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Working in a hands-on learning environment, led by our Mahout expert instructor, students will learn about and explore: introduction to machine learning with Apache Mahout.Real-world examples the course presents practical use cases and then illustrates how Mahout can be applied to solve them.

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February 5, 2021

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This course covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework.

Course Curriculum

      • Defining recommendation 00:00:00
      • Running a first recommender engine 00:00:00
      • Evaluating a recommender 00:00:00
      • Evaluating precision and recall 00:00:00
      • Evaluating the GroupLens data set 00:00:00
      • Representing preference data 00:00:00
      • In-memory DataModels 00:00:00
      • Coping without preference values 00:00:00
      • Understanding user-based recommendation 00:00:00
      • Exploring the user-based recommender 00:00:00
      • Exploring similarity metrics 00:00:00
      • Item-based recommendation 00:00:00
      • Slope-one recommender 00:00:00
      • New and experimental recommenders 00:00:00
      • Comparison to other recommenders 00:00:00
      • Comparison to model-based recommenders 00:00:00
      • Analyzing example data from a dating site 00:00:00
      • Finding an effective recommender 00:00:00
      • Injecting domain-specific information 00:00:00
      • Recommending to anonymous users 00:00:00
      • Creating a web-enabled 00:00:00
      • Updating and monitoring the recommender 00:00:00
      • Analyzing the Wikipedia data set 00:00:00
      • Designing a distributed item-based algorithm 00:00:00
      • Implementing a distributed algorithm with MapReduce 00:00:00
      • Running MapReduces with Hadoop 00:00:00
      • Pseudo-distributing a recommender 00:00:00
      • Looking beyond first steps with recommendations 00:00:00
      • Clustering basics 00:00:00
      • Measuring the similarity of items 00:00:00
      • Hello World: running a simple clustering example 00:00:00
      • Exploring distance measures 00:00:00
      • Exploring distance measures 00:00:00
      • Hello World again! Trying out various distance measures 00:00:00
      • Visualizing vectors 00:00:00
      • Representing text documents as vectors 00:00:00
      • Generating vectors from documents 00:00:00
      • Improving quality of vectors using normalization 00:00:00
      • K-means clustering 00:00:00
      • Beyond k-means: an overview of clustering techniques 00:00:00
      • Fuzzy k-means clustering 00:00:00
      • Model-based clustering 00:00:00
      • Topic modeling using latent Dirichlet allocation (LDA) 00:00:00
      • Inspecting clustering output 00:00:00
      • Analyzing clustering output 00:00:00
      • Improving clustering quality 00:00:00
      • Quick-start tutorial for running clustering on Hadoop 00:00:00
      • Tuning clustering performance 00:00:00
      • Batch and online clustering 00:00:00
      • Finding similar users on Twitter 00:00:00
      • Suggesting tags for artists on Last.fm 00:00:00
      • Analyzing the Stack Overflow data set 00:00:00
      • Why use Mahout for classification? 00:00:00
      • The fundamentals of classification systems 00:00:00
      • How classification works 00:00:00
      • Work flow in a typical classification project 00:00:00
      • Step-by-step simple classification example 00:00:00
      • Extracting features to build a Mahout classifier 00:00:00
      • Preprocessing raw data into classifiable data 00:00:00
      • Converting classifiable data into vectors 00:00:00
      • Classifying the 20 newsgroups data set with SGD 00:00:00
      • Choosing an algorithm to train the classifier 00:00:00
      • Classifying the 20 newsgroups data with naive Bayes 00:00:00
      • Classifier evaluation in Mahout 00:00:00
      • When classifiers go bad 00:00:00
      • Tuning for better performance 00:00:00
      • Process for deployment in huge systems 00:00:00
      • Determining scale and speed requirements 00:00:00
      • Building a training pipeline for large systems 00:00:00
      • Integrating a Mahout classifier 00:00:00
      • Example: a Thrift-based classification server 00:00:00
      • Why Shop It To Me chose Mahout 00:00:00
      • General structure of the email marketing system 00:00:00
      • Training the model 00:00:00
      • Speeding up classification 00:00:00

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