!

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