下载 [Packt] Machine Learning Projects with Java [FCO] GloDLS torrent - GloDLS
洪流细节 "[Packt] Machine Learning Projects with Java [FCO] GloDLS"

[Packt] Machine Learning Projects with Java [FCO] GloDLS

To download this torrent, you need a BitTorrent client: Vuze or BTGuard
下载这洪流
Download using Magnet Link

健康:
种子: 10
懒鬼: 0
已完成: 18 
上次检查: 14-11-2021 07:04:32

上传者的声誉点 : 16612





Write a Review for the Uploader:   235   Say Thanks with one good review:
Share on Facebook


Details
name:[Packt] Machine Learning Projects with Java [FCO] GloDLS
说明:


By: Tomasz Lelek
Released: Friday, March 29, 2019 [New Release!]
Torrent Contains: 30 Files, 5 Folders
Course Source: https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-projects-java-video

Learn how to leverage well-proven ML algorithms to solve day-to-day ML problems

Video Details

ISBN 9781789612455
Course Length 2 hour 12 minutes

Table of Contents

• FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA
• ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY
• BUILDING REGRESSION MODEL FOR HOUSING MARKET
• DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME
• FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING

Video Description

Developers are worried about using various algorithms to solve different problems. This course is a perfect guide to identifying the best solution to efficiently build machine learning projects for different use cases to solve real-world problems.

In this course, you will learn how to build a model that takes complex feature vector form sensor data and classifies data points into classes with similar characteristics. Then you will predict the price of a house based on historical data. Finally, you will build a Deep Learning model that can guess personality traits using labeled data.

By the end of this course, you will have mastered each machine learning domain and will be able to build your own powerful projects at work.

Style and Approach

This is a step-by-step and fast-paced guide that will help you learn different ML techniques you can use to solve real-world problems, Every section will tackle a practical problem and take your ML skills to the next level

What You Will Learn

• Perform classification using the Weka Library.
• Implement Pattern Recognition of non-labeled data
• Build Regression models for data with multiple features
• Save trained models for further reusability
• Learn how to perform cross-validation
• Leverage Deep Learning in ML problems
• Implement Natural Language Processing with Deep Learning

Authors

Tomasz Lelek

Tomasz Lelek is a Software Engineer, programming mostly in Java, Scala. He has worked with ML algorithms for the past 5 years, with production experience in processing petabytes of data.
He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. Recently he was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and also at Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.

He is a co-founder of www.initlearn.com, an e-learning platform that was built with the Java language.

He has also written articles about everything related to the Java world: http://www.baeldung.com/.




YouTube 视频:
类别:Tutorials
语言:English  English
总大小:630.08 MB
哈希信息:3D5AC8BD3D049CE74E8488561DDF8C5271C45C83
增加:Prom3th3uS Super AdministratorMovie PirateVIP
加入的日期:2019-04-04 15:50:25
洪流地位:Torrent Verified


评级:Not Yet Rated (Log in to rate it)


Tracker:
https://tracker.fastdownload.xyz:443/announce

这个洪流也有备份的纤夫
URL播种机懒鬼已完成
https://tracker.fastdownload.xyz:443/announce000
udp://tracker.torrent.eu.org:451/announce200
udp://tracker.cyberia.is:6969/announce000
udp://tracker.leechers-paradise.org:6969/announce000
udp://open.stealth.si:80/announce209
udp://tracker.coppersurfer.tk:6969/announce000
udp://tracker.cyberia.is:6969/announce000
https://opentracker.xyz:443/announce000
https://t.quic.ws:443/announce000
udp://9.rarbg.to:2710/announce000
udp://tracker.opentrackr.org:1337/announce209
udp://ipv4.tracker.harry.lu:80/announce200
udp://tracker.coppersurfer.tk:6969/announce000
udp://tracker.internetwarriors.net:1337/announce200
udp://open.demonii.si:1337/announce000


文件列表: 





Comments
无可奉告,仍将过帐