MetaCyberGuru https://validator.w3.org/feed/docs/rss2.html Project: Build a Secure, Cited RAG Assistant Capstone: Build a Multilingual Knowledge Discovery System Project: Compare Five Transformer NLP Tasks Project: Build a Semantic Duplicate Detector Project: Build a Reviewed Support Ticket Triage System Project: Build an Explainable TF-IDF Search Engine Project: Build an Auditable Entity and Relation Extractor Project: Audit a Reproducible Text Preprocessing Pipeline Capstone: Build a Production-Ready Data Mining System Project: Investigate Transaction Patterns and Anomalies Project: Reduce Dimensions and Validate a Cluster Solution Project: Benchmark Classifiers and Diagnose Their Errors Project: Build and Audit a Leakage-Safe Baseline Project: Build a Clean Analytical Dataset and Star Schema Project: Build a Dataset Profiler in Python Project: Build a Data Discovery Brief That Can Be Reviewed Evaluate Retrieval and LLM Answers with Calibrated Rubrics Responsible NLP: Privacy, Bias, Copyright and Documentation Fine-Tuning, PEFT, LoRA and Quantization RNN, GRU and LSTM Sequence Models Explained Topic Modelling and Text Clustering That You Can Inspect Cosine Similarity and Document Ranking Normalization, Stemming and Lemmatization Rule-Based Information Extraction and Relation Patterns Scalable Data Mining with MapReduce and Apache Spark Outlier and Novelty Detection with LOF and Isolation Forest DBSCAN and OPTICS Density-Based Clustering Bagging, Random Forests and Gradient Boosting Build a Reproducible Machine Learning Pipeline ETL, Dimensional Modelling and OLAP for Data Mining Distance and Similarity Measures for Data Mining Responsible Data Mining: Provenance, Privacy and Fair Use RAG Ingestion, Hybrid Retrieval, Reranking and Citations Production NLP APIs, Batching, Caching and Drift Monitoring Pretrained Transformer Pipelines and Task Selection Sentiment Analysis with Honest Error Analysis Sentence Embeddings and Semantic Similarity Word and Character N-Grams for Text Features Named Entity Recognition with Span-Level Evaluation Regex and Tokenization Without Losing Meaning Recommender Systems: Collaborative and Content-Based Methods Hierarchical Clustering, Linkage and Dendrograms Sequential Pattern Mining with GSP and PrefixSpan Compare Logistic Regression, Naive Bayes and SVM Scale, Discretize and Reduce Features with Evidence Choose Evaluation Metrics for Classification and Clustering Understand Data with Distributions, Sampling and Robust Statistics Frame a Data Mining Problem Before Choosing a Model Multilingual and Low-Resource NLP with Urdu and Code-Switching Structured LLM Outputs, Decoding and Tool Boundaries