TOPIC MODELING | LATENT DIRICHLET ALLOCATION ( LDA ) | IN DEPTH | BY YASHVI PATEL

Topic Models are a type of statistical language models used for finding hidden structures in a collection of texts. For example, when we think of 'entertainment' - the topic, the words that come to the mind are 'movie', 'dance', 'youtube' and so on.
In this video, I am explaining the unsupervised machine learning technique, Latent Dirichlet Allocation (LDA), for automatically finding the mixture of similar words together, thus forming the topic.
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⭐️ ABOUT ME ⭐️
I am Yashvi Patel, Software Developer with Data science skills and Kaggle Notebook Master. I created this channel to share my knowledge and experience with you all. This channel will include practical tutorials solving problems from Kaggle datasets and competitions. I will upload videos related to Data Science, Machine learning, Deep learning, Natural Language Processing, and Computer vision.
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[@user-bs6ll3in3x]

Thank you @Yashvi Patel, this is honestly one of the simplest explanation of LDA that's available on RUclips

[@KA00_7]

Very well explained , thank you

[@ramendrachaudhary9784]

Good explanation. Well done

[@omkarkamble2024]

great explanation

[@pravinmhaske]

Why reduce the count by 1? Any intuitive explanation? And what is the outcome of this algorithm? How to interpret it?

[@arenashawn772]

Hi, I was wondering how the topics are initially defined and given a document of thousands of words, how do we decide how many topics there is or how large (word counts) a topic is? Thanks!

[@abduladhilpk8223]

Very well explained

[@YashviPatel]

Thank you

[@srishtichaurasia7402]

The calculations appear to be wrong

[@clusterwound4818]

☹️

[@user-bs6ll3in3x]

itni problem hai toh khud kar le ya pakist@n chala ja