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Dan Hermes
Creating data models using machine learning requires effective training data. This course will teach you how to feed your data model’s training process using data labeling for supervised training and unlabeled data for semi-supervised training.
Read more
Creating data models using machine learning requires effective training data. This course will teach you how to feed your data model’s training process using data labeling for supervised training and unlabeled data for semi-supervised training.
Read more
Creating data models using machine learning requires effective training data. This course will teach you how to feed your data model’s training process using data labeling for supervised training and unlabeled data for semi-supervised training.
Machine learning data models are only as effective as their training data. In this course, Efficient Data Feeding and Labeling for Model Training, you’ll gain the ability to finalize the preparation of your training data and choose the most appropriate manner to feed it into your data model training. First, you’ll explore the meaning of data feeding and common techniques. Next, you’ll discover data labeling for supervised learning, followed by unlabeled data for semi-supervised learning. Finally, you’ll learn how to employ data labeling tools.
When you’re finished with this course, you’ll have the skills and knowledge of data labeling and feeding needed to train machine learning data models.
Register for this course and see more details by visiting:
OpenCourser.com/course/0xuj83/efficient
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What's inside
Syllabus
Course Overview
Data Feeding
Data Labeling
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what to watch for , and
possible dealbreakers
Explores data feeding and labeling, which is the core of training data preparation for machine learning models
Provides guidance on finalizing training data preparation and choosing the most appropriate manner to feed it into data model training
Taught by recognized industry expert Dan Hermes
Suitable for learners with a background in machine learning who seek to enhance their skills in data feeding and labeling for model training
May require additional research or knowledge of specific machine learning algorithms for practical implementation
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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 Efficient Data Feeding and Labeling for Model Training with these
activities:
Practice data labeling
Show steps
This activity will help you practice data labeling, which is a critical skill for training machine learning data models.
Browse courses on
Data Labeling
Show steps
-
Find a data labeling dataset.
-
Label the data using the instructions provided.
-
Check your work against the provided answer key.
Follow a tutorial on data feeding
Show steps
This activity will help you learn how to feed data into a machine learning model.
Show steps
-
Find a tutorial on data feeding.
-
Follow the instructions in the tutorial.
Discuss data labeling and feeding with a peer
Show steps
This activity will help you learn from others and gain different perspectives on data labeling and feeding.
Browse courses on
Data Labeling
Show steps
-
Find a peer who is also taking the course.
-
Schedule a time to meet and discuss data labeling and feeding.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a cheat sheet on data labeling and feeding
Show steps
This activity will help you solidify your understanding of data labeling and feeding by creating a cheat sheet that you can reference later.
Browse courses on
Data Labeling
Show steps
-
Identify the key concepts of data labeling and feeding.
-
Create a cheat sheet that summarizes these concepts.
Start a data labeling project
Show steps
This project will help you gain hands-on experience with data labeling.
Browse courses on
Data Labeling
Show steps
-
Find a dataset that needs to be labeled.
-
Create a data labeling plan.
-
Label the data.
-
Validate your labels.
Create a data labeling tool
Show steps
This project will help you apply your knowledge of data labeling to a real-world problem.
Browse courses on
Data Labeling
Show steps
-
Identify a problem that could be solved using a data labeling tool.
-
Design and develop a data labeling tool.
-
Test and evaluate your tool.
Practice data labeling
Show steps
This activity will help you practice data labeling, which is a critical skill for training machine learning data models.
Browse courses on
Data Labeling
Show steps
Show steps
Show steps
- Find a data labeling dataset.
- Label the data using the instructions provided.
- Check your work against the provided answer key.
Follow a tutorial on data feeding
Show steps
This activity will help you learn how to feed data into a machine learning model.
Show steps
Show steps
Show steps
- Find a tutorial on data feeding.
- Follow the instructions in the tutorial.
Discuss data labeling and feeding with a peer
Show steps
This activity will help you learn from others and gain different perspectives on data labeling and feeding.
Browse courses on
Data Labeling
Show steps
Show steps
Show steps
- Find a peer who is also taking the course.
- Schedule a time to meet and discuss data labeling and feeding.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a cheat sheet on data labeling and feeding
Show steps
This activity will help you solidify your understanding of data labeling and feeding by creating a cheat sheet that you can reference later.
Browse courses on
Data Labeling
Show steps
Show steps
Show steps
- Identify the key concepts of data labeling and feeding.
- Create a cheat sheet that summarizes these concepts.
Start a data labeling project
Show steps
This project will help you gain hands-on experience with data labeling.
Browse courses on
Data Labeling
Show steps
Show steps
Show steps
- Find a dataset that needs to be labeled.
- Create a data labeling plan.
- Label the data.
- Validate your labels.
Create a data labeling tool
Show steps
This project will help you apply your knowledge of data labeling to a real-world problem.
Browse courses on
Data Labeling
Show steps
Show steps
Show steps
- Identify a problem that could be solved using a data labeling tool.
- Design and develop a data labeling tool.
- Test and evaluate your tool.
Career center
Learners who complete Efficient Data Feeding and Labeling for Model Training will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of machine learning to build models that can be used to solve a variety of problems. They are responsible for collecting and preparing data, training models, and evaluating results. This course will help Data Scientists understand the different ways to feed data into a model, and how to label data for supervised learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building and deploying machine learning models. They work closely with Data Scientists to ensure that the models are accurate and efficient. This course will help Machine Learning Engineers understand the different ways to feed data into a model, and how to label data for supervised learning.
