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Building Unsupervised Learning Models with TensorFlow
Janani Ravi
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.
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.
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
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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
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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
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