⛏️ Data Mining

Classification • Clustering • Association Rules • Regression • Anomaly Detection • Text Mining

Master the art of discovering patterns — from frequent itemsets to predictive models, deep learning, and big data analytics.

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Data Mining Algorithms & Techniques

Beginner

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Data Mining Basics

CRISP-DM, data preprocessing, feature engineering.

Data Preprocessing

Cleaning, normalization, discretization, missing values.

Exploratory Data Analysis

Summary statistics, visualization, correlations.

Decision Trees

ID3, C4.5, CART, entropy, information gain.

Naive Bayes

Bayes theorem, Gaussian, multinomial, Bernoulli.

K-Nearest Neighbors

Distance metrics, k selection, voting, weighted KNN.

SVM (Support Vector)

Hyperplanes, kernels, slack variables, SMO.

Random Forest

Bagging, feature sampling, OOB error, variable importance.

Gradient Boosting

XGBoost, LightGBM, CatBoost, boosting.

K-Means Clustering

Centroids, inertia, elbow method, k-means++.

Hierarchical Clustering

Agglomerative, divisive, dendrograms, linkage.

DBSCAN

Density-based, eps, minPts, noise handling.

Gaussian Mixture Models

EM algorithm, soft clustering, covariance types.

Apriori Algorithm

Frequent itemsets, support, confidence, lift.

FP-Growth

FP-tree, conditional pattern bases, efficient mining.

Eclat Algorithm

Vertical data format, tidsets, equivalence classes.

Linear Regression

OLS, gradient descent, R-squared, regularization.

Logistic Regression

Sigmoid, log-odds, MLE, classification.

Regularized Regression

Ridge (L2), Lasso (L1), Elastic Net.

Anomaly Detection

Isolation Forest, LOF, statistical methods, autoencoders.

Isolation Forest

Anomaly isolation, ensemble, efficient.

Text Mining

Tokenization, stemming, TF-IDF, word embeddings.

Topic Modeling

LDA, NMF, Dirichlet, latent topics.

Sentiment Analysis

Lexicon-based, ML, deep learning for sentiment.

PCA (Principal Components)

Eigenvalues, variance explained, feature projection.

t-SNE & UMAP

Manifold learning, visualization, t-distributed.

Deep Learning for Mining

CNNs for images, RNNs for sequences, autoencoders.

Graph Mining

PageRank, community detection, graph neural networks.

Stream Mining

Sliding windows, Hoeffding trees, online learning.

Big Data Mining

Spark MLlib, distributed algorithms, scaling.

Model Evaluation

Accuracy, precision, recall, F1, ROC, AUC.

Cross Validation

K-fold, stratified, LOOCV, bootstrap.

Feature Selection

Filter, wrapper, embedded methods, RFE.

Reduction

Selection

⛏️ Data Mining Mastery Roadmap

🔰 Foundation

Data prep, EDA, decision trees, naive Bayes

📶 Core

K-means, SVM, Apriori, linear regression, PCA

🔥 Deep Dive

Random Forest, XGBoost, DBSCAN, anomaly, text mining

🏆 Frontier

Deep learning, graph mining, stream mining, big data

⛏️ Uncover Hidden Patterns — Master Data Mining

Interactive notebooks, real datasets, and advanced analytics challenges.

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⛏️ Data Mining

Data Mining Basics

Data Preprocessing

Exploratory Data Analysis

Decision Trees

Naive Bayes

K-Nearest Neighbors

SVM (Support Vector)

Random Forest

Gradient Boosting

K-Means Clustering

Hierarchical Clustering

DBSCAN

Gaussian Mixture Models

Apriori Algorithm

FP-Growth

Eclat Algorithm

Linear Regression

Logistic Regression

Regularized Regression

Anomaly Detection

Isolation Forest

Text Mining

Topic Modeling

Sentiment Analysis

PCA (Principal Components)

t-SNE & UMAP

Deep Learning for Mining

Graph Mining

Stream Mining

Big Data Mining

Model Evaluation

Cross Validation

Feature Selection

⛏️ Data Mining Mastery Roadmap

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⛏️ Uncover Hidden Patterns — Master Data Mining