Data Engineer
Data Engineer
Data Engineers work closely with Data Scientists to build the infrastructure that will allow them to complete their projects. They are responsible for maintaining the databases and ensuring that data is constantly feeding into the machine learning models. This course will help Data Engineers understand the different ways to feed data into a model, and how to label data for supervised learning.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data to help businesses make better decisions. They are responsible for collecting, cleaning, and analyzing data, and presenting their findings to stakeholders. This course will help Data Analysts understand the different ways to feed data into a model, and how to label data for supervised learning.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business to help organizations improve their operations. They are responsible for analyzing data, identifying problems, and recommending solutions. This course will help Business Analysts understand the different ways to feed data into a model, and how to label data for supervised learning.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They are responsible for ensuring that projects are completed on time, within budget, and to the required quality. This course will help Project Managers understand the different ways to feed data into a model, and how to label data for supervised learning.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and testing software applications. They are responsible for ensuring that software is reliable, efficient, and secure. This course will help Software Engineers understand the different ways to feed data into a model, and how to label data for supervised learning.
Database Administrator
Database Administrator
Database Administrators are responsible for managing databases. They are responsible for ensuring that databases are reliable, efficient, and secure. This course will help Database Administrators understand the different ways to feed data into a model, and how to label data for supervised learning.
Data Architect
Data Architect
Data Architects are responsible for designing and managing data systems. They are responsible for ensuring that data is stored, processed, and accessed in a way that meets the needs of the business. This course will help Data Architects understand the different ways to feed data into a model, and how to label data for supervised learning.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and statistics to solve problems in a variety of industries. They are responsible for developing and implementing mathematical models, and for presenting their findings to stakeholders. This course will help Operations Research Analysts understand the different ways to feed data into a model, and how to label data for supervised learning.
Statistician
Statistician
Statisticians use their knowledge of statistics to collect, analyze, and interpret data. They are responsible for developing and implementing statistical methods, and for presenting their findings to stakeholders. This course will help Statisticians understand the different ways to feed data into a model, and how to label data for supervised learning.
Marketing Analyst
Marketing Analyst
Marketing Analysts use their knowledge of marketing to analyze and interpret data about customers. They are responsible for developing and implementing marketing analysis studies, and for presenting their findings to stakeholders. This course may help Marketing Analysts understand the different ways to feed data into a model, and how to label data for supervised learning.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance to analyze and interpret financial data. They are responsible for making recommendations to investors and other stakeholders. This course may help Financial Analysts understand the different ways to feed data into a model, and how to label data for supervised learning.
Sales Analyst
Sales Analyst
Sales Analysts use their knowledge of sales to analyze and interpret data about customers. They are responsible for developing and implementing sales analysis studies, and for presenting their findings to stakeholders. This course may help Sales Analysts understand the different ways to feed data into a model, and how to label data for supervised learning.
Market Researcher
Market Researcher
Market Researchers use their knowledge of marketing to collect, analyze, and interpret data about consumers. They are responsible for developing and implementing market research studies, and for presenting their findings to stakeholders. This course may help Market Researchers understand the different ways to feed data into a model, and how to label data for supervised learning.
For more career information including salaries, visit:
OpenCourser.com/course/0xuj83/efficient
Reading list
We've selected 11 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
Efficient Data Feeding and Labeling for Model Training.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers the fundamentals of machine learning, machine learning algorithms, and machine learning applications. It valuable resource for anyone who wants to learn about machine learning from a probabilistic perspective.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It covers the fundamentals of Bayesian reasoning, Bayesian machine learning algorithms, and Bayesian machine learning applications. It valuable resource for anyone who wants to learn about Bayesian reasoning and machine learning.
Provides a comprehensive overview of statistical learning. It covers the fundamentals of statistical learning, statistical learning algorithms, and statistical learning applications. It valuable resource for anyone who wants to learn about statistical learning.
Provides a comprehensive overview of reinforcement learning. It covers the fundamentals of reinforcement learning, reinforcement learning algorithms, and reinforcement learning applications. It valuable resource for anyone who wants to learn about reinforcement learning.
Provides a practical introduction to data science for business professionals. It covers the fundamentals of data science, data analysis techniques, and machine learning algorithms. It valuable resource for anyone who wants to learn how to use data to make better decisions.
Save
Provides a comprehensive overview of deep learning. It covers the fundamentals of deep learning, deep learning architectures, and deep learning applications. It valuable resource for anyone who wants to learn about deep learning.
Provides a practical introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers the fundamentals of machine learning, machine learning algorithms, and machine learning applications. It valuable resource for anyone who wants to learn how to use Python for machine learning.
Provides a practical introduction to deep learning with Python. It covers the fundamentals of deep learning, deep learning architectures, and deep learning applications. It valuable resource for anyone who wants to learn how to use Python for deep learning.
Provides a hands-on introduction to data science. It covers the fundamentals of data science, data science techniques, and data science applications. It valuable resource for anyone who wants to learn how to use Python for data science.
Provides a comprehensive overview of Python for data analysis. It covers the fundamentals of Python, data manipulation techniques, and data visualization techniques. It valuable resource for anyone who wants to learn how to use Python for data analysis.
Save
Provides a gentle introduction to machine learning. It covers the fundamentals of machine learning, machine learning algorithms, and machine learning applications. It valuable resource for anyone who wants to learn about machine learning without getting bogged down in the technical details.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0xuj83/efficient
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Time
31 hours
Level
Introductory
Via
Pluralsight
Instructor
Dan Hermes
Language
English
Good to know
Explores data feeding and labeling, which is the core of training data preparation for machine learning models
Provides guidance on finalizing training data preparation and choosing the most appropriate manner to feed it into data model training
Taught by recognized industry expert Dan Hermes
Suitable for learners with a background in machine learning who seek to enhance their skills in data feeding and labeling for model training
May require additional research or knowledge of specific machine learning algorithms for practical implementation
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