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Working with big data
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Working with big data
Lesson 5: Experimentation and Running Algorithms in Production
5.3. Integrate a trained model into production
5.3. Integrate a trained model into production
Sửa lần cuối: Thứ tư, 18 Tháng tư 2018, 7:55 AM
◄ 5.2. Use cross validation to test a predictive model
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Introduction to Working with Big Data LiveLessons
What is Big Data?
Diễn đàn tin tức
1.1. Learning objectives
1.2. Set up a basic Hadoop installation
1.3. Write data into the Hadoop file system
1.3. Write a Hadoop streaming job to process text files
2.1. Learning objectives
2.2. Set up a basic Cassandra installation
2.3. Create a Cassandra schema for storing data
2.4. Store and retrieve data from Cassandra using the Ruby library
2.5. Write data into Cassandra from a Hadoop streaming job
2.6. Use the Hadoop reduce phase to parallelize writes
3.1. Learning objectives
3.2. Set up the Kafka messaging system
3.3. Publish and consume data from Kafka in Ruby
3.4. Aggregate log files into Hadoop using Kafka and a Ruby consumer
3.5. Create horizontally scalable message consumers
3.6. Sample messages using Kafka’s partitioning
3.7 Create redundant message consumers for high availability
4.1. Learning objectives
4.2. Grasp the concepts of machine learning and implement the k-nearest neighbors algorithm
4.3 Understand the basics of distance metrics and implement euclidean distance and cosine similarity
4.4. Transform raw data into a matrix and convert a text document into the vector space model
4.5. Use k-nearest neighbors to make predictions
4.6. Improve execution time by reducing the search space
5.1. Learning objectives
5.2. Use cross validation to test a predictive model
5.4. Version a model and track feedback data
5.5. Write a test harness to compare versioned models
5.6. Test new predicted models in production
6.1. Learning objectives
6.2. Prepare raw data for use in visualizations
6.3. Use core functions of the D3 JavaScript visualizaiton toolkit
6.4. Use D3 to create a barchart
6.5. Use D3 to create a time series
5.4. Version a model and track feedback data ►
Working with big data
Introduction
Lesson 1: Unstructured Storage and Hadoop
Lesson 2: Structured Storage and Cassandra
Lesson 3: Real Time Processing and Messaging
Lesson 4: Working with Machine Learning Algorithms
Lesson 5: Experimentation and Running Algorithms in Production
Lesson 6: Basic Visualizations
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