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Ari Anastassiou
In this 1-hour long project, you will learn how to generate your own high-dimensional dummy dataset. You will then learn how to preprocess it effectively before training a baseline PCA model. You will learn the theory behind the autoencoder, and how to train one in scikit-learn. You will also learn how to extract the encoder portion of it to reduce dimensionality of your input data. In the course of this project, you will also be exposed to some basic clustering strength metrics.
Read more
In this 1-hour long project, you will learn how to generate your own high-dimensional dummy dataset. You will then learn how to preprocess it effectively before training a baseline PCA model. You will learn the theory behind the autoencoder, and how to train one in scikit-learn. You will also learn how to extract the encoder portion of it to reduce dimensionality of your input data. In the course of this project, you will also be exposed to some basic clustering strength metrics.
Read more
In this 1-hour long project, you will learn how to generate your own high-dimensional dummy dataset. You will then learn how to preprocess it effectively before training a baseline PCA model. You will learn the theory behind the autoencoder, and how to train one in scikit-learn. You will also learn how to extract the encoder portion of it to reduce dimensionality of your input data. In the course of this project, you will also be exposed to some basic clustering strength metrics.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/n4nho2/dimensionality
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What's inside
Syllabus
Dimensionality Reduction using an Autoencoder in Python
In this 1-hour long project, you will learn how to generate your own high-dimensional dummy dataset. You will then learn how to preprocess it effectively, before training a baseline PCA model. You will learn the theory behind the autoencoder, and how it is a nuanced, but unsupervised, neural network. You will learn how to train one in scikit-learn. You will also learn how to extract the encoder portion of this trained autoencoder to reduce dimensionality of your input data. In the course of this project, you will also be exposed to some basic clustering strength metrics to evaluate how well your autoencoder works.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in unsupervised learning that are essential for deep learning and neural network models
Introduces autoencoder models, which are powerful dimensionality reduction tool used in data science and machine learning
Real-world application by having learners generate their own high-dimensional dummy dataset
Step-by-step instructions on how to train and use autoencoders in real-world applications
Teaches the intuition and concepts behind autoencoders through hands-on coding exercises
Provides foundational knowledge in dimensionality reduction for beginners in machine learning
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Save Dimensionality Reduction using an Autoencoder in Python to your list so you can find it easily later:
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Reviews summary
Autoencoder techniques in python
Learners say this is a largely positive course on the basics of autoencoders in Python. Reviews indicate that this course is practical, engaging, and well-paced. Students report finding it easy to follow and clear. This course is well-received by students who enjoyed the format and recommend it.
Course is practical and has real-world applications.
"Very practical and useful introductory course."
"Nice project. Well explained, good duration. Main concept came across clearly."
Course is well-paced.
"Short and clear."
"Nice example and great explanation."
Course is engaging and interesting.
"I really enjoyed this course."
Course is clear and easy to follow.
"The time is too short, especially if you want to not just type in the desktop, but also take notes."
"I found it relaxing to be able to turn the work on the assignments and test at my leisure and when I had the time."
Course is well-received by students.
"I really enjoyed this class and the format it was presented in."
"I will definitely be taking another online course from you!"
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 Dimensionality Reduction using an Autoencoder in Python with these
activities:
Create a dummy dataset and preprocess it.
Show steps
Practice creating and preprocessing of dummy datasets from scratch to gain a deeper understanding of the concepts.
Browse courses on
Dimensionality Reduction
Show steps
-
Follow along the project instructions to generate a dummy dataset.
-
Implement data preprocessing techniques such as scaling and normalization.
Train a baseline PCA model on the preprocessed dataset.
Show steps
Apply PCA to reduce the dimensionality of the preprocessed dataset. This will help students understand how PCA works in practice.
Browse courses on
Dimensionality Reduction
Show steps
-
Follow along the project instructions to train a baseline PCA model.
-
Use scikit-learn's PCA class to fit the model to the preprocessed data.
Build an autoencoder model in scikit-learn for dimensionality reduction.
Show steps
Demonstrate the implementation of an autoencoder model for dimensionality reduction. This will help students understand the theory behind autoencoders and how to apply them in practice.
Browse courses on
Dimensionality Reduction
Show steps
-
Follow along the project instructions to build an autoencoder model.
