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Efficient Data Feeding and Labeling for Model Training

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.

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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.

This course is no longer available. Find something similar by browsing:
Data Feeding Data Labeling Machine Learning Supervised Learning Semi-Supervised Learning Data Model Training Data Preparation

What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
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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Reviews summary

Essential ml data preparation techniques

According to students, this course provides a strong foundational understanding of efficient data feeding and labeling for machine learning models. Learners particularly appreciate the practical insights into preparing training data and the clear explanations of complex concepts like supervised and semi-supervised learning. Many found the hands-on approach with labeling tools highly beneficial, making the skills immediately applicable to their projects. While largely positive, some learners noted that the course assumes basic ML knowledge, and a few suggested it could delve deeper into advanced topics or offer more updated tool examples, though recent feedback indicates continuous improvement.
Requires foundational understanding of machine learning.
"It's better if you have some basic machine learning background before taking this course."
"I felt a little lost at times, as it seemed to assume more ML knowledge than I had."
"While not explicitly stated, knowing the basics of model training is very helpful here."
Recent updates have improved relevance and addressed feedback.
"Noticed improvements in the course content, particularly regarding updated tools, since my first attempt."
"The recent revisions made the material even more current and valuable."
"It's great to see the instructor continuously refining the course based on student suggestions."
Explains complex data feeding and labeling concepts clearly.
"The instructor did an excellent job explaining the nuances of data feeding and labeling."
"I appreciated the clear breakdown of supervised vs. semi-supervised labeling."
"Concepts that seemed daunting before, like data augmentation, were made very understandable."
Delivers highly practical and directly applicable skills for ML.
"This course gave me practical insights into how data preparation is done in the real world."
"I found the techniques taught here immediately applicable to my ongoing machine learning projects."
"The hands-on examples helped me grasp the concepts and apply them effectively at work."
Offers solid foundation but could benefit from more advanced topics.
"The course provides a great overview, but I wished for more in-depth coverage on specific advanced labeling techniques."
"It builds a strong foundation, though some newer tools and optimization methods could be explored further."
"I felt it was a good starting point, but I'll need to seek additional resources for more specialized scenarios."

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
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
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
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
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
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
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.

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 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.
See salaries and explore the career path for Data Engineer
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Machine Learning Engineer
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.
See salaries and explore the career path for Data 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.
See salaries and explore the career path for Business Analyst
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.
See salaries and explore the career path for Project Manager
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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Data Architect
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.
See salaries and explore the career path for Database Administrator
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.
See salaries and explore the career path for Statistician
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.
See salaries and explore the career path for Operations Research 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.
See salaries and explore the career path for Financial Analyst
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.
See salaries and explore the career path for Market Researcher
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.
See salaries and explore the career path for Sales 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.
See salaries and explore the career path for Marketing Analyst

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.
Cover image
Probabilistic Machine Learning
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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.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
Reinforcement Learning, second edition
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Provides a comprehensive overview of reinforcement learning. It covers the fundamentals of reinforcement learning, reinforcement learning algorithms, and reinforcement learning applications. It valuable resource for anyone who wants to learn about reinforcement learning.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Artificial Intelligence
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Provides a comprehensive overview of Bayesian reasoning and machine learning. It covers the fundamentals of Bayesian reasoning, Bayesian machine learning algorithms, and Bayesian machine learning applications. It valuable resource for anyone who wants to learn about Bayesian reasoning and machine learning.
Artificial Intelligence: Foundations of...
Hardcover
$$$
Artificial Intelligence: Foundations of...
Hardcover
$$$
Artificial Intelligence: Foundations of...
Kindle Edition
$$$
Artificial Intelligence: Foundations of...
Kindle Edition
$$$
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The Elements of Statistical Learning
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Provides a comprehensive overview of statistical learning. It covers the fundamentals of statistical learning, statistical learning algorithms, and statistical learning applications. It valuable resource for anyone who wants to learn about statistical learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
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The Elements of Statistical Learning: Data Mining,...
Kindle Edition
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Data Science for Business
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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.
Data Science for Business: What You Need to Know...
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Data Science for Business: What You Need to Know...
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Deep Learning
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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.
Deep Learning
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Deep Learning (Adaptive Computation and Machine...
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Deep Learning (Adaptive Computation and Machine...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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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.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
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Deep Learning with Python, Second Edition
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Provides a practical introduction to deep learning with Python. It covers the fundamentals of deep learning, deep learning architectures, and deep learning applications. It valuable resource for anyone who wants to learn how to use Python for deep learning.
Deep Learning with Python, Second Edition
Paperback
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Deep Learning with Python, Second Edition
Kindle Edition
$$
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Python for Data Analysis
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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.
Python for Data Analysis: Data Wrangling with...
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Python for Data Analysis
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Python for Data Analysis: Data Wrangling with...
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Data Science from Scratch
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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.
Data Science from Scratch: First Principles with...
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Data Science from Scratch: First Principles with...
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Data Science from Scratch: First Principles with...
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How to Speak Machine
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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.
How to Speak Machine: Computational Thinking for...
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How to Speak Machine: Computational Thinking for...
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Course image
Time
31 hours
Level
Introductory
Via
Pluralsight
Instructor
Dan Hermes
Language
English
Traffic lights
Read about what's good
what should give you pause
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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