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Amber Israelsen
Are you facing the challenge of ever-changing data when it comes to machine learning? This course will teach you how to continuously train and adapt your models, ensuring long-term effectiveness.
In the fast-paced world of data science, keeping your machine learning models up-to-date and relevant is a never-ending job. The data never stays the same for long!
In this course, Continuous Model Training with Evolving Data Streams, you’ll gain the ability to maintain accurate models, no matter how much the data changes.
Read more
Are you facing the challenge of ever-changing data when it comes to machine learning? This course will teach you how to continuously train and adapt your models, ensuring long-term effectiveness.
In the fast-paced world of data science, keeping your machine learning models up-to-date and relevant is a never-ending job. The data never stays the same for long!
In this course, Continuous Model Training with Evolving Data Streams, you’ll gain the ability to maintain accurate models, no matter how much the data changes.
Read more
Are you facing the challenge of ever-changing data when it comes to machine learning? This course will teach you how to continuously train and adapt your models, ensuring long-term effectiveness.
In the fast-paced world of data science, keeping your machine learning models up-to-date and relevant is a never-ending job. The data never stays the same for long!
In this course, Continuous Model Training with Evolving Data Streams, you’ll gain the ability to maintain accurate models, no matter how much the data changes.
First, you’ll explore why continuous training is so important, delving into topics like concept drift and data drift.
Next, you’ll discover various strategies for the continuous adaptation of models, including batch learning and incremental training techniques, to help your models evolve as new data arrives.
Finally, you’ll explore model retraining frameworks, employing automated pipelines and feedback loops to integrate real-world insights into ongoing model adjustments.
When you’re finished with this course, you’ll have the skills and knowledge of continuous training needed to keep your machine learning models at peak performance, adapting to new data.
Register for this course and see more details by visiting:
OpenCourser.com/course/4x9cv6/continuous
What's inside
Syllabus
Course Overview
Why Continuous Model Training?
Frameworks, Evaluation, and Feedback
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops a strong foundation in continuous model training, particularly for learners who are new to deploying models in the field
Hands-on labs and interactive materials support the learning process effectively
Teaches strategies for adapting models incrementally, which is important for real-time learning systems
Provides a comprehensive overview of model retraining frameworks, which are essential for operationalizing continuous training
Utilizes a mix of videos, readings, discussions, and interactive materials to cater to diverse learning styles
Taught by an experienced instructor who is recognized for their expertise in machine learning
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Save Continuous Model Training with Evolving Data Streams to your list so you can find it easily later:
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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 Continuous Model Training with Evolving Data Streams with these
activities:
Practice data preparation and cleaning
Show steps
Data preparation and cleaning are essential skills for machine learning. Practice these skills to ensure you are ready for this course.
Browse courses on
Data Preparation
Show steps
-
Find a dataset that you are interested in.
-
Load the dataset into a programming environment.
-
Explore the dataset to identify any missing or corrupted data.
Review regression analysis concepts
Show steps
Refresh your understanding of regression analysis. Make sure you are comfortable with the dataset for this course.
Browse courses on
Regression Analysis
Show steps
-
Read through your notes from your last course on regression analysis.
-
Work through some practice problems on regression analysis.
-
Review the dataset that you will be using for this course.
Form a study group with classmates
Show steps
Working with peers can help improve your learning. Form a study group to discuss the concepts and work through the assignments.
Show steps
-
Introduce yourself to your classmates on the discussion board.
-
Find a few classmates who are interested in forming a study group.
-
Decide on a meeting time and place.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Explore frameworks for continuous training
Show steps
Familiarizing yourself with the latest frameworks will enhance your ability to implement continuous training solutions.
Show steps
-
Review popular continuous training frameworks
-
Identify the key features and benefits of each framework
-
Select a framework that aligns with your project requirements
-
Follow tutorials or documentation to get started with the framework
Practice implementing batch and incremental training algorithms
Show steps
Regularly practicing the implementation of various training algorithms will help you develop proficiency.
Show steps
-
Identify a dataset appropriate for batch and incremental training
-
Experiment with different batch sizes and training epochs
-
Implement incremental training using mini-batches
-
Test and evaluate the performance of the trained models
Watch tutorials on machine learning algorithms
Show steps
There are many different machine learning algorithms. Watch tutorials to learn about the different algorithms and how to use them.
Browse courses on
Machine Learning Algorithms
Show steps
-
Find a few tutorials on machine learning algorithms.
-
Watch the tutorials and take notes.
-
Try out the algorithms on your own dataset.
Build a machine learning model
Show steps
The goal of this course is to build a machine learning model. Start working on your model early so that you have time to iterate and improve it.
Browse courses on
Machine Learning Model
Show steps
-
Choose a machine learning algorithm.
-
Train the model on your dataset.
-
Evaluate the model's performance.
Develop a continuous training pipeline
Show steps
Building a continuous training pipeline will provide hands-on experience in setting up and managing a production-grade system.