-
Use scikit-learn's AutoEncoder class to train the autoencoder.
-
Use the encoder portion of the trained autoencoder to reduce the dimensionality of the input data.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Evaluate the autoencoder's performance using basic clustering strength metrics.
Show steps
Reinforce understanding of the autoencoder by evaluating its performance using clustering strength metrics.
Browse courses on
Dimensionality Reduction
Show steps
-
Follow along the project instructions to evaluate the autoencoder's performance.
-
Use metrics such as silhouette score and Davies-Bouldin index to measure the clustering strength.
Explore additional resources on dimensionality reduction using autoencoders.
Show steps
Extend understanding of the topic by exploring additional resources on dimensionality reduction and autoencoders.
Browse courses on
Dimensionality Reduction
Show steps
-
Search online for tutorials, articles, and videos on dimensionality reduction using autoencoders.
-
Follow along the tutorials and articles to gain a deeper understanding of the concepts.
Participate in a coding competition related to dimensionality reduction.
Show steps
Challenge students to apply their skills in a practical setting. This activity will help them to develop problem-solving skills and to test their understanding of the concepts.
Browse courses on
Dimensionality Reduction
Show steps
-
Find a coding competition related to dimensionality reduction.
-
Participate in the competition and apply the skills learned in the course.
Contribute to an open-source project related to dimensionality reduction.
Show steps
Provide students with an opportunity to make a practical contribution to the field of dimensionality reduction. This will help them to develop their skills in a real-world setting and to gain recognition for their work.
Browse courses on
Dimensionality Reduction
Show steps
-
Find an open-source project related to dimensionality reduction.
-
Contribute to the project by adding new features, fixing bugs, or improving documentation.
Create a dummy dataset and preprocess it.
Show steps
Practice creating and preprocessing of dummy datasets from scratch to gain a deeper understanding of the concepts.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Follow along the project instructions to generate a dummy dataset.
- Implement data preprocessing techniques such as scaling and normalization.
Train a baseline PCA model on the preprocessed dataset.
Show steps
Apply PCA to reduce the dimensionality of the preprocessed dataset. This will help students understand how PCA works in practice.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Follow along the project instructions to train a baseline PCA model.
- Use scikit-learn's PCA class to fit the model to the preprocessed data.
Build an autoencoder model in scikit-learn for dimensionality reduction.
Show steps
Demonstrate the implementation of an autoencoder model for dimensionality reduction. This will help students understand the theory behind autoencoders and how to apply them in practice.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Follow along the project instructions to build an autoencoder model.
- Use scikit-learn's AutoEncoder class to train the autoencoder.
- Use the encoder portion of the trained autoencoder to reduce the dimensionality of the input data.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Evaluate the autoencoder's performance using basic clustering strength metrics.
Show steps
Reinforce understanding of the autoencoder by evaluating its performance using clustering strength metrics.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Follow along the project instructions to evaluate the autoencoder's performance.
- Use metrics such as silhouette score and Davies-Bouldin index to measure the clustering strength.
Explore additional resources on dimensionality reduction using autoencoders.
Show steps
Extend understanding of the topic by exploring additional resources on dimensionality reduction and autoencoders.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Search online for tutorials, articles, and videos on dimensionality reduction using autoencoders.
- Follow along the tutorials and articles to gain a deeper understanding of the concepts.
Participate in a coding competition related to dimensionality reduction.
Show steps
Challenge students to apply their skills in a practical setting. This activity will help them to develop problem-solving skills and to test their understanding of the concepts.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Find a coding competition related to dimensionality reduction.
- Participate in the competition and apply the skills learned in the course.
Contribute to an open-source project related to dimensionality reduction.
Show steps
Provide students with an opportunity to make a practical contribution to the field of dimensionality reduction. This will help them to develop their skills in a real-world setting and to gain recognition for their work.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Find an open-source project related to dimensionality reduction.
- Contribute to the project by adding new features, fixing bugs, or improving documentation.
