We may earn an affiliate commission when you visit our partners.
Unsupervised Learning, Recommenders, Reinforcement Learning
Andrew Ng,
Eddy Shyu,
Aarti Bagul,
and
Geoff Ladwig
Andrew Ng,
Eddy Shyu,
Aarti Bagul,
and
Geoff Ladwig
In the third course of the Machine Learning Specialization, you will:
• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
Read more
In the third course of the Machine Learning Specialization, you will:
• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
Read more
In the third course of the Machine Learning Specialization, you will:
• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
• Build a deep reinforcement learning model.
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
Register for this course and see more details by visiting:
OpenCourser.com/course/mcyn60/unsupervised
What's inside
Syllabus
Unsupervised learning
This week, you will learn two key unsupervised learning algorithms: clustering and anomaly detection
Recommender systems
Read more
Syllabus
Unsupervised learning
This week, you will learn two key unsupervised learning algorithms: clustering and anomaly detection
Recommender systems
Read more
Reinforcement learning
This week, you will learn about reinforcement learning, and build a deep Q-learning neural network in order to land a virtual lunar lander on Mars!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches how clustering and deep learning approaches enhance recommender systems
Taught by instructors who are recognized for their work in the AI and machine learning field
Covers real-world AI applications, fostering practical implementation skills
Builds a solid foundation for beginners in machine learning
Requires learners to come in with some background knowledge in machine learning
Save this course
Save Unsupervised Learning, Recommenders, Reinforcement Learning to your list so you can find it easily later:
Save
Save
Career center
Learners who complete Unsupervised Learning, Recommenders, Reinforcement Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop, deploy, and maintain machine learning models. They work closely with data scientists to ensure that models are accurate and efficient. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are essential for building and deploying machine learning models in a variety of applications.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are essential for Data Scientists who want to build and deploy data-driven solutions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used in software applications to improve performance and efficiency.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Quantitative Analysts to develop new trading strategies and risk management models.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Business Analysts to develop new products and services, and to improve customer satisfaction.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Product Managers to develop new products that are tailored to the needs of customers.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Marketing Managers to target customers more effectively and to improve campaign performance.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and managing sales teams. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Sales Managers to improve sales performance and to close more deals.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Operations Managers to improve efficiency and productivity.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for the management of human resources within an organization. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Human Resources Managers to improve recruiting, hiring, and employee retention.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial markets and make investment recommendations. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Financial Analysts to develop new investment strategies and to improve risk management models.
Consultant
Consultant
Consultants provide advice and guidance to organizations on a variety of topics. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Consultants to develop new solutions to business problems and to improve organizational performance.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Project Managers to improve project efficiency and to deliver successful outcomes.
Teacher
Teacher
Teachers develop and deliver lesson plans to students. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Teachers to personalize learning experiences and to improve student outcomes.
Writer
Writer
Writers create content for a variety of purposes, including marketing, journalism, and entertainment. This course provides a strong foundation in machine learning techniques, including unsupervised learning, recommender systems, and reinforcement learning. These techniques are increasingly being used by Writers to generate new ideas, to improve writing quality, and to reach a wider audience.
For more career information including salaries, visit:
OpenCourser.com/course/mcyn60/unsupervised
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
Unsupervised Learning, Recommenders, Reinforcement Learning.
Save
Comprehensive guide to deep learning. It covers a wide range of topics, from the basics of neural networks to advanced techniques such as generative adversarial networks. It valuable resource for anyone who wants to learn more about deep learning.
Classic introduction to reinforcement learning. It covers the basics of reinforcement learning, as well as more advanced topics such as deep reinforcement learning. It valuable resource for anyone who wants to learn more about reinforcement learning.
Covers the basics of unsupervised learning. It valuable resource for anyone who wants to learn more about unsupervised learning.
Practical guide to machine learning. It covers a wide range of topics, from the basics of machine learning to advanced techniques such as deep learning. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to machine learning using Python. It covers a wide range of topics, from the basics of machine learning to advanced techniques such as deep learning. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to machine learning using R. It covers a wide range of topics, from the basics of machine learning to advanced techniques such as deep learning. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to deep learning using Python. It covers a wide range of topics, from the basics of deep learning to advanced techniques such as generative adversarial networks. It valuable resource for anyone who wants to learn more about deep learning.
Practical guide to unsupervised learning using Python. It covers a wide range of topics, from the basics of unsupervised learning to advanced techniques such as deep learning. It valuable resource for anyone who wants to learn more about unsupervised learning.
Save
Practical guide to machine learning for business. It covers a wide range of topics, from the basics of machine learning to advanced techniques such as deep learning. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to machine learning for finance. It covers a wide range of topics, from the basics of machine learning to advanced techniques such as deep learning. It valuable resource for anyone who wants to learn more about machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mcyn60/unsupervised
Share
Similar courses
Here are nine courses similar to
Unsupervised Learning, Recommenders, Reinforcement Learning.
Supervised Machine Learning: Regression and Classification
OpenCourser.com/course/u54csk/supervised
Supervised Machine Learning: Regression and...
Most relevant
Advanced Learning Algorithms
OpenCourser.com/course/g1jq38/advanced
Advanced Learning Algorithms
Most relevant
Deep Learning and Reinforcement Learning
OpenCourser.com/course/ppm31d/deep
Deep Learning and Reinforcement Learning
Most relevant
Machine Learning
OpenCourser.com/course/4x55uk/machine
Machine Learning
Most relevant
Implementing Machine Learning Workflow with Weka
OpenCourser.com/course/dtbh1p/implementing
Implementing Machine Learning Workflow with Weka
Most relevant
Unsupervised Algorithms in Machine Learning
OpenCourser.com/course/xrgssc/unsupervised
Unsupervised Algorithms in Machine Learning
Most relevant
Unsupervised Machine Learning
OpenCourser.com/course/uc0oqf/unsupervised
Unsupervised Machine Learning
Most relevant
Machine Learning with Python: A Practical Introduction
OpenCourser.com/course/0f0cht/machine
Machine Learning with Python: A Practical Introduction
Most relevant
Machine Learning with Apache Spark
OpenCourser.com/course/s1yvpd/machine
Machine Learning with Apache Spark
Most relevant
Effort
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.
Level
Introductory
Via
Coursera
Institutions
Stanford University
DeepLearning.AI
Instructors
Andrew Ng
Eddy Shyu
Aarti Bagul
Geoff Ladwig
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Bahasa Indonesia
ไทย
қазақша
اُردُو,
فارسی
বাংলা
پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Machine Learning. It's comprised of three courses:
- Advanced Learning Algorithms
- Supervised Machine Learning: Regression and Classification
- Unsupervised Learning, Recommenders, Reinforcement Learning (viewing)
Good to know
Teaches how clustering and deep learning approaches enhance recommender systems
Taught by instructors who are recognized for their work in the AI and machine learning field
Covers real-world AI applications, fostering practical implementation skills
Builds a solid foundation for beginners in machine learning
Requires learners to come in with some background knowledge in machine learning
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