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  • Book Overview & Buying Scala Machine Learning Projects
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Scala Machine Learning Projects

Scala Machine Learning Projects

By : Karim
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Scala Machine Learning Projects

Scala Machine Learning Projects

By: Karim
Buy this Book

Overview of this book

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.
Table of Contents (13 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
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1
Analyzing Insurance Severity Claims
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Analyzing Insurance Severity Claims
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Machine learning and learning workflow
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Hyperparameter tuning and cross-validation
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Analyzing and predicting insurance severity claims
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LR for predicting insurance severity claims
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GBT regressor for predicting insurance severity claims
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Boosting the performance using random forest regressor
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Comparative analysis and model deployment
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Summary
2
Analyzing and Predicting Telecommunication Churn
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Analyzing and Predicting Telecommunication Churn
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Why do we perform churn analysis, and how do we do it?
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Developing a churn analytics pipeline
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LR for churn prediction
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SVM for churn prediction
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DTs for churn prediction
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Random Forest for churn prediction
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Selecting the best model for deployment
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Summary
3
High Frequency Bitcoin Price Prediction from Historical and Live Data
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High Frequency Bitcoin Price Prediction from Historical and Live Data
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Bitcoin, cryptocurrency, and online trading
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High-level data pipeline of the prototype
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Historical and live-price data collection
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Model training for prediction
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Scala Play web service
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Predicting prices and evaluating the model
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Demo prediction using Scala Play framework
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Summary
4
Population-Scale Clustering and Ethnicity Prediction
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Population-Scale Clustering and Ethnicity Prediction
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Population scale clustering and geographic ethnicity
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1000 Genomes Projects dataset description
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Algorithms, tools, and techniques
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Configuring programming environment
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Data pre-processing and feature engineering
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Summary
5
Topic Modeling - A Better Insight into Large-Scale Texts
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Topic Modeling - A Better Insight into Large-Scale Texts
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Topic modeling and text clustering
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Topic modeling with Spark MLlib and Stanford NLP
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Other topic models versus the scalability of LDA
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Deploying the trained LDA model
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Summary
6
Developing Model-based Movie Recommendation Engines
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Developing Model-based Movie Recommendation Engines
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Recommendation system
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Spark-based movie recommendation systems
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Selecting and deploying the best model
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Summary
7
Options Trading Using Q-learning and Scala Play Framework
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Options Trading Using Q-learning and Scala Play Framework
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Reinforcement versus supervised and unsupervised learning
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A simple Q-learning implementation
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Developing an options trading web app using Q-learning
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Summary
8
Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks
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Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks
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Client subscription assessment through telemarketing
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Summary
9
Fraud Analytics Using Autoencoders and Anomaly Detection
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Fraud Analytics Using Autoencoders and Anomaly Detection
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Outlier and anomaly detection
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Autoencoders and unsupervised learning
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Developing a fraud analytics model
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Hyperparameter tuning and feature selection
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Summary
10
Human Activity Recognition using Recurrent Neural Networks
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Human Activity Recognition using Recurrent Neural Networks
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Working with RNNs
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Human activity recognition using the LSTM model
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Implementing an LSTM model for HAR
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Tuning LSTM hyperparameters and GRU
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Summary
11
Image Classification using Convolutional Neural Networks
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Image Classification using Convolutional Neural Networks
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Image classification and drawbacks of DNNs
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CNN architecture
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Large-scale image classification using CNN
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Tuning and optimizing CNN hyperparameters
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Summary
12
Other Books You May Enjoy
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Other Books You May Enjoy
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Leave a review - let other readers know what you think

Working with RNNs

In this section, we will first provide some contextual information about RNNs. Then, we will highlight some potential drawbacks of classical RNNs. Finally, we will see an improved variation of RNNs called LSTM to address the drawbacks.

Contextual information and the architecture of RNNs

Human beings don't start thinking from scratch; the human mind has so-called persistence of memory, the ability to associate the past with recent information. Traditional neural networks, instead, ignore past events. For example, in a movie scenes classifier, it's not possible for a neural network to use a past scene to classify current ones. RNNs were developed to try to solve this problem:

Figure 1: RNNs have loops

In contrast to conventional neural networks, RNNs are networks with a loop that allows the information to be persistent (Figure 1). In a neural network say, A: at some time t, input xt and outputs a value ht. So from Figure 1, we can think of an RNN as multiple copies of the same...

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Tech Concepts
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Programming languages
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