Browse courses on
Machine Learning Pipeline
Show steps
-
Design the architecture of the pipeline, including data ingestion, training, and deployment
-
Implement the pipeline using cloud services or open-source tools
-
Integrate automated data collection and model evaluation
-
Monitor and maintain the pipeline to ensure it operates seamlessly
Mentor a new student
Show steps
Mentoring others can help you solidify your understanding of the material. Volunteer to mentor a new student in this course.
Show steps
-
Reach out to the instructor or TAs and offer to mentor a new student.
-
Meet with your mentee regularly to discuss the course material and answer any questions.
Contribute to an open-source machine learning project
Show steps
Contributing to open-source projects is a great way to learn about machine learning and give back to the community. Find a project that you are interested in and start contributing.
Show steps
-
Find an open-source machine learning project that you are interested in.
-
Read the project's documentation and contribute code.
-
Submit a pull request to the project.
Practice data preparation and cleaning
Show steps
Data preparation and cleaning are essential skills for machine learning. Practice these skills to ensure you are ready for this course.
Browse courses on
Data Preparation
Show steps
Show steps
Show steps
- Find a dataset that you are interested in.
- Load the dataset into a programming environment.
- Explore the dataset to identify any missing or corrupted data.
Review regression analysis concepts
Show steps
Refresh your understanding of regression analysis. Make sure you are comfortable with the dataset for this course.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Read through your notes from your last course on regression analysis.
- Work through some practice problems on regression analysis.
- Review the dataset that you will be using for this course.
Form a study group with classmates
Show steps
Working with peers can help improve your learning. Form a study group to discuss the concepts and work through the assignments.
Show steps
Show steps
Show steps
- Introduce yourself to your classmates on the discussion board.
- Find a few classmates who are interested in forming a study group.
- Decide on a meeting time and place.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Explore frameworks for continuous training
Show steps
Familiarizing yourself with the latest frameworks will enhance your ability to implement continuous training solutions.
Show steps
Show steps
Show steps
- Review popular continuous training frameworks
- Identify the key features and benefits of each framework
- Select a framework that aligns with your project requirements
- Follow tutorials or documentation to get started with the framework
Practice implementing batch and incremental training algorithms
Show steps
Regularly practicing the implementation of various training algorithms will help you develop proficiency.
Show steps
Show steps
Show steps
- Identify a dataset appropriate for batch and incremental training
- Experiment with different batch sizes and training epochs
- Implement incremental training using mini-batches
- Test and evaluate the performance of the trained models
Watch tutorials on machine learning algorithms
Show steps
There are many different machine learning algorithms. Watch tutorials to learn about the different algorithms and how to use them.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Find a few tutorials on machine learning algorithms.
- Watch the tutorials and take notes.
- Try out the algorithms on your own dataset.
Build a machine learning model
Show steps
The goal of this course is to build a machine learning model. Start working on your model early so that you have time to iterate and improve it.
Browse courses on
Machine Learning Model
Show steps
Show steps
Show steps
- Choose a machine learning algorithm.
- Train the model on your dataset.
- Evaluate the model's performance.
Develop a continuous training pipeline
Show steps
Building a continuous training pipeline will provide hands-on experience in setting up and managing a production-grade system.
Browse courses on
Machine Learning Pipeline
Show steps
Show steps
Show steps
- Design the architecture of the pipeline, including data ingestion, training, and deployment
- Implement the pipeline using cloud services or open-source tools
- Integrate automated data collection and model evaluation
- Monitor and maintain the pipeline to ensure it operates seamlessly
Mentor a new student
Show steps
Mentoring others can help you solidify your understanding of the material. Volunteer to mentor a new student in this course.
Show steps
Show steps
Show steps
- Reach out to the instructor or TAs and offer to mentor a new student.
- Meet with your mentee regularly to discuss the course material and answer any questions.
Contribute to an open-source machine learning project
Show steps
Contributing to open-source projects is a great way to learn about machine learning and give back to the community. Find a project that you are interested in and start contributing.
Show steps
Show steps
Show steps
- Find an open-source machine learning project that you are interested in.
- Read the project's documentation and contribute code.
- Submit a pull request to the project.
