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Scala: Applied Machine Learning
by Pascal Bugnion and Patrick R. Nicolas
Publisher finelybook 出版社: Packt Publishing
book: February 2017
Pages: 1265
ISBN-13: 9781787126640
ISBN-10: 178712455X
Book Description
This Learning Path aims to put the entire world of machine learning with Scala in front of you.
Scala for Data Science,the first module in this course,is a tutorial guide that provides tutorials on some of the most common Scala libraries for data science,allowing you to quickly get up to speed building data science and data engineering solutions.
The second course,Scala for Machine Learning guides you through the process of building AI applications with diagrams,formal mathematical notation,source code snippets,and useful tips. A review of the Akka framework and Apache Spark clusters concludes the tutorial.
The next module,Mastering Scala Machine Learning,is the final step in this course. It will take your knowledge to next level and help you use the knowledge to build advanced applications such as social media mining,intelligent news portals,and more. After a quick refresher on functional programming concepts using REPL,you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees.
By the end of this course,you will be a master at Scala machine learning and have enough expertise to be able to build complex machine learning projects using Scala.
This Learning Path combines some of the best that Packt has to offer in one complete,curated package. It includes content from the following Packt products:
Scala for Data Science,Pascal Bugnion
Scala for Machine Learning,Patrick Nicolas
Mastering Scala Machine Learning,Alex Kozlov
Contents
1: SCALA AND DATA SCIENCE
2: MANIPULATING DATA WITH BREEZE
3: PLOTTING WITH BREEZE-VIZ
4: PARALLEL COLLECTIONS AND FUTURES
5: SCALA AND SQL THROUGH JDBC
6: SLICK – A FUNCTIONAL INTERFACE FOR SQL
7: WEB APIS
8: SCALA AND MONGODB
9: CONCURRENCY WITH AKKA
10: DISTRIBUTED BATCH PROCESSING WITH SPARK
11: SPARK SQL AND DATAFRAMES
12: DISTRIBUTED MACHINE LEARNING WITH MLLIB
13: WEB APIS WITH PLAY
14: VISUALIZATION WITH D3 AND THE PLAY FRAMEWORK
15: GETTING STARTED
16: HELLO WORLD!
17: DATA PREPROCESSING
18: UNSUPERVISED LEARNING
19: NAÏVE BAYES CLASSIFIERS
20: REGRESSION AND REGULARIZATION
21: SEQUENTIAL DATA MODELS
22: KERNEL MODELS AND SUPPORT VECTOR MACHINES
23: ARTIFICIAL NEURAL NETWORKS
24: GENETIC ALGORITHMS
25: REINFORCEMENT LEARNING
26: SCALABLE FRAMEWORKS
27: EXPLORATORY DATA ANALYSIS
28: DATA PIPELINES AND MODELING
29: WORKING WITH SPARK AND MLLIB
30: SUPERVISED AND UNSUPERVISED LEARNING
31: REGRESSION AND CLASSIFICATION
32: WORKING WITH UNSTRUCTURED DATA
33: WORKING WITH GRAPH ALGORITHMS
34: INTEGRATING SCALA WITH R AND PYTHON
35: NLP IN SCALA
36: ADVANCED MODEL MONITORING
图书说明
这个学习路径旨在将Scala的整个机器学习世界放在你面前。
数据科学Scala是本课程的第一个模块，是一个教程指南，提供了一些用于数据科学的最常见的Scala库的教程，可以帮助您快速建立数据科学和数据工程解决方案。
第二个课程，Scala机器学习指导您完成使用图表，正式数学符号，源代码片段和实用提示构建AI应用程序的过程。对Akka框架和Apache Spark集群的回顾结束于本教程。
下一个模块，掌握Scala机器学习，是本课程的最后一步。这将使您的知识进入下一个阶段，并帮助您利用知识构建高级应用程序，如社交媒体挖掘，智能新闻门户等。在使用REPL进行功能编程概念的快速回顾之后，您将看到一些设置开发环境和修改数据的实例。然后，我们将使用k-means和决策树探索与Spark和MLlib的工作。
在本课程结束之前，您将成为Scala机器学习的高手，并拥有足够的专业知识，能够使用Scala构建复杂的机器学习项目。
这个学习路径结合了Packt在一个完整的，策划的包中提供的一些最好的。它包含以下Packt产品的内容:
数据科学Scala，Pascal Bugnion
斯卡拉机器学习，帕特里克尼古拉斯
掌握Scala机器学习，Alex Kozlov
目录
1: SCALA AND DATA SCIENCE
2: 使用BREEZE操作数据
3: 用BREEZE-VIZ绘制
4: 并行收藏与期货
5: 通过JDBC进行SCALA和SQL
6: SLICK – SQL的功能界面
7: WEB APIS
8: SCALA和MONGODB
9: 与AKKA约定
10: 用SPARK进行分配处理
11: SPARK SQL和DATAFRAMES
12: 分布式机器与MLLIB学习
13: 具有播放的WEB APIS
14: 可视化与D3和播放框架
15: 入门
16: 嗨，世界！
17: 数据预处理
18: 不安全的学习
19: NAVEVE BAYES CLASSIFIERS
20: 回归和正常化
21: 序列数据模型
22: KERNEL模型和支持向量机
23: 人造神经网络
24: 遗传算法
25: 加固学习
26: 可扩展的框架
27: 爆炸数据分析
28: 数据管道和建模
29: 使用火花和MLLIB
30: 监督和不经过学习
31: 回归和分类
32: 使用非结构化数据
33: 使用图表算法
34与R和PYTHON整合SCALA
35: NLP在SCALA
36: 高级模型监控
