Java: Data Science Made Easy
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Free ChapterModule 1
-
Getting Started with Data Science
- Getting Started with Data Science
- Problems solved using data science
- Understanding the data science problem - Â solving approach
- Acquiring data for an application
- The importance and process of cleaning data
- Visualizing data to enhance understanding
- The use of statistical methods in data science
- Machine learning applied to data science
- Using neural networks in data science
- Deep learning approaches
- Performing text analysis
- Visual and audio analysis
- Improving application performance using parallel techniques
- Assembling the pieces
- Summary
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Data Acquisition
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Data Cleaning
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Data Visualization
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Statistical Data Analysis Techniques
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Machine Learning
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Neural Networks
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Deep Learning
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Text Analysis
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Visual and Audio Analysis
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Visual and Audio Analysis
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Mathematical and Parallel Techniques for Data Analysis
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Bringing It All Together
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Module 2
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Data Science Using Java
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Data Processing Toolbox
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Exploratory Data Analysis
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Supervised Learning - Classification and Regression
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Unsupervised Learning - Clustering and Dimensionality Reduction
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Working with Text - Natural Language Processing and Information Retrieval
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Extreme Gradient Boosting
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Deep Learning with DeepLearning4J
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Scaling Data Science
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Deploying Data Science Models
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Bibliography
About this
book
Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings.
By the end of this course, you will be up and running with various facets of data science using Java, in no time at all.
This course contains premium content from two of our recently published popular titles:
- Java for Data Science
- Mastering Java for Data Science
- Publication date:
- July 2017
- Publisher
- Packt
- ISBN
- 9781788475655
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