9781788479042
Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala
by: Md. Rezaul Karim
ISBN-10: 1788479041
ISBN-13: 9781788479042
Released: 2018-01-31
Pages: 470
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.
Contents
1: ANALYZING INSURANCE SEVERITY CLAIMS
2: ANALYZING AND PREDICTING TELECOMMUNICATION CHURN
3: HIGH FREQUENCY BITCOIN PRICE PREDICTION FROM HISTORICAL AND LIVE DATA
4: POPULATION-SCALE CLUSTERING AND ETHNICITY PREDICTION
5: TOPIC MODELING – A BETTER INSIGHT INTO LARGE-SCALE TEXTS
6: DEVELOPING MODEL-BASED MOVIE RECOMMENDATION ENGINES
7: OPTIONS TRADING USING Q-LEARNING AND SCALA PLAY FRAMEWORK
8: CLIENTS SUBSCRIPTION ASSESSMENT FOR BANK TELEMARKETING USING DEEP NEURAL NETWORKS
9: FRAUD ANALYTICS USING AUTOENCODERS AND ANOMALY DETECTION
10: HUMAN ACTIVITY RECOGNITION USING RECURRENT NEURAL NETWORKS
11: IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS
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
Authors
Md. Rezaul Karim
Md. Rezaul Karim is a research scientist at Fraunhofer FIT,Germany. He is also a PhD candidate at RWTH Aachen University,Germany. Before joining FIT,he worked as a researcher at the Insight Center for Data Analytics,Ireland. He was a lead engineer at Samsung Electronics,Korea. He has 9 years’ R&D experience with C++,Java,R,Scala,and Python. He has published research papers on bioinformatics,big data,and deep learning. He has practical working experience with Spark,Zeppelin,Hadoop,Keras,Scikit-Learn,TensorFlow,Deeplearning4j,MXNet,and H2O.
