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Federico Mestrone
Data is at the heart of machine learning. This course will teach you how to bring data into Java from various sources, as well as how to perform basic tidying up and transformations in view of further processing by specialized Java ML libraries.
Read more
Data is at the heart of machine learning. This course will teach you how to bring data into Java from various sources, as well as how to perform basic tidying up and transformations in view of further processing by specialized Java ML libraries.
Read more
Data is at the heart of machine learning. This course will teach you how to bring data into Java from various sources, as well as how to perform basic tidying up and transformations in view of further processing by specialized Java ML libraries.
Machine learning algorithms require that data is formatted and presented in very specific ways. In this course, Preparing Data for Machine Learning with Java, you’ll learn to use the standard Java API to make data ready for ML libraries. First, you’ll explore various options to read files into Java objects and data structures. Next, you’ll discover how to scrape the web for data you could use in your ML models. Finally, you’ll learn how to perform transformation both in vanilla Java and at scale with the Beam SDK. When you’re finished with this course, you’ll have the skills and knowledge of data gathering needed to digitize various sources into Java data structures.
Register for this course and see more details by visiting:
OpenCourser.com/course/8y4ae2/preparing
What's inside
Syllabus
Course Overview
Ingesting Data from Files in Various Formats
Automating Data Collection and Scheduling
Data Cleaning Using Regex and Formatter
Read more
Syllabus
Course Overview
Ingesting Data from Files in Various Formats
Automating Data Collection and Scheduling
Data Cleaning Using Regex and Formatter
Read more
Data Transformation
Data Preparation at Scale
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops data gathering, cleaning, and preparation skills that digitize sources into Java data structures
Taught by Federico Mestrone, a recognized expert in Java machine learning
Introduces the Java API for data preparation
Can help learners advance into a foundational course on machine learning
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Preparing Data for Machine Learning with Java with these
activities:
Review Data Structures & Algorithms
Show steps
Recall key data structures and algorithms essential for understanding the course materials.
Browse courses on
Data Structures
Show steps
-
Revisit notes or study materials on basic data structures (e.g., arrays, linked lists, stacks, queues).
-
Practice solving problems using fundamental algorithms (e.g., sorting, searching, recursion).
Walkthrough Java IO with Pluralsight
Show steps
Strengthen understanding of Java IO, a crucial skill for data ingestion into Java data structures.
Browse courses on
Data Ingestion
Show steps
-
Sign up for a free Pluralsight account
-
Search for and enroll in the "Java IO Fundamentals" course
-
Watch the video lessons and complete the interactive exercises
-
Complete the course quiz to assess your understanding
Explore Java Data Manipulation APIs
Show steps
Become familiar with essential Java APIs for data handling.
Browse courses on
Java API
Show steps
-
Follow tutorials on Java API for file reading and parsing (e.g., java.io, java.nio).
-
Explore API for data structuring (e.g., java.util, java.collections).
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Practice Data Wrangling with Java Regex
Show steps
Develop proficiency in data cleaning using Java Regex, essential for data preparation.
Browse courses on
Data Cleaning
Show steps
-
Find a dataset with messy data that needs cleaning
-
Use Java Regex to write patterns for identifying and replacing incorrect or inconsistent data
-
Test and refine your patterns until the data is cleaned to a satisfactory level
-
Repeat the process with different datasets
Data Tidying Exercises
Show steps
Gain hands-on experience in cleaning and tidying data for machine learning.
Browse courses on
Data Cleaning
Show steps
-
Use Java methods (e.g., replaceAll(), split()) to remove unwanted characters or split strings.
-
Practice creating custom functions for data transformations.
Data Wrangling Challenge
Show steps
Apply data wrangling techniques to a real-world dataset.
Show steps
-
Collect or identify a suitable dataset for the project.
-
Implement data cleaning, transformation, and feature engineering on the dataset.
Master Data Transformation with Java Beam SDK
Show steps
Gain proficiency in data transformation using Java Beam, a powerful tool for large-scale data processing.
Browse courses on
Data Transformation
Show steps
-
Follow the "Apache Beam Getting Started" guide
-
Create a Java project and set up the Beam SDK
-
Write a Beam pipeline to perform a specific data transformation
-
Test and refine your pipeline to achieve the desired results
Master Apache Beam for Data Processing
Show steps
Gain insights into using Apache Beam for scalable data processing.
Browse courses on
Apache Beam
Show steps
-
Complete tutorials on Apache Beam concepts (e.g., pipelines, PCollections).
-
Build a sample data processing pipeline using Apache Beam.