Career center
Learners who complete Dimensionality Reduction using an Autoencoder in Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use this information to make recommendations to businesses on how to improve their operations.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in data analysis to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Data Analysts who need to work with high-dimensional data.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They also develop and implement machine learning models to make predictions and recommendations.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in data science to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Data Scientists who need to work with high-dimensional data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers work on developing and maintaining machine learning systems, which can be used for tasks such as data analysis, predictive modeling, and natural language processing.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in machine learning to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Machine Learning Engineers who need to work with high-dimensional data.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including science, engineering, and medicine. They use their knowledge and skills to develop new technologies and solve complex problems.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in research to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Research Scientists who need to work with high-dimensional data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work on a variety of projects, from small personal apps to large enterprise systems.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in software engineering to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Software Engineers who need to work with high-dimensional data.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. They work with businesses to identify their needs and develop solutions that will help them achieve their goals.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in business analysis to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Business Analysts who need to work with high-dimensional data.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use their findings to make informed decisions about a variety of topics, such as public health, education, and business.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in statistics to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Statisticians who need to work with high-dimensional data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They work for hedge funds, investment banks, and other financial institutions.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in quantitative finance to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Quantitative Analysts who need to work with high-dimensional data.
Data Scientist (Computer Vision)
Data Scientist (Computer Vision)
Data Scientists (Computer Vision) use data to solve problems related to computer vision. They work on a variety of projects, such as developing image recognition, object detection, and video analysis.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in computer vision to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Data Scientists (Computer Vision) who need to work with high-dimensional data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They work for a variety of industries, including manufacturing, transportation, and healthcare.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in operations research to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Operations Research Analysts who need to work with high-dimensional data.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They work for hedge funds, investment banks, and other financial institutions.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in financial analysis to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Financial Analysts who need to work with high-dimensional data.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. They work for a variety of industries, including finance, insurance, and healthcare.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in risk analysis to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Risk Analysts who need to work with high-dimensional data.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work with data from a variety of sources, including databases, data warehouses, and cloud storage.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in data engineering to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Data Engineers who need to work with high-dimensional data.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work for a variety of industries, including academia, industry, and government.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in machine learning research to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Machine Learning Researchers who need to work with high-dimensional data.
Data Scientist (Natural Language Processing)
Data Scientist (Natural Language Processing)
Data Scientists (Natural Language Processing) use data to solve problems related to natural language. They work on a variety of projects, such as developing chatbots, machine translation, and text summarization.
This course can help you build a foundation in the principles of dimensionality reduction, which is a key technique used in natural language processing to reduce the dimensionality of data while preserving its most important features. This knowledge can be valuable for Data Scientists (Natural Language Processing) who need to work with high-dimensional data.
For more career information including salaries, visit:
OpenCourser.com/course/n4nho2/dimensionality
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
Dimensionality Reduction using an Autoencoder in Python .
Provides a comprehensive overview of deep learning, including a chapter on autoencoders. It is written in a clear and concise style, making it a valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of pattern recognition and machine learning, including a chapter on dimensionality reduction. It is written in a clear and concise style, making it a valuable resource for both beginners and experienced practitioners.
Provides a gentle introduction to machine learning, including a chapter on dimensionality reduction. It is written in a clear and concise style, making it a great resource for beginners.
Save
Provides a comprehensive overview of data mining, including a chapter on dimensionality reduction. It is written in a clear and concise style, making it a valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of machine learning, including a chapter on dimensionality reduction. It is written in a clear and concise style, making it a valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It includes a chapter on dimensionality reduction, making it a valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It includes a chapter on dimensionality reduction, making it a valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It includes a chapter on dimensionality reduction, making it a valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of kernel methods for machine learning. It includes a chapter on dimensionality reduction, making it a valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of autoencoders. It is written by one of the pioneers of the field, making it a valuable resource for anyone who wants to learn more about this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/n4nho2/dimensionality
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Effort
60 minutes
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ari Anastassiou
Language
English
Good to know
Develops skills in unsupervised learning that are essential for deep learning and neural network models
Introduces autoencoder models, which are powerful dimensionality reduction tool used in data science and machine learning
Real-world application by having learners generate their own high-dimensional dummy dataset
Step-by-step instructions on how to train and use autoencoders in real-world applications
Teaches the intuition and concepts behind autoencoders through hands-on coding exercises
Provides foundational knowledge in dimensionality reduction for beginners in machine learning
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