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TORRENT DETAILS

[FreeCoursesOnline.Me] [Packt] Hands-On Data Science With Java [FCO]

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	4a85e9f998c3648c88de4a230e21a3847a74922c
Size:	603.4 MB in 29 files
Release Date(s):	Torrent Release: 15-04-2019 by user
Swarm:	0 Seeds & 0 Peers
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DESCRIPTION

By: Rahab Wangari

Released: Saturday, March 30, 2019 [New Release!]

Torrent Contains: 31 Files, 6 Folders

Course Source: https://www.packtpub.com/big-data-and-business-intelligence/hands-data-science-java-video

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

Video Details

ISBN 9781787125346

Course Length 3 hours 5 minutes

Table of Contents

• INTRODUCTION AND SETTING INFRASTRUCTURE

• DATA MANIPULATION AND CLEANING

• DATA INTERACTION AND VISUALIZATION

• IMPLEMENTING ESSENTIAL MACHINE LEARNING ALGORITHMS

• CREATING DEEP LEARNING MODELS WITH DEEP LEARNING

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.

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.

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FILE LIST

Filename		Size
	01.Introduction and Setting Infrastructure/0101.The Course Overview.mp4	25.4 MB
	01.Introduction and Setting Infrastructure/0102.Environment Configuration — Step 1.mp4	45.9 MB
	01.Introduction and Setting Infrastructure/0103.Environment Configuration — Step 2.mp4	8 MB
	01.Introduction and Setting Infrastructure/0104.Environment Configuration — Step 3.mp4	7.5 MB
	02.Data Manipulation and Cleaning/0201.Loading the Data from Different Sources.mp4	27.9 MB
	02.Data Manipulation and Cleaning/0202.Accessing Different Objects from The Data Sets.mp4	28.3 MB
	02.Data Manipulation and Cleaning/0203.Filtering Unwanted Data.mp4	14.1 MB
	02.Data Manipulation and Cleaning/0204.Handling the Null and the NAN.mp4	36.7 MB
	02.Data Manipulation and Cleaning/0205.Formatting Various Data Types.mp4	8.3 MB
	03.Data Interaction and Visualization/0301.Efficient Distribution of The Data.mp4	14.3 MB
	03.Data Interaction and Visualization/0302.Correlation in The Data.mp4	16.3 MB
	03.Data Interaction and Visualization/0303.Trend Analysis for Features.mp4	7.7 MB
	03.Data Interaction and Visualization/0304.Visualizing Different Data Forms.mp4	25 MB
	04.Implementing Essential Machine Learning Algorithms/0401.Using Unsupervised Learning.mp4	31.5 MB
	04.Implementing Essential Machine Learning Algorithms/0402.Executing Supervised Learning (Regression).mp4	29.5 MB
	04.Implementing Essential Machine Learning Algorithms/0403.Executing Supervised Learning (Classification).mp4	25.7 MB
	04.Implementing Essential Machine Learning Algorithms/0404.Formatting the Data for Your Model.mp4	23.5 MB
	04.Implementing Essential Machine Learning Algorithms/0405.Performing Cross Validation.mp4	41 MB
	04.Implementing Essential Machine Learning Algorithms/0406.Fitting the Model.mp4	11.7 MB
	04.Implementing Essential Machine Learning Algorithms/0407.Predicting and Determining the Accuracy Of The Model.mp4	22.5 MB
	05.Creating Deep Learning Models with Deep Learning/0501.Importing Deep Learning4 Into Your Environment.mp4	61.7 MB
	05.Creating Deep Learning Models with Deep Learning/0502.Choosing and Preparing Data for Deep Learning Model.mp4	15.7 MB
	05.Creating Deep Learning Models with Deep Learning/0503.Building A Training A Model with A Framework.mp4	22.3 MB
	05.Creating Deep Learning Models with Deep Learning/0504.Building and Training A Model Without A Framework.mp4	51.4 MB
	Discuss.FTUForum.com.html	31.9 KB
	Exercise Files/exercise_files.zip	1.2 MB
	FreeCoursesOnline.Me.html	108.3 KB
	FTUForum.com.html	100.4 KB
	How you can help Team-FTU.txt	235 B

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