Assist Peers in Data Preparation
Show steps
Enhance understanding of data preparation principles by sharing knowledge with others.
Browse courses on
Mentoring
Show steps
-
Answer questions and offer guidance to classmates on data preprocessing techniques.
-
Review and provide feedback on peers' code for data handling tasks.
Develop a Data Preparation Module for a Machine Learning Project
Show steps
Apply data preparation skills to a practical project, deepening your understanding of the process.
Browse courses on
Data Preparation
Show steps
-
Choose a dataset and define the machine learning task
-
Use Java to ingest, clean, and transform the data
-
Package the data preparation code into a reusable module
-
Integrate the module into a machine learning pipeline
Build a Custom Data Reader
Show steps
Develop a reusable data reader tailored to specific data sources.
Browse courses on
Data Engineering
Show steps
-
Design an interface or class for the custom data reader.
-
Implement methods to parse and load data from a specific data source.
Review Data Structures & Algorithms
Show steps
Recall key data structures and algorithms essential for understanding the course materials.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Revisit notes or study materials on basic data structures (e.g., arrays, linked lists, stacks, queues).
- Practice solving problems using fundamental algorithms (e.g., sorting, searching, recursion).
Walkthrough Java IO with Pluralsight
Show steps
Strengthen understanding of Java IO, a crucial skill for data ingestion into Java data structures.
Browse courses on
Data Ingestion
Show steps
Show steps
Show steps
- Sign up for a free Pluralsight account
- Search for and enroll in the "Java IO Fundamentals" course
- Watch the video lessons and complete the interactive exercises
- Complete the course quiz to assess your understanding
Explore Java Data Manipulation APIs
Show steps
Become familiar with essential Java APIs for data handling.
Browse courses on
Java API
Show steps
Show steps
Show steps
- Follow tutorials on Java API for file reading and parsing (e.g., java.io, java.nio).
- Explore API for data structuring (e.g., java.util, java.collections).
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Practice Data Wrangling with Java Regex
Show steps
Develop proficiency in data cleaning using Java Regex, essential for data preparation.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Find a dataset with messy data that needs cleaning
- Use Java Regex to write patterns for identifying and replacing incorrect or inconsistent data
- Test and refine your patterns until the data is cleaned to a satisfactory level
- Repeat the process with different datasets
Data Tidying Exercises
Show steps
Gain hands-on experience in cleaning and tidying data for machine learning.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Use Java methods (e.g., replaceAll(), split()) to remove unwanted characters or split strings.
- Practice creating custom functions for data transformations.
Data Wrangling Challenge
Show steps
Apply data wrangling techniques to a real-world dataset.
Show steps
Show steps
Show steps
- Collect or identify a suitable dataset for the project.
- Implement data cleaning, transformation, and feature engineering on the dataset.
Master Data Transformation with Java Beam SDK
Show steps
Gain proficiency in data transformation using Java Beam, a powerful tool for large-scale data processing.
Browse courses on
Data Transformation
Show steps
Show steps
Show steps
- Follow the "Apache Beam Getting Started" guide
- Create a Java project and set up the Beam SDK
- Write a Beam pipeline to perform a specific data transformation
- Test and refine your pipeline to achieve the desired results
Master Apache Beam for Data Processing
Show steps
Gain insights into using Apache Beam for scalable data processing.
Browse courses on
Apache Beam
Show steps
Show steps
Show steps
- Complete tutorials on Apache Beam concepts (e.g., pipelines, PCollections).
- Build a sample data processing pipeline using Apache Beam.
Assist Peers in Data Preparation
Show steps
Enhance understanding of data preparation principles by sharing knowledge with others.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Answer questions and offer guidance to classmates on data preprocessing techniques.
- Review and provide feedback on peers' code for data handling tasks.
Develop a Data Preparation Module for a Machine Learning Project
Show steps
Apply data preparation skills to a practical project, deepening your understanding of the process.
Browse courses on
Data Preparation
Show steps
Show steps
Show steps
- Choose a dataset and define the machine learning task
- Use Java to ingest, clean, and transform the data
- Package the data preparation code into a reusable module
- Integrate the module into a machine learning pipeline
Build a Custom Data Reader
Show steps
Develop a reusable data reader tailored to specific data sources.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Design an interface or class for the custom data reader.
- Implement methods to parse and load data from a specific data source.
