Topic Modeling Workshop for the Beginners in Python

[Beginners Workshop in Python] This workshop is all you need to learn topic modeling in python combining Gensim, spacy, NLTK and few other python libraries. This workshop has the following 6 steps: 1. Reading and Loading Data - NIPS Papers as our source dataset 2. Data Preparation - Select required columns - Remove Punctuations marks 3. Exploratory Data Analysis - Word Cloud - Document Term Matrix 4. Data Modeling and tokenization - Stop words removal - bigram and trigram - Vectorization and Tokenization 5. Model Building - LDA Modelling 6. Model Evaluations - LDA Model Visualization - Coherence Score GitHub notebook for this workshop: https://github.com/prodramp/DeepWorks/tree/main/TopicModelling/BaseWorkshop ▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬ - (00:00) Tutorial Starts - (00:12) Topic Modeling Intro - (03:10) Workshop Environment - (03:26) Content location at GitHub - (03:50) Dataset used in this workshop - (04:40) LDA Intro - (05:35) Topic Modeling Use Cases - (05:55) 6 Steps in this Workshop - (06:06) Step 1: Loading Data - (09:20) Step 2: Data Preparation - (09:41) Step 2.1: Removing Punctuation - (10:22) Step 2.2: Removing digits and word with digits - (10:43) Step 2.3: Lowercase all context - (11:08) Step 3: EDA - (11:27) Step 3.1: Word Cloud - (12:28) Step 3.2: Document Term Matrix - (14:50) Step 4: Data Modeling - (15:21) Step 4.1: Stop words removal - (21:09) Step 4.2: Creating Bigram and Trigram - (23:26) Step 4.3: Lemmatization - (26:03) Step 4.4: Tokenization - (28:14) Step 5: LDA Topic Modeling - (32:53) Step 6: Topic Modeling Performance and analysis - (34:29) Step 6.1: Topic visualization - (37:32) Step 6.2: Coherence Score - (39:30) Saving notebook to GitHub - (39:55) Recap Please visit: ------------------ - Prodramp LLC | https://prodramp.com | @prodramp - https://www.linkedin.com/company/prodramp Content Creator: Avkash Chauhan (@avkashchauhan) - https://www.linkedin.com/in/avkashchauhan Tags: #python #lda #nlp #spacy #gensim #nlp #ml #ai #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #modelperformance #modelfit #modeleffect #modelimpact #modelbias #modeldeployment #modelregistery #modelpipeline #neptuneai #streamlit #pythonapps #deepchecks #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #supervisor #supervisord #kaggle #keplergl #mapbox #lightgbm #xgboost #classification #regression #dataengineering #pandas #keras #tensorflow #tensorboard #mnist #cnn #convnet #alexnet #prodramp #avkashchauhan #cnnexplainer #gnn #graph #graphneuralnetwork #pyg #networkx