Chuyển tới nội dung chính
Side panel
Call us : 024-2662350
E-mail :
it@huph.edu.vn
Bạn đang truy cập với tư cách khách vãng lai (
Đăng nhập
)
Trang chủ HUPH
Microsoft Teams
Các khóa học
Tài nguyên
Google Classroom
Videos học tập
Hướng dẫn
Hướng dẫn sử dụng LMS (dành cho giảng viên)
Hướng dẫn sử dụng LMS (dành cho học viên)
Hướng dẫn sử dụng Microsoft Team cho giảng viên
Hướng dẫn sử dụng Microsoft Team cho sinh viên
Hướng dẫn soạn thảo bài giảng Elearning
Working with big data
Trang chủ
Khoá học
Data Science Courses
Working with big data
Lesson 4: Working with Machine Learning Algorithms
4.6. Improve execution time by reducing the search space
4.6. Improve execution time by reducing the search space
Sửa lần cuối: Thứ tư, 18 Tháng tư 2018, 7:52 AM
◄ 4.5. Use k-nearest neighbors to make predictions
Chuyển tới...
Chuyển tới...
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
5.1. Learning objectives
5.2. Use cross validation to test a predictive model
5.3. Integrate a trained model into production
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
5.1. Learning objectives ►
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
Trang chủ
Lịch