Executive Development Programme in Applied Topic Modeling
-- ViewingNowThe Executive Development Programme in Applied Topic Modeling is a certificate course designed to empower professionals with the latest techniques in data analysis. This programme is crucial in today's data-driven world, where businesses are seeking experts who can derive meaningful insights from complex data sets.
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⢠Introduction to Topic Modeling: Basics of topic modeling, its applications, and benefits. Understanding the importance of topic modeling in text analysis and natural language processing. ⢠Data Preprocessing for Topic Modeling: Data cleaning, wrangling, and exploration. Tokenization, stopwords, and stemming/lemmatization techniques. ⢠Latent Dirichlet Allocation (LDA): Introduction to LDA, its mathematical foundations, and implementation. Understanding LDA's assumptions, advantages, and limitations. ⢠Non-Negative Matrix Factorization (NMF): NMF principles and its application in topic modeling. Comparing NMF with LDA and understanding their differences. ⢠Hierarchical Dirichlet Process (HDP): HDP's background, intuition, and practical implementation. Exploring its advantages over LDA and NMF in certain scenarios. ⢠Topic Coherence Evaluation: Evaluation metrics for topic modeling, including coherence scores and human judgments. Ensuring high-quality topics and interpreting the results. ⢠Visualizing Topic Models: Data visualization techniques to represent topic models effectively. Visualizing topics, topic distributions, and document-topic relationships. ⢠Topic Modeling in Python (using Gensim and other libraries): Hands-on experience with Python libraries to implement topic modeling. Creating, evaluating, and visualizing topic models. ⢠Topic Modeling in R (using topicmodels and other packages): Applying topic modeling in R, understanding package functionalities, and comparing results with Python implementations. ⢠Real-world Applications of Topic Modeling: Case studies, industry examples, and best practices for applying topic modeling in various fields.
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