Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.
About This Book
* Explore machine learning techniques with the prominent Scala libraries such as H2O, Spark ML/MLlib, MXNET and DeepLearning4j to perform variety of tasks
* A comprehensive guide covering all key aspects of building models from collection, storing, analysis predicting to monitoring
* Implement real-world practical projects to take machine learning with Scala to the next level
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. Also having some basic understanding of high-level knowledge of these techniques would act as an added advantage.
What You Will Learn
* Advanced Regression Techniques for Analyzing Insurance Severity Claim
* Tree Ensembles for Analyzing and Predicting Telecommunication Churn
* High-Frequency Bitcoin Price Prediction from Historical and Live Data
* Using K-means and MLP for Population Scale Clustering and Ethnicity Prediction
* Topic Modelling for Better Insights from Large-Scale Texts in NLP
* Developing Movie Recommenders using Collaborative Filtering Approach
* Deep Reinforcement Learning App using Markov Decision Processes
* Clients Subscription Assessment using Telemarketing and Neural Networks
* Fraud Analytics using Autoencoders and Anomaly Detection
* Applying LSTM for Human Activity Recognition
* Developing an Image Classifier using CNN on Real Image Datasets.
In Detail
Machine learning has made huge impacts on academia and industry by turning data into actionable intelligence. Scala, on the other hand, has been observing a steady rise in adoption over the past few years, especially in the field of data science and analytics. This book is comprehended for data scientists, data engineers and deep learning enthusiasts who have a good background with complex numerical computing and want to know more hands-on machine learning application development.
So If you're well versed in 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! Through 11 end-to-end projects, you will be acquainted with the popular machine learning libraries such as Spark ML, H2O, MDP, DeepLearning4j, and MXNet.
At the end, you will be able to dominate numerical computing, deep learning, and functional programming to carry out complex numerical tasks to develop, build and deploy research or commercial projects in a production ready environment.
