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Machine Learning with Scala Quick Start Guide

By : Karim, Kumar N

Buy this Book

Machine Learning with Scala Quick Start Guide

By: Karim, Kumar N

Buy this Book

Overview of this book

Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala. The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms. It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.

Preface

Who this book is for

What this book covers

To get the most out of this book

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Free Chapter

Introduction to Machine Learning with Scala

Technical requirements

Overview of ML

ML tasks

Overview of Scala

ML libraries in Scala

Getting started learning

Summary

Scala for Regression Analysis

Technical requirements

An overview of regression analysis

Regression analysis algorithms

Learning regression analysis through examples

Linear regression

Generalized linear regression (GLR)

Hyperparameter tuning and cross-validation

Summary

Scala for Learning Classification

Technical requirements

Overview of classification

Developing predictive models for churn

LR for churn prediction

NB for churn prediction

SVM for churn prediction

Summary

Scala for Tree-Based Ensemble Techniques

Technical requirements

Decision trees and tree ensembles

Decision trees for supervised learning

Gradient boosted trees for supervised learning

Random forest for supervised learning

What's next?

Summary

Scala for Dimensionality Reduction and Clustering

Technical requirements

Overview of unsupervised learning

Clustering analysis through examples

Dimensionality reduction

Summary

Scala for Recommender System

Technical requirements

Overview of recommendation systems

Model-based book recommendation system

Summary

Introduction to Deep Learning with Scala

Technical requirements

DL versus ML

DL and ANNs

Neural network architectures

DL frameworks

Getting started with learning

Summary

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Overview of classification

As a supervised learning task, classification is the problem of identifying which set of observations (sample) belongs to what based on one or more independent variables. This learning process is based on a training set containing observations (or instances) about the class or label of membership. Typically, classification problems are when we are training a model to predict quantitative (but discrete) targets, such as spam detection, churn prediction, sentiment analysis, cancer type prediction, and so on.

Suppose we want to develop a predictive model, which will predict whether a student is competent enough to get admission into computer science based on his/her competency in TOEFL and GRE. Also, suppose we have some historical data in the following range/format:

TOEFL: Between 0 and 100
GRE: Between 0 and 100
Admission: 1 for admitted, 0 if not admitted...