Packt | Hands-On Data Science with Java [FCO] GloDLS
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Torrent File Contents
Packt | Hands-On Data Science with Java [FCO] GloDLS
01.Introduction and Setting Infrastructure/0101.The Course Overview.mp4
01.Introduction and Setting Infrastructure/0102.Environment Configuration — Step 1.mp4
01.Introduction and Setting Infrastructure/0103.Environment Configuration — Step 2.mp4
01.Introduction and Setting Infrastructure/0104.Environment Configuration — Step 3.mp4
02.Data Manipulation and Cleaning/0201.Loading the Data from Different Sources.mp4
02.Data Manipulation and Cleaning/0202.Accessing Different Objects from The Data Sets.mp4
02.Data Manipulation and Cleaning/0203.Filtering Unwanted Data.mp4
02.Data Manipulation and Cleaning/0204.Handling the Null and the NAN.mp4
02.Data Manipulation and Cleaning/0205.Formatting Various Data Types.mp4
03.Data Interaction and Visualization/0301.Efficient Distribution of The Data.mp4
03.Data Interaction and Visualization/0302.Correlation in The Data.mp4
03.Data Interaction and Visualization/0303.Trend Analysis for Features.mp4
03.Data Interaction and Visualization/0304.Visualizing Different Data Forms.mp4
04.Implementing Essential Machine Learning Algorithms/0401.Using Unsupervised Learning.mp4
04.Implementing Essential Machine Learning Algorithms/0402.Executing Supervised Learning (Regression).mp4
04.Implementing Essential Machine Learning Algorithms/0403.Executing Supervised Learning (Classification).mp4
04.Implementing Essential Machine Learning Algorithms/0404.Formatting the Data for Your Model.mp4
04.Implementing Essential Machine Learning Algorithms/0405.Performing Cross Validation.mp4
04.Implementing Essential Machine Learning Algorithms/0406.Fitting the Model.mp4
04.Implementing Essential Machine Learning Algorithms/0407.Predicting and Determining the Accuracy Of The Model.mp4
05.Creating Deep Learning Models with Deep Learning/0501.Importing Deep Learning4 Into Your Environment.mp4
05.Creating Deep Learning Models with Deep Learning/0502.Choosing and Preparing Data for Deep Learning Model.mp4
05.Creating Deep Learning Models with Deep Learning/0503.Building A Training A Model with A Framework.mp4
05.Creating Deep Learning Models with Deep Learning/0504.Building and Training A Model Without A Framework.mp4
Exercise Files/exercise_files.zip
Discuss.FTUForum.com.html
FreeCoursesOnline.Me.html
FTUForum.com.html
How you can help Team-FTU.txt
[TGx]Downloaded from torrentgalaxy.org.txt
Torrent Downloaded From GloDls.buzz.txt
01.Introduction and Setting Infrastructure/0101.The Course Overview.mp4
- 25.35 MB
01.Introduction and Setting Infrastructure/0102.Environment Configuration — Step 1.mp4
- 45.88 MB
01.Introduction and Setting Infrastructure/0103.Environment Configuration — Step 2.mp4
- 8.02 MB
01.Introduction and Setting Infrastructure/0104.Environment Configuration — Step 3.mp4
- 7.47 MB
02.Data Manipulation and Cleaning/0201.Loading the Data from Different Sources.mp4
- 27.89 MB
02.Data Manipulation and Cleaning/0202.Accessing Different Objects from The Data Sets.mp4
- 28.29 MB
02.Data Manipulation and Cleaning/0203.Filtering Unwanted Data.mp4
- 14.06 MB
02.Data Manipulation and Cleaning/0204.Handling the Null and the NAN.mp4
- 36.69 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
- 14.33 MB
03.Data Interaction and Visualization/0302.Correlation in The Data.mp4
- 16.32 MB
03.Data Interaction and Visualization/0303.Trend Analysis for Features.mp4
- 7.72 MB
03.Data Interaction and Visualization/0304.Visualizing Different Data Forms.mp4
- 24.96 MB
04.Implementing Essential Machine Learning Algorithms/0401.Using Unsupervised Learning.mp4
- 31.53 MB
04.Implementing Essential Machine Learning Algorithms/0402.Executing Supervised Learning (Regression).mp4
- 29.52 MB
04.Implementing Essential Machine Learning Algorithms/0403.Executing Supervised Learning (Classification).mp4
- 25.72 MB
04.Implementing Essential Machine Learning Algorithms/0404.Formatting the Data for Your Model.mp4
- 23.52 MB
04.Implementing Essential Machine Learning Algorithms/0405.Performing Cross Validation.mp4
- 40.98 MB
04.Implementing Essential Machine Learning Algorithms/0406.Fitting the Model.mp4
- 11.71 MB
04.Implementing Essential Machine Learning Algorithms/0407.Predicting and Determining the Accuracy Of The Model.mp4
- 22.53 MB
05.Creating Deep Learning Models with Deep Learning/0501.Importing Deep Learning4 Into Your Environment.mp4
- 61.74 MB
05.Creating Deep Learning Models with Deep Learning/0502.Choosing and Preparing Data for Deep Learning Model.mp4
- 15.71 MB
05.Creating Deep Learning Models with Deep Learning/0503.Building A Training A Model with A Framework.mp4
- 22.32 MB
05.Creating Deep Learning Models with Deep Learning/0504.Building and Training A Model Without A Framework.mp4
- 51.42 MB
Exercise Files/exercise_files.zip
- 1.22 MB
Discuss.FTUForum.com.html
- 31.89 KB
FreeCoursesOnline.Me.html
- 108.3 KB
FTUForum.com.html
- 100.44 KB
How you can help Team-FTU.txt
- 235 Bytes
[TGx]Downloaded from torrentgalaxy.org.txt
- 524 Bytes
Torrent Downloaded From GloDls.buzz.txt
- 84 Bytes
Torrent Description
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.
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.