Hands-On Data Science with Java [Video]

Rahab Wangari

Saturday, March 30, 2019

New Release!

Load, clean, analyze, and visualize your data using Java. Develop Machine Learning and Deep Learning models from scratch

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Video Details

ISBN 13 9781787125346

Course Length 3 hours 5 minutes

Introduction and Setting Infrastructure

The Course Overview

Environment Configuration — Step 1

Environment Configuration — Step 2

Environment Configuration — Step 3

Data Manipulation and Cleaning

Loading the Data from Different Sources

Accessing Different Objects from The Data Sets

Filtering Unwanted Data

Handling the Null and the NAN

Formatting Various Data Types

Data Interaction and Visualization

Efficient Distribution of The Data

Correlation in The Data

Trend Analysis for Features

Visualizing Different Data Forms

Implementing Essential Machine Learning Algorithms

Using Unsupervised Learning

Executing Supervised Learning (Regression)

Executing Supervised Learning (Classification)

Formatting the Data for Your Model

Performing Cross Validation

Fitting the Model

Predicting and Determining the Accuracy Of The Model

Creating Deep Learning Models with Deep Learning

Importing Deep Learning4 Into Your Environment

Choosing and Preparing Data for Deep Learning Model

Building A Training A Model with A Framework

Building and Training A Model Without A Framework

Video Description

Building intensive data science projects is a long and tedious process. Analyzing large data sets requires knowledge of how to deal with all data structures. This means easy access, easier storage, and faster loading. Java provides an efficient way of doing these tasks to improve the efficiency of such data-intensive projects.
In this course, you will use efficient Java libraries to simplify your data analysis. You will perform essential tasks such as loading, cleaning, and visualizing your data. You'll connect your data with different frameworks, making it easier to analyze small and large data sets. Using the DeepLearning4j library makes training your ML models that much simpler.
By the end of the course, you will be able to build sophisticated and robust data science projects. You will simplify the integration challenges in production using Java. All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Hands-on-Data-Science-with-Java

Style and Approach

In this hands-on course, the focus is on solving real-world problems. You will start with an introduction to loading data from different sources, and data cleaning, exploration, and visualization. You will implement different machine learning models and build Deep learning models compatible with JVM using Deeplearning4J. To ensure you understand everything from the beginning, we will use familiar data sets in the different scenarios to reduce the time taken to become familiar with the data set.

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What You Will Learn

Perform data science tasks using a set of robust Java tools and libraries
Develop robust Java code that correctly executes in multiple environments
Load and analyze data from databases and flat files irrespective of the size and variety of the data
Clean and filter data and handle null values for data formatting
Visualize data to understanding its distribution and discover hidden patterns
Compare supervised and unsupervised machine learning models and their use cases
Implement machine learning and deep learning models with real-world data sets
Split training and testing datasets and determine the accuracy of models using different techniques

Authors

Rahab Wangari

Rahab Wangari is a data science expert with over 4 years' experience in data analytics and software engineering. She is currently a data scientist at Hepta Analytics limited where she oversees the implementation of data analytics projects. She holds a Masters degree in Information Technology with a concentration in Data Science from Carnegie Mellon University and a Bachelor of Science degree in Computer Science (Cum Laude) from Ashesi University College. Her background is in diverse sectors: Academia, Banking, and Manufacturing. Her major interests are in data analytics, machine learning, cyber security management, and software engineering.

Introduction and Setting Infrastructure

The Course Overview

Environment Configuration — Step 1

Environment Configuration — Step 2

Environment Configuration — Step 3

Data Manipulation and Cleaning

Loading the Data from Different Sources

Accessing Different Objects from The Data Sets

Filtering Unwanted Data

Handling the Null and the NAN

Formatting Various Data Types

Data Interaction and Visualization