Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.

Key Features

• Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j
• Solve real-world machine learning problems by delving complex numerical computing with Scala functional programming in a scalable and faster way
• Cover all key aspects such as collection, storing, processing, analyzing, and evaluation required to build and deploy machine models on computing clusters using Scala Play framework.

Book Description

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development.

If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet.

At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.

What you will learn

• Apply advanced regression techniques to boost the performance of predictive models
• Use different classification algorithms for business analytics
• Generate trading strategies for Bitcoin and stock trading using ensemble techniques
• Train Deep Neural Networks (DNN) using H2O and Spark ML
• Utilize NLP to build scalable machine learning models
• Learn how to apply reinforcement learning algorithms such as Q-learning for developing ML application
• Learn how to use autoencoders to develop a fraud detection application
• Implement LSTM and CNN models using DeepLearning4j and MXNet

Who This Book Is For

If you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful.

Table of Contents

1. Analyzing Insurance Severity Claim
2. Analyzing Outgoing Customers through Churn Prediction
3. High Frequency Bitcoin Price Prediction from Historical and Live Data
4. Population Scale Clustering and Ethnicity Analysis
5. Topic Modelling in NLP: A Better Insight to Large-Scale Texts
6. Model-based Movie Recommendation Engine
7. Deep Reinforcement Learning using Markov Decision Process (MDP)
8. Using Deep Belief Networks in Bank Marketing
9. Fraud Analytics using Autoencoders and Anomaly Detection
10. Human Activity Recognition using RNN
11. Image Classification using CNN

強大的智能應用程式使用深度學習演算法來主導數值計算、深度學習和函數式編程。

主要特點:
- 使用知名的開源Scala庫（如Spark ML、H2O、MXNet、Zeppelin和DeepLearning4j）探索機器學習技術。
- 以可擴展且更快的方式，通過Scala函數式編程來解決實際的數值計算問題。
- 涵蓋構建和部署機器模型所需的所有關鍵方面，包括收集、存儲、處理、分析和評估，並使用Scala Play框架在計算集群上進行。

書籍描述:
機器學習通過將數據轉化為可操作的信息，對學術界和工業界產生了巨大的影響。Scala在過去幾年中在數據科學和分析領域尤其受到採用的增長。本書適用於具有複雜數值計算背景並希望進一步了解實際機器學習應用開發的數據科學家、數據工程師和深度學習愛好者。

如果您對機器學習概念很熟悉，並且希望通過利用Scala的威力深入探索這些概念的實際實施，那麼這本書就是您所需要的！通過11個端到端的項目，您將熟悉流行的機器學習庫，如Spark ML、H2O、DeepLearning4j和MXNet。

最終，您將能夠使用數值計算和函數式編程執行複雜的數值任務，以在生產就緒的環境中開發、構建和部署研究或商業項目。

您將學到什麼:
- 應用高級回歸技術以提高預測模型的性能。
- 使用不同的分類算法進行業務分析。
- 使用集成技術生成比特幣和股票交易的交易策略。
- 使用H2O和Spark ML訓練深度神經網絡（DNN）。
- 利用自然語言處理（NLP）構建可擴展的機器學習模型。
- 學習如何應用強化學習算法（如Q學習）開發機器學習應用。
- 學習如何使用自編碼器開發詐騙檢測應用。
- 使用DeepLearning4j和MXNet實現LSTM和CNN模型。

本書適合對Scala和Spark兩者的強大功能來處理大數據感興趣的讀者。如果您對機器學習概念很熟悉，並且希望通過利用Scala的威力深入探索這些概念的實際實施，那麼這本書就是您所需要的！建議具有扎實的Scala編程語言基礎。對機器學習技術有基本的了解將更有幫助。

目錄:
1. 分析保險索賠嚴重性
2. 通過流失預測分析流失客戶
3. 從歷史和實時數據中預測高頻比特幣價格
4. 人口規模的聚類和種族分析
5. NLP中的主題建模：對大規模文本的更好洞察
6. 基於模型的電影推薦引擎
7. 使用馬爾可夫決策過程（MDP）進行深度強化學習
8. 在銀行營銷中使用深度信念網絡
9. 使用自編碼器和異常檢測進行欺詐分析
10. 使用RNN進行人體活動識別
11. 使用CNN進行圖像分類