Lesson 1TF-IDF, Hashin, an Document Embeddins: When fi Use Each an Parameter ChoicesDis section compare TF-IDF, hashin, an document embeddins fi text representation. Yuh guh learn strengths, weaknesses, an tunin strategies, an how fi choose methods an parameters fi search, clusterin, an classification tasks, yuh zeet.
TF-IDF weighting schemes and normalizationHashing trick, collisions, and feature space sizeChoosing n-grams and vocabulary pruning rulesWhen sparse vectors beat dense embeddingsEmbedding dimensionality and pooling choicesEvaluating representations for downstream tasksLesson 2N-gram Extraction an Selection: Unigrams, Bigrams, Trigrams; Frequency an PMI FilterinDis section detail n-gram extraction an selection. Yuh guh generate unigrams, bigrams, an trigrams, apply frequency an PMI filters, an build robust vocabularies fi models an exploratory analysis, true.
Generating n-grams with sliding windowsMinimum frequency thresholds and cutoffsPMI and other association measures for n-gramsHandling multiword expressions and phrasesDomain-specific stoplists and collocation filtersEvaluating n-gram sets on downstream tasksLesson 3Keyphrase Extraction: RAKE, YAKE, TextRank an Scorin/Threshold SelectionDis section cover keyphrase extraction wid RAKE, YAKE, an TextRank. Yuh guh learn preprocessin, scorin, threshold selection, an evaluation, an how fi adapt methods fi domains like support tickets or reviews, mi bredda.
Text preprocessing and candidate phrase generationRAKE scoring, stoplists, and phrase length limitsYAKE features, window sizes, and language settingsTextRank graph construction and edge weightingScore normalization and threshold calibrationEvaluating keyphrases with gold labels or expertsLesson 4Dimensionality Reduction fi Topics: LSA (SVD), UMAP, t-SNE fi VisualizationDis section cover dimensionality reduction fi topic exploration. Yuh guh apply LSA wid SVD, UMAP, an t-SNE fi project document or topic vectors, tune parameters, an design clear, trustworthy visualizations, yuh hear.
LSA with truncated SVD for topic structureChoosing k and interpreting singular vectorsUMAP parameters for global versus local structuret-SNE perplexity, learning rate, and iterationsVisual encoding choices for topic scatterplotsPitfalls and validation of visual clustersLesson 5Word an Sentence Embeddins: Word2Vec, GloVe, FastText, Transformer Embeddins (BERT Variants)Dis section explore word an sentence embeddins, from Word2Vec, GloVe, an FastText to transformer-based models. Yuh guh learn trainin, fine-tunin, poolin, an how fi select embeddins fi different analytic tasks, seen.
Word2Vec architectures and training settingsGloVe co-occurrence matrices and hyperparametersFastText subword modeling and rare wordsSentence pooling strategies for static embeddingsTransformer embeddings and BERT variantsTask-specific fine-tuning versus frozen encodersLesson 6Neural Topic Approaches an BERTopic: Clusterin Embeddins, Topic Mergin an InterpretabilityDis section present neural topic approaches, focusin pon BERTopic. Yuh guh cluster embeddins, reduce dimensionality, refine topics, merge or split clusters, an improve interpretability wid representative terms an labels, yuh know.
Embedding selection and preprocessing for topicsUMAP and HDBSCAN configuration in BERTopicTopic representation and c-TF-IDF weightingMerging, splitting, and pruning noisy topicsImproving topic labels with domain knowledgeEvaluating neural topics against LDA baselinesLesson 7Frequent Pattern Minin an Association Rules fi Co-occurrin Complaint TermsDis section introduce frequent pattern minin an association rules fi text. Yuh guh transform documents into transactions, mine co-occurrin complaint terms, tune support an confidence, an interpret rules fi insights, mi fren.
Building term transactions from documentsChoosing support and confidence thresholdsApriori and FP-Growth algorithm basicsInterpreting association rules and liftFiltering spurious or redundant patternsUsing patterns to refine taxonomies and alertsLesson 8Unsupervised Topic Modelin: LDA Configuration, Coherence Measures, Number of Topics TuninDis section introduce unsupervised topic modelin wid LDA. Yuh guh configure priors, passes, an optimization, use coherence an perplexity, an design experiments fi select topic numbers dat balance interpretability an stability, true.
Bag-of-words preparation and stopword controlDirichlet priors: alpha, eta, and sparsityPasses, iterations, and convergence diagnosticsTopic coherence metrics and their variantsTuning number of topics with grid searchesStability checks and qualitative topic reviewLesson 9Basic Lexical Features: Token Counts, Character Counts, Unique Token Ratio, Readability ScoresDis section focus pon basic lexical features fi text analytics. Yuh guh compute token an character counts, type–token ratios, an readability scores, an learn when dese simple features outperform more complex representations, yuh zeet.
Tokenization choices and token count featuresCharacter-level counts and length distributionsType–token ratio and vocabulary richnessStopword ratios and punctuation-based signalsReadability indices and formula selectionCombining lexical features with other signalsLesson 10Annotation Schema Design fi Manual Labels: Issue Types, Sentiment, Urgency, Topic TagsDis section explain how fi design annotation schemas fi manual labels. Yuh guh define issue types, sentiment, urgency, an topic tags, write clear guidelines, handle ambiguity, an measure agreement fi refine di schema iteratively, seen.
Defining label taxonomies and granularityOperationalizing sentiment and emotion labelsModeling urgency, impact, and priority levelsDesigning multi-label topic tag structuresWriting annotation guidelines with examplesInter-annotator agreement and schema revision
