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PCA and Clustering Analysis Tool
Upload CSV data, standardize numeric features, compute PCA step by step, visualize PC1/PC2, and cluster samples with k-means.
.csv.csv
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Machine Learning
Learn core machine learning algorithms with clear explanations, diagrams, and practical intuition — from KNN to LSTM and Transformers.
24 tools/tools/machine-learning/all-toolsSame MuhammadLab theme
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Univariate, Bivariate, and Multivariate Analysis Explorer
Explore one-variable, two-variable, and multi-variable analysis using sample data or your own uploaded dataset.
.csv, .tsv, .txt
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Diffusion Models — Modern Generative AI (Explained)
Learn the diffusion idea: add noise, denoise step-by-step, and how text-to-image works at a high level.
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TF-IDF Tools - Calculator and Search Engine
Learn TF-IDF with editable example documents, term scoring, query ranking, cosine similarity, and top-keyword tables.
.txt.csv
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Classification Studio — Random Forest vs XGBoost
Interactive classification playground: upload a CSV, choose features + target, and compare tree-based classifiers (Random Forest mode and XGBoost boosting). See accuracy, confusion matrix, precision/recall/F1, and export Python code.
.csv.py
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Zoo Data TriSurface 3D Visualization
Draw and interpret a trisurface plot for zoo_data.csv with a browser-based 3D surface, Python command example, custom CSV upload, and student-friendly explanations.
.csv, .tsv, .txt
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Perceptron Algorithm Interactive Demo
Train a perceptron step by step, watch the decision boundary move, inspect weight updates, and classify new points.
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Transformers — Learn Self-Attention
Understand Transformers with self-attention intuition, why they scale, and common model variants (encoder/decoder).
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XGBoost — Gradient Boosted Trees (Explained)
Learn boosting intuition, why XGBoost works, and how regularization/shrinkage help.
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Regression Studio — Linear vs Polynomial vs Ridge vs Lasso
Interactive regression playground: compare linear regression, polynomial regression, ridge (L2), lasso (L1), and elastic net on one dataset. See the learned equation, R², MSE, and the fitted curve.
.csv.py
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BERT — Encoder-Only Transformers (Explained)
Learn what BERT is, masked language modeling, embeddings, and typical NLP uses.
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Random Forests — Bagging for Trees
Learn how Random Forests combine many trees with bootstrapping and feature randomness.
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CNNs — Convolutional Neural Networks (Explained)
Learn convolution, kernels/filters, pooling, feature maps, and why CNNs work for images.
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Vision Transformers (ViT) — Transformers for Images
Learn how ViT uses patches + attention for vision tasks and how it differs from CNNs.
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Decision Trees — Learn Splits & Impurity
Learn decision trees: splits, Gini/entropy, overfitting, pruning, and interpretability.
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Logistic Regression — Classification Basics
Learn logistic regression, decision boundaries, logits, and regularization for classification.
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Gradient Descent — Optimization (Explained)
Learn gradient descent intuition, learning rate, momentum, and why optimization can be tricky.
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LSTM — Learn Long Short-Term Memory Networks
Learn how LSTMs use gates and a memory cell to handle longer dependencies in sequence data.
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SVM — Support Vector Machines (Explained)
Learn how SVM separates classes with maximum margin, kernels, and where it performs best.
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K-Means — Clustering (Explained)
Learn K-means clustering: centroids, assignments, inertia, and how to pick k.
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PCA — Principal Component Analysis (Explained)
Learn PCA: variance, principal components, dimensionality reduction, and when to use it.
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Word2Vec — Word Embeddings (Explained)
Learn how Word2Vec creates vector embeddings and why semantics emerge from co-occurrence.
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KNN (K-Nearest Neighbors) — Learn the Algorithm
Understand KNN classification and regression with distance intuition, scaling tips, and how to choose k.
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Naive Bayes — Fast Probabilistic Classification
Learn Bayes’ rule, conditional independence, and why Naive Bayes is strong for text.
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