Book Image

Machine Learning with Scala Quick Start Guide

By : Md. Rezaul Karim, Ajay Kumar N
Book Image

Machine Learning with Scala Quick Start Guide

By: Md. Rezaul Karim, Ajay Kumar N

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.

Related Content you might be interested in

Current Title:

Small book image
Machine Learning with Scala Quick Start Guide
Md. Rezaul Karim | Ajay Kumar N
Apr, 2019 | 220 pages
Small book image
Java Deep Learning Projects
Md Rezaul Karim
Jun, 2018 | 436 pages
Small book image
Hands-On Deep Learning for IoT
Md Rezaul Karim | Mohammad Abdur Razzaque
Jun, 2019 | 308 pages
Small book image
Scala and Spark for Big Data Analytics
Md Rezaul Karim | Sridhar Alla
Jul, 2017 | 796 pages
Table of Contents (9 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to Machine Learning with Scala
Introduction to Machine Learning with Scala
Technical requirements
Overview of ML
ML tasks
Overview of Scala
ML libraries in Scala
Getting started learning
Summary
2
Scala for Regression Analysis
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
3
Scala for Learning Classification
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
4
Scala for Tree-Based Ensemble Techniques
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
5
Scala for Dimensionality Reduction and Clustering
Scala for Dimensionality Reduction and Clustering
Technical requirements
Overview of unsupervised learning
Clustering analysis through examples
Dimensionality reduction
Summary
6
Scala for Recommender System
Scala for Recommender System
Technical requirements
Overview of recommendation systems
Model-based book recommendation system
Summary
7
Introduction to Deep Learning with Scala
Introduction to Deep Learning with Scala
Technical requirements
DL versus ML
DL and ANNs
Neural network architectures
DL frameworks
Getting started with learning
Summary
8
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Hyperparameter tuning and cross-validation

In machine learning, the term hyperparameter refers to those parameters that cannot be learned from the regular training process directly. These are the various knobs that you can tweak on your machine learning algorithms. Hyperparameters are usually decided by training the model with different combinations of the parameters and deciding which ones work best by testing them. Ultimately, the combination that provides the best model would be our final hyperparameters. Setting hyperparameters can have a significant influence on the performance of the trained models.

On the other hand, cross-validation is often used in conjunction with hyperparameter tuning. Cross-validation (also know as rotation estimation) is a model validation technique for assessing the quality of the statistical analysis and results. Cross-validation helps to describe...

Previous Section
End of Section 8
Next Section