Unsupervised Learning, Recommenders, Reinforcement Learning from Coursera

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.

What's inside

Syllabus

Unsupervised learning

This week, you will learn two key unsupervised learning algorithms: clustering and anomaly detection

Recommender systems

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

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

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 Unsupervised Learning, Recommenders, Reinforcement Learning with these activities:

Review concepts of linear algebra

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Refresh concepts of linear algebra to reinforce foundational understanding for the course.

Browse courses on Linear Algebra

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Review the definition and properties of vectors and matrices.
Solve systems of linear equations.
Find eigenvalues and eigenvectors.

Pandas & NumPy tutorials

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Take a guided tutorial on Pandas and NumPy to further develop data manipulation and exploration skills.

Browse courses on Pandas

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Learn the basics of Pandas dataframe manipulation.
Explore data with NumPy arrays.
Combine Pandas and NumPy for data analysis.

Clustering algorithm practice

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Complete practice drills on clustering to strengthen understanding and apply clustering techniques to real-world datasets.

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Implement K-Means clustering using an existing library.
Cluster data using K-Means for different values of k.
Evaluate the quality of clustering results using metrics like the silhouette coefficient.
Apply clustering to a real-world dataset.

Four other activities

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Build a recommender system

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Construct a simple recommender system to gain hands-on experience in designing and implementing recommendation engines.

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Choose a dataset and define metrics for evaluation.
Implement a collaborative filtering algorithm.
Evaluate the performance of the recommender system.

Workshop: Introduction to reinforcement learning

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Attend a workshop on reinforcement learning to learn the fundamental concepts and to develop a reinforcement learning model.

Browse courses on Reinforcement Learning

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Understand the basics of reinforcement learning.
Implement a simple reinforcement learning algorithm.