Career center
Learners who complete Continuous Model Training with Evolving Data Streams will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of data analysis and machine learning to extract insights from data. They use these insights to solve business problems and make better decisions. This course will help Data Scientists build a foundation in continuous model training, which is essential for keeping models up-to-date and relevant as data changes. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use their knowledge of machine learning algorithms and techniques to solve real-world problems. This course will help Machine Learning Engineers build a foundation in continuous model training, which is essential for keeping models up-to-date and relevant as data changes. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their knowledge of programming languages and software development techniques to create software that meets the needs of users. This course will help Software Engineers build a foundation in continuous model training, which is becoming increasingly important as more and more software systems use machine learning. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data analysis and visualization to communicate insights from data to stakeholders. They use these insights to help stakeholders make better decisions. This course will help Data Analysts build a foundation in continuous model training, which is essential for keeping models up-to-date and relevant as data changes. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business processes and data analysis to identify and solve business problems. They use their findings to make recommendations to stakeholders. This course will help Business Analysts build a foundation in continuous model training, which can help them to identify and solve business problems more effectively. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. They use their knowledge of market research, user experience, and data analysis to create products that meet the needs of users. This course will help Product Managers build a foundation in continuous model training, which can help them to create better products. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Project Manager
Project Manager
Project Managers are responsible for the planning, execution, and delivery of projects. They use their knowledge of project management techniques and tools to ensure that projects are completed on time, within budget, and to the satisfaction of stakeholders. This course will help Project Managers build a foundation in continuous model training, which can help them to manage projects more effectively. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics, statistics, and computer science to solve business problems. They use their findings to make recommendations to stakeholders. This course will help Operations Research Analysts build a foundation in continuous model training, which can help them to solve business problems more effectively. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Statistician
Statistician
Statisticians use their knowledge of statistics to collect, analyze, and interpret data. They use their findings to make recommendations to stakeholders. This course will help Statisticians build a foundation in continuous model training, which can help them to collect, analyze, and interpret data more effectively. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of financial analysis to make investment recommendations. They use their findings to help investors make better investment decisions. This course will help Financial Analysts build a foundation in continuous model training, which can help them to make better investment decisions. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Risk Analyst
Risk Analyst
Risk Analysts use their knowledge of risk management to identify and assess risks. They use their findings to make recommendations to stakeholders. This course will help Risk Analysts build a foundation in continuous model training, which can help them to identify and assess risks more effectively. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and computer science to analyze financial data. They use their findings to make investment recommendations. This course will help Quantitative Analysts build a foundation in continuous model training, which can help them to make better investment decisions. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Machine Learning Specialist
Machine Learning Specialist
Machine Learning Specialists use their knowledge of machine learning to develop and deploy machine learning models. They use their findings to solve business problems and make better decisions. This course will help Machine Learning Specialists build a foundation in continuous model training, which is essential for keeping models up-to-date and relevant as data changes. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop artificial intelligence systems. They use their knowledge of artificial intelligence to create systems that can learn and adapt. This course will help Artificial Intelligence Engineers build a foundation in continuous model training, which is essential for keeping models up-to-date and relevant as data changes. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
Data Architect
Data Architect
Data Architects design and build data systems. They use their knowledge of data architecture to create systems that are scalable, reliable, and secure. This course will help Data Architects build a foundation in continuous model training, which can help them to create better data systems. It will teach them how to continuously train and adapt their models, ensuring long-term effectiveness.
For more career information including salaries, visit:
OpenCourser.com/course/4x9cv6/continuous
Reading list
We've selected 12 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
Continuous Model Training with Evolving Data Streams.
Save
Presents a comprehensive overview of deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks, with a focus on theoretical foundations and practical applications.
Save
Serves as a foundational reference on the theory and practice of data streams, presenting core algorithms for processing and analyzing data streams, covering topics such as filtering, sampling, counting, sketching, and clustering.
Presents a comprehensive overview of machine learning from a probabilistic perspective, covering topics such as Bayesian inference, graphical models, and deep learning, with a focus on theoretical foundations and mathematical rigor.
Presents a practical introduction to Python for data analysis, covering topics such as data manipulation, data visualization, and machine learning, with a focus on real-world applications and case studies.
Presents a non-technical overview of data science, covering topics such as data mining, data analysis, and data visualization, with a focus on business applications and case studies.
Save
Provides a practical introduction to data visualization, covering topics such as data visualization principles, techniques, and tools, with a focus on effective communication and storytelling.
Provides a good starting point for those completely new to the topic of machine learning.
Bridges the gap between academic machine learning and tools used by industry machine learning practitioners.
Will provide some essential foundational knowledge on deep learning, which fundamental algorithm used in many modern machine learning workflows.
Is an excellent general-purpose resource on machine learning with Python. It can provide additional insight into the general topics of machine learning, as well as Python's implementation of those topics.
Provides a valuable insight into how machine learning models are used in finance.
Provides a comprehensive overview of natural language processing (NLP) with Python libraries and frameworks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4x9cv6/continuous
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Time
29 hours
Level
Introductory
Via
Pluralsight
Instructor
Amber Israelsen
Language
English
Good to know
Develops a strong foundation in continuous model training, particularly for learners who are new to deploying models in the field
Hands-on labs and interactive materials support the learning process effectively
Teaches strategies for adapting models incrementally, which is important for real-time learning systems
Provides a comprehensive overview of model retraining frameworks, which are essential for operationalizing continuous training
Utilizes a mix of videos, readings, discussions, and interactive materials to cater to diverse learning styles
Taught by an experienced instructor who is recognized for their expertise in machine learning
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