We may earn an affiliate commission when you visit our partners.
Janani Ravi
Unsupervised learning techniques work with huge data sets to find patterns within the data. This course teaches you the details of clustering and autoencoding, two versatile unsupervised learning techniques, and how to implement them in TensorFlow.
Unsupervised learning techniques are powerful, but under utilized and often not well understood. In this course,
, you'll learn the various characteristics and features of clustering models such as K-means clustering and hierarchical clustering.
, you'll dive into building a k-means clustering model in TensorFlow.
Read more
Unsupervised learning techniques work with huge data sets to find patterns within the data. This course teaches you the details of clustering and autoencoding, two versatile unsupervised learning techniques, and how to implement them in TensorFlow.
Unsupervised learning techniques are powerful, but under utilized and often not well understood. In this course,
, you'll learn the various characteristics and features of clustering models such as K-means clustering and hierarchical clustering.
, you'll dive into building a k-means clustering model in TensorFlow.
Read more
Unsupervised learning techniques work with huge data sets to find patterns within the data. This course teaches you the details of clustering and autoencoding, two versatile unsupervised learning techniques, and how to implement them in TensorFlow.
Unsupervised learning techniques are powerful, but under utilized and often not well understood. In this course,
, you'll learn the various characteristics and features of clustering models such as K-means clustering and hierarchical clustering.
, you'll dive into building a k-means clustering model in TensorFlow.
, you'll discover autoencoders in detail, which are a type of artificial neural network used for unsupervised learning.
, you'll explore encodings or representation of data for dimensionality reduction of problems.
By the end of this course, you'll have a better understanding of how you can work with unlabeled data using unsupervised learning techniques.
Register for this course and see more details by visiting:
OpenCourser.com/course/gyh911/building
What's inside
Syllabus
Course Overview
Introduction to Unsupervised Learning
Clustering Using Unsupervised Learning
Understanding Neurons and Neural Networks
Read more
Syllabus
Course Overview
Introduction to Unsupervised Learning
Clustering Using Unsupervised Learning
Understanding Neurons and Neural Networks
Read more
Autoencoders Using Unsupervised Learning
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches a foundational knowledge of k-means clustering and hierarchical clustering
Explores various characteristics and features of clustering models
Introduces the concept of autoencoders, which are growing in popularity in industry
Uses TensorFlow, a popular framework in industry, to implement unsupervised learning techniques
Shows learners how to use unsupervised learning to work with unlabeled data
Builds a strong foundation in unsupervised learning for beginners, making it accessible to a wider audience
Save this course
Save Building Unsupervised Learning Models with TensorFlow to your list so you can find it easily later:
Save
Save
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 Building Unsupervised Learning Models with TensorFlow with these
activities:
Review Clustering Algorithms
Show steps
Build a solid foundation in clustering algorithms to enhance your understanding of unsupervised learning techniques.
Browse courses on
Clustering
Show steps
-
Revisit the fundamentals of clustering algorithms, such as k-means and hierarchical clustering.
-
Explore real-world applications of clustering algorithms in various domains.
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Gain a comprehensive understanding of deep learning, including unsupervised learning techniques, by reading this authoritative book.
View
Deep Learning
on Amazon
Show steps
-
Acquire the book and set aside dedicated time for reading.
-
Read each chapter thoroughly, taking notes and highlighting key concepts.
-
Complete the exercises and assignments provided in the book.
Implement Clustering Algorithms in Python
Show steps
Gain hands-on experience in implementing clustering algorithms using TensorFlow to solidify your understanding.
Browse courses on
Clustering
Show steps
-
Set up a Python environment with TensorFlow and the necessary libraries.
-
Implement the k-means clustering algorithm from scratch.
-
Apply the implemented algorithm to real-world datasets.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a Study Group on Autoencoders
Show steps
Engage with peers to discuss and explore the concepts and applications of autoencoders, deepening your understanding.
Browse courses on
Autoencoders
Show steps
-
Join or create a study group focused on autoencoders.
-
Participate in regular discussions and knowledge-sharing sessions.
Develop an Unsupervised Learning Model for Anomaly Detection
Show steps
Apply your learning by building a practical unsupervised learning model to detect anomalies in data, enhancing your understanding of data patterns and model implementation.
Browse courses on
Unsupervised Learning
Show steps
-
Define the problem statement and gather the necessary dataset.
-
Choose and implement an unsupervised learning algorithm.
-
Evaluate the model's performance and make necessary adjustments.
Build an Interactive Data Visualization Tool for Clustering Results
Show steps
Create an interactive tool to visualize and explore clustering results, enhancing your understanding of data patterns.
Browse courses on
Clustering
Show steps
-
Design the user interface and functionality of the tool.
-
Integrate the TensorFlow model with the visualization tool.
-
Deploy the tool for use and gather feedback.
Explore Advanced Topics in Unsupervised Learning
Show steps
Expand your knowledge beyond the course by exploring advanced unsupervised learning techniques to gain a deeper perspective.