Career center
Learners who complete Preparing Data for Machine Learning with Java will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
Data Engineers are primarily responsible for collecting, transforming, and storing data. This is necessary for many different business processes, such as data analysis and machine learning. This course helps build a strong foundation for a Data Engineer, by teaching the student how to read data from files and webpages, and transform it into a format suitable for machine learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. These models can be used for a variety of purposes, such as predicting customer behavior or identifying fraud. This course will benefit a Machine Learning Engineer by teaching them how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
Data Scientist
Data Scientist
Data Scientists are responsible for the entire data science process, from collecting and cleaning data to building and evaluating models. This course will help a Data Scientist to learn how to read data from files and webpages, and transform it into a format suitable for use in machine learning models. It will also teach them how to perform transformations both in vanilla Java and at scale with the Beam SDK.
Data Analyst
Data Analyst
Data Analysts interpret large amounts of data to identify trends and patterns. This information is crucial in driving informed decision making. This course can be very useful for a Data Analyst by allowing them to learn how to transform data into a format suitable for machine learning, which can help them perform much more advanced data analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While it is not a requirement, many Software Engineers work with machine learning and data science, and therefore understanding how to prepare data for machine learning can be very helpful. This course will teach a Software Engineer how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
Data Architect
Data Architect
A Data Architect is responsible for designing and maintaining data systems. Data systems are used to store, manage, and process data, and can be used for a variety of purposes, such as data analysis and machine learning. This course will benefit a Data Architect by teaching them how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
Business Analyst
Business Analyst
A Business Analyst identifies business needs and translates them into requirements for the development team. They also work with stakeholders to ensure that the final product meets the business's needs. This course will provide a Business Analyst with a better understanding of how data is used in machine learning, and how to prepare data for machine learning models.
Data Warehouse Engineer
Data Warehouse Engineer
A Data Warehouse Engineer is responsible for designing and maintaining data warehouses. Data warehouses are used to store and manage large amounts of data, and can be used for a variety of purposes, such as data analysis and machine learning. This course will benefit a Data Warehouse Engineer by teaching them how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
Database Administrator
Database Administrator
A Database Administrator is responsible for managing and maintaining databases. Databases are used to store and manage large amounts of data, and can be used for a variety of purposes, such as data analysis and machine learning. This course will benefit a Database Administrator by teaching them how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve problems in a variety of industries, such as manufacturing, healthcare, and transportation. This course can be helpful for an Operations Research Analyst by teaching them how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and recommend investments. They also provide advice to clients on financial planning and management. This course can be helpful for a Financial Analyst by teaching them how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
Statistician
Statistician
Statisticians apply statistical methods to collect, analyze, interpret, and present data. They also develop and use mathematical models to describe and predict the behavior of data. This course can be helpful for a Statistician by teaching them how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help businesses improve their performance. They use data to identify trends and patterns, and they develop reports and dashboards to help businesses make better decisions. This course can be helpful for a Business Intelligence Analyst by teaching them how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
Market Research Analyst
Market Research Analyst
Market Research Analysts conduct surveys, interviews, and other research activities to gather information about a target market. They use this information to help businesses understand their customers and make better marketing decisions. This course can be helpful for a Market Research Analyst by teaching them how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
Quality Assurance Engineer
Quality Assurance Engineer
Quality Assurance Engineers test software to ensure that it meets the requirements of the users. This course can be helpful for a Quality Assurance Engineer by teaching them how to read data from files and webpages, and transform it into a format suitable for use in machine learning models.
For more career information including salaries, visit:
OpenCourser.com/course/8y4ae2/preparing
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Preparing Data for Machine Learning with Java.
Introduces the Java programming language and its use in data science, providing a solid foundation for data preparation and analysis.
Save
A foundational text on design patterns, offering valuable insights into object-oriented programming and software architecture.
Save
A classic reference for Java best practices, offering valuable insights into software design and coding.
Save
Promotes clean coding principles and patterns, emphasizing readability, maintainability, and extensibility.
Explores Java 8's functional programming features, which can enhance the efficiency and readability of data manipulation and processing tasks.
Explores Java's generics and collections framework, useful for understanding data structures and efficient data management.
Covers concurrency and multithreading in Java, essential topics for efficient data processing and handling.
Save
A beginner-friendly and engaging introduction to Java, using a conversational and humorous approach.
Provides a concise yet comprehensive introduction to Java, helpful for learners with limited programming experience.
Save
Serves as a beginner-friendly introduction to Java, providing a solid foundation for the course's content.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8y4ae2/preparing
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Time
122 hours
Level
Introductory
Via
Pluralsight
Instructor
Federico Mestrone
Language
English
Good to know
Develops data gathering, cleaning, and preparation skills that digitize sources into Java data structures
Taught by Federico Mestrone, a recognized expert in Java machine learning
Introduces the Java API for data preparation
Can help learners advance into a foundational course on machine learning
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