Machine Learning with Mahout

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Overview 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

Pattern Recognition I

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Lecture Details Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit httpnptel.ac.in

Pattern Recognition

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Lecture Details Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IISc Bangalore. For more details on NPTEL visit httpnptel.ac.in

Convolutional Neural Networks for Visual Recognition

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Overview Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection.

Natural Language Processing with Deep Learning

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Overview Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation.

Machine Learning

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Overview This is an introductory course by Caltech Professor Yaser Abu-Mostafa on machine learning that covers the basic theory, algorithms, and applications. Machine learning (ML) enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML techniques are widely applied in engineering, science, finance, and commerce to build systems for which we do not have full mathematical specification (and that covers a lot of systems). The course balances theory and practice, and covers the mathematical as well as the heuristic aspects.