Machine Learning with Mahout
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Updated On 02 Feb, 19
Apache Mahout Tutorial - Machine Learning with Mahout - Introduction to Apache Mahout - What is Mahout ? - Mahout Overview - Machine Learning Use cases - Classification of Apache Mahout - Mahout Machine Learning - Learning Techniques in Mahout - Supervised Learning Technique In Mahout - Introduction to Recommendation Systems - Introduction to Pearsons Correlation - Understanding Distance Measures in Apache Mahout - Understanding Euclidean Distance & Cosine Similarities in Mahout - Tanimoto Coefficient - Understanding Basics of Clustering - Introduction to Clustering in Mahout - Clustering Algorithms - ClickStream Analytics in Mahout - Introduction to Fuzzy K Means - Collaborative Filtering Framework - Similarities Metrics in Mahout - Introduction to Myrrix and Oryx - What is Canopy Clustering | Canopy Clustering in Mahout - What is Topic Model | Understanding LDA (Latent Dirichlet Allocation) - Introduction to Clustering Techniques - Understanding Apriori Algorithm - Apriori Algorithm Using Mahout - Mahout Clustering - Mahout Clustering Tutorial - Apache Mahout Clustering - Mahout Overview - Mahout Machine Learning - Mahout Use Cases - Apache Mahout Tutorial
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In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be
explained by unobserved groups that explain why some parts of the data are similar.
Know More about canopy clustering through this video.
Following are the topics covered in the video
1.What is Topic Model?
2. How Topic Model Works ?
3.Understanding Latent Dirichlet allocation.
4.K-means with LDA.
5.Tunning the parameters of LDA
Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning
environment, accessible from any part of the world.
The topics related to ‘Topic model and LDA’ have extensively been covered in our course ‘Machine Learning with Mahout’.
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Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
March 29, 2019
Great course. Thank you very much.