Browse courses on
Unsupervised Learning
Show steps
-
Identify online tutorials or courses on advanced unsupervised learning topics.
-
Follow the tutorials, complete exercises, and engage in discussions.
Participate in a Machine Learning Competition on Unsupervised Learning
Show steps
Challenge yourself and apply your skills in a competitive setting, enhancing your problem-solving abilities and fostering a deeper understanding of unsupervised learning.
Browse courses on
Unsupervised Learning
Show steps
-
Identify and register for a machine learning competition focused on unsupervised learning.
-
Study the competition guidelines and dataset.
-
Develop and submit your unsupervised learning model.
Review Clustering Algorithms
Show steps
Build a solid foundation in clustering algorithms to enhance your understanding of unsupervised learning techniques.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Revisit the fundamentals of clustering algorithms, such as k-means and hierarchical clustering.
- Explore real-world applications of clustering algorithms in various domains.
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Gain a comprehensive understanding of deep learning, including unsupervised learning techniques, by reading this authoritative book.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Acquire the book and set aside dedicated time for reading.
- Read each chapter thoroughly, taking notes and highlighting key concepts.
- Complete the exercises and assignments provided in the book.
Implement Clustering Algorithms in Python
Show steps
Gain hands-on experience in implementing clustering algorithms using TensorFlow to solidify your understanding.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Set up a Python environment with TensorFlow and the necessary libraries.
- Implement the k-means clustering algorithm from scratch.
- Apply the implemented algorithm to real-world datasets.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a Study Group on Autoencoders
Show steps
Engage with peers to discuss and explore the concepts and applications of autoencoders, deepening your understanding.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Join or create a study group focused on autoencoders.
- Participate in regular discussions and knowledge-sharing sessions.
Develop an Unsupervised Learning Model for Anomaly Detection
Show steps
Apply your learning by building a practical unsupervised learning model to detect anomalies in data, enhancing your understanding of data patterns and model implementation.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Define the problem statement and gather the necessary dataset.
- Choose and implement an unsupervised learning algorithm.
- Evaluate the model's performance and make necessary adjustments.
Build an Interactive Data Visualization Tool for Clustering Results
Show steps
Create an interactive tool to visualize and explore clustering results, enhancing your understanding of data patterns.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Design the user interface and functionality of the tool.
- Integrate the TensorFlow model with the visualization tool.
- Deploy the tool for use and gather feedback.
Explore Advanced Topics in Unsupervised Learning
Show steps
Expand your knowledge beyond the course by exploring advanced unsupervised learning techniques to gain a deeper perspective.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Identify online tutorials or courses on advanced unsupervised learning topics.
- Follow the tutorials, complete exercises, and engage in discussions.
Participate in a Machine Learning Competition on Unsupervised Learning
Show steps
Challenge yourself and apply your skills in a competitive setting, enhancing your problem-solving abilities and fostering a deeper understanding of unsupervised learning.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Identify and register for a machine learning competition focused on unsupervised learning.
- Study the competition guidelines and dataset.
- Develop and submit your unsupervised learning model.
Career center
Learners who complete Building Unsupervised Learning Models with TensorFlow will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists apply scientific methods to extract value from data. They use unsupervised learning techniques such as clustering and autoencoding to find patterns within large datasets. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Data Scientists build their skills.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models to solve real-world problems. They use unsupervised learning techniques to train models on unlabeled data. The Building Unsupervised Learning Models with TensorFlow course can help aspiring Machine Learning Engineers develop the skills they need to succeed in this field.
Data Analyst
Data Analyst
Data Analysts use data to help businesses make better decisions. They use unsupervised learning techniques to identify trends and patterns in data. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Data Analysts build their skills.
Business Analyst
Business Analyst
Business Analysts use data to help businesses improve their operations. They use unsupervised learning techniques to identify inefficiencies and opportunities for improvement. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Business Analysts build their skills.
Statistician
Statistician
Statisticians use data to make inferences about the world. They use unsupervised learning techniques to identify patterns and trends in data. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Statisticians build their skills.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use unsupervised learning techniques to improve the performance and efficiency of software systems. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Software Engineers build their skills.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines. They use unsupervised learning techniques to identify patterns and trends in data and to design pipelines that can efficiently move data from one system to another. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Data Engineers build their skills.
Cloud Architect
Cloud Architect
Cloud Architects design and build cloud-based systems. They use unsupervised learning techniques to identify patterns and trends in data and to design systems that can efficiently store and process data in the cloud. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Cloud Architects build their skills.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. They use unsupervised learning techniques to identify trends and patterns in financial data. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Quantitative Analysts build their skills.
Actuary
Actuary
Actuaries use data to assess and manage risk. They use unsupervised learning techniques to identify patterns and trends in data. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Actuaries build their skills.
Computer Scientist
Computer Scientist
Computer Scientists conduct research in the field of computer science. They use unsupervised learning techniques to develop new algorithms and techniques for solving problems. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Computer Scientists build their skills.
