Mahout Training Courses

Mahout Training

Mahout - scalable machine learning and data mining courses

Mahout Course Outlines

ID Name Duration Overview
287841 Apache Mahout for Developers 14 hours Audience Developers involved in projects that use machine learning with Apache Mahout. Format Hands on introduction to machine learning. The course is delivered in a lab format based on real world practical use cases. Implementing Recommendation Systems with Mahout Introduction to recommender systems Representing recommender data Making recommendation Optimizing recommendation Clustering Basics of clustering Data representation Clustering algorithms Clustering quality improvements Optimizing clustering implementation Application of clustering in real world Classification Basics of classification Classifier training Classifier quality improvements
4141233 Big Data Analytics 21 hours Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. Day 1: Big Data Analytics (8.5 hours) Quick Overview Data Sources Mining Data Recommender systems Datatypess Structured vs unstructured Static vs streamed Data-driven vs user-driven analytics data validity Models and Classification Statistical Models Classification Clustering: kGroups, k-means, nearest neighbours Ant colonies, birds flocking Predictive Models Decision trees Support vector machine Naive Bayes classification Markov Model Regression Ensemble methods Building Models Data Preparation (MapReduce) Data cleansing Developing and testing a model Model evaluation, deployment and integration Overview of Open Source and commercial software Selection of R-project package Python libraries Hadoop and Mahout Selected Apache projects related to Big Data and Analytics Selected commercial solution Integration with existing software and data sources     Day 2: Mahout and Spark (8.5 hours) Implementing Recommendation Systems with Mahout Introduction to recommender systems Representing recommender data Making recommendation Optimizing recommendation Spark basics Spark and Hadoop Spark concepts and architecture Spark eco system (core, spark sql, mlib, streaming) Labs : Installing and running Spark Running Spark in local mode Spark web UI Spark shell Inspecting RDDs Labs: Spark shell exploration Spark API programming Introduction to Spark API / RDD API Submitting the first program to Spark Debugging / logging Configuration properties Spark and Hadoop Hadoop Intro (HDFS / YARN) Hadoop + Spark architecture Running Spark on Hadoop YARN Processing HDFS files using Spark Spark Operations Deploying Spark in production Sample deployment templates Configurations Monitoring Troubleshooting     Day 3 : Google Cloud Platform Big Data & Machine Learning Fundamentals (4 hours) Data Analytics on the Cloud What is the Google Cloud Platform? GCP Big Data Products CloudSQL: your SQL database on the cloud A no-ops database Lab: importing data into CloudSQL and running queries on rentals data Dataproc Managed Hadoop + Pig + Spark on the cloud Lab: Machine Learning with SparkML Scaling data analysis Fast random access Datastore: Key-Entity BigTable: wide-column Datalab Why Datalab? (interactive, iterative) Demo: Sample notebook in datalab BigQuery Interactive queries on petabytes Lab: Build machine learning dataset Machine Learning with TensorFlow TensorFlow Lab: Train and use neural network Fully built models for common needs Vision API Translate API Lab: Translate Genomics API (optional) What is linkage disequilibrium? Finding LD using Dataflow and BigQuery Data processing architectures Asynchronous processing with TaskQueues Message-oriented architectures with Pub/Sub Creating pipelines with Dataflow Summary Where to go from here Resources
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