Machine Learning with Mahout
Other,
Updated On 02 Feb, 19
Other,
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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Watch Sample Class Recording
httpwww.edureka.comahout?utm_source=youtube&utm_medium=referral&utm_campaign=euclidean-distance-cosine-similarities
The video explains the following concepts from Mahout
1. Euclidean Similarities
2. Cosine Measure Similarity
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The topics related to ‘Euclidean Distance & Cosine Similarities’ have extensively been covered in our course ‘Apache Mahout’.
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Sam
Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
Dembe
March 29, 2019
Great course. Thank you very much.