DevOps Engineer
DevOps Engineer
DevOps Engineers build and maintain software systems. They use unsupervised learning techniques to identify patterns and trends in data and to design systems that can efficiently store and process data. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring DevOps Engineers build their skills.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency and effectiveness of operations. They use unsupervised learning techniques to identify bottlenecks and opportunities for improvement. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Operations Research Analysts build their skills.
Software Architect
Software Architect
Software Architects design and build software systems. They use unsupervised learning techniques to identify patterns and trends in data and to design systems that can efficiently store and process data. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Software Architects build their skills.
Data Architect
Data Architect
Data Architects design and build data systems. They use unsupervised learning techniques to identify patterns and trends in data and to design systems that can efficiently store and process data. The Building Unsupervised Learning Models with TensorFlow course can provide a foundation in these techniques and help aspiring Data Architects build their skills.
For more career information including salaries, visit:
OpenCourser.com/course/gyh911/building
Reading list
We've selected nine 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
Building Unsupervised Learning Models with TensorFlow.
Provides a comprehensive overview of deep learning, including neural networks and autoencoders. It valuable resource for those who want to gain a deeper understanding of the theoretical and practical aspects of unsupervised learning.
Provides a comprehensive overview of clustering algorithms, including k-means and hierarchical clustering. It valuable resource for those who want to learn about the theoretical and practical aspects of clustering.
Provides a comprehensive overview of autoencoders. It valuable resource for those who want to learn about the theoretical and practical aspects of autoencoders.
Provides a comprehensive overview of TensorFlow, including how to use TensorFlow for unsupervised learning. It valuable resource for those who want to learn how to use TensorFlow for unsupervised learning.
Provides a comprehensive overview of machine learning, including unsupervised learning. It valuable resource for those who want to learn how to use scikit-learn, Keras, and TensorFlow for unsupervised learning.
Provides a comprehensive overview of deep learning, including how to use Keras for unsupervised learning. It valuable resource for those who want to learn how to use Keras for unsupervised learning.
Provides a comprehensive overview of machine learning, including unsupervised learning. It valuable resource for those who want to learn how to use R for unsupervised learning.
Save
Provides a comprehensive overview of data mining, including unsupervised learning. It valuable resource for those who want to learn how to use R for unsupervised learning.
Provides a comprehensive overview of machine learning, including unsupervised learning. It valuable resource for those who want to learn how to use Python for unsupervised learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gyh911/building
Share
Similar courses
Here are nine courses similar to
Building Unsupervised Learning Models with TensorFlow.
Building Clustering Models with scikit-learn
OpenCourser.com/course/tb255z/building
Building Clustering Models with scikit-learn
Most relevant
Implementing Machine Learning Workflow with Weka
OpenCourser.com/course/dtbh1p/implementing
Implementing Machine Learning Workflow with Weka
Most relevant
Data Science in Python: Unsupervised Learning
OpenCourser.com/course/yzrest/data
Data Science in Python: Unsupervised Learning
Most relevant
Machine Learning with Python
OpenCourser.com/course/nv15cy/machine
Machine Learning with Python
Most relevant
Image Compression with K-Means Clustering
OpenCourser.com/course/id77yf/image
Image Compression with K-Means Clustering
Most relevant
Machine Learning: Clustering & Retrieval
OpenCourser.com/course/a60amb/machine
Machine Learning: Clustering & Retrieval
Most relevant
The Nuts and Bolts of Machine Learning
OpenCourser.com/course/jdxlbe/the
The Nuts and Bolts of Machine Learning
Most relevant
Introduction to Customer Segmentation in Python
OpenCourser.com/course/ovxznh/introduction
Introduction to Customer Segmentation in Python
Most relevant
Mathematical Methods for Data Analysis
OpenCourser.com/course/n1rqys/mathematical
Mathematical Methods for Data Analysis
Most relevant
Time
182 hours
Level
Intermediate
Via
Pluralsight
Instructor
Janani Ravi
Language
English
This course belongs to a
collection
called
Building Machine Learning Solutions with Tensorflow. It's comprised of 11 courses:
- TensorFlow 1: Getting Started
- Understanding the Foundations of TensorFlow
- Building Regression Models Using TensorFlow 1
- Building Classification Models with TensorFlow
- Building Unsupervised Learning Models with TensorFlow (viewing)
- Debugging and Monitoring TensorFlow Programs
- Deploying TensorFlow Models to AWS, Azure, and the GCP
- Language Modeling with Recurrent Neural Networks in TensorFlow
- Implementing Image Recognition Systems with TensorFlow 1
- Implementing Predictive Analytics with TensorFlow
- Sentiment Analysis with Recurrent Neural Networks in TensorFlow
Good to know
Teaches a foundational knowledge of k-means clustering and hierarchical clustering
Explores various characteristics and features of clustering models
Introduces the concept of autoencoders, which are growing in popularity in industry
Uses TensorFlow, a popular framework in industry, to implement unsupervised learning techniques
Shows learners how to use unsupervised learning to work with unlabeled data
Builds a strong foundation in unsupervised learning for beginners, making it accessible to a wider audience
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser