Techniques with mahout tutorial

This Machine Learning with Mahout tutorial will introduce you to Apache Mahout which is an evironment with scalable machine learning algorithms focused in collaborative filtering, clustering and classification. In this Machine Learning with Mahout tutorial you will learn what Apache Mahout is and why should we use it, Machine Learning, Techniques, Recommendation Systems, Pearsons Correlation, Distance Measures, Euclidean Distance & Cosine Similarities, Tanimoto Coefficient, Basics of Clustering, Clustering Algorithms, ClickStream Analytics, Fuzzy K Means, Collaborative Filtering Framework, Similarities Metrics, Myrrix and Oryx, Canopy Clustering, Topic Model, Apriori Algorithm and Use Cases. When you complete this Machine Learning with Mahout tutorial you will be able to use Apache Mahout environment for building your own scalable math algorithms.

Metas de aprendizaje
Programa del curso
13:57:13
Unidad 1: Contenido
Clase 1: Apache mahout tutorial-1 | apache mahout tutorial for beginners-1 | edureka
01:13:41
Clase 2: Apache mahout tutorial-2 | apache mahout tutorial for beginners-2 | edureka
02:07:51
Clase 3: Machine learning with mahout | apache mahout tutorial | edureka
00:34:35
Clase 4: Introduction to apache mahout | edureka
00:28:41
Clase 5: What is mahout ? | edureka
00:25:41
Clase 6: Mahout overview | edureka
00:08:55
Clase 7: Machine learning use cases | edureka
00:08:54
Clase 8: Classification of apache mahout | edureka
01:23:23
Clase 9: Mahout machine learning | edureka
00:06:54
Clase 10: Learning techniques in mahout | edureka
00:19:58
Clase 11: Supervised learning technique in mahout | edureka
00:11:07
Clase 12: Introduction to recommendation systems | apache mahout | edureka
00:45:57
Clase 13: Introduction to pearsons correlation | apache mahout | edureka
00:16:33
Clase 14: Understanding distance measures in apache mahout | edureka
00:07:09
Clase 15: Understanding euclidean distance & cosine similarities in mahout | edureka
00:04:10
Clase 16: Tanimoto coefficient | apache mahout | edureka
00:04:25
Clase 17: Understanding basics of clustering | clustering basics tutorial - 1 | edureka
00:19:16
Clase 18: Introduction to clustering in mahout | edureka
00:02:46
Clase 19: Clustering algorithms | apache mahout | edureka
00:13:14
Clase 20: Clickstream analytics in mahout | edureka
00:04:26
Clase 21: Introduction to fuzzy k means | apache mahout | edureka
00:04:35
Clase 22: Collaborative filtering framework | apache mahout | edureka
00:20:56
Clase 23: Similarities metrics in mahout | edureka
00:19:33
Clase 24: Introduction to myrrix and oryx | apache mahout | edureka
00:08:33
Clase 25: What is canopy clustering | canopy clustering in mahout | mahout clustering tutorial | edureka
00:36:08
Clase 26: What is topic model | understanding lda (latent dirichlet allocation) | edureka
00:26:58
Clase 27: Introduction to clustering techniques | mahout clustering techniques | mahout clustering tutorial
00:20:09
Clase 28: Understanding apriori algorithm | apriori algorithm using mahout | edureka
00:35:42
Clase 29: Mahout clustering | mahout clustering tutorial | apache mahout clustering | edureka
01:00:45
Clase 30: Mahout overview | mahout tutorial | mahout machine learning | edureka
00:30:41
Clase 31: Mahout use cases | mahout tutorial | apache mahout tutorial | edureka
00:25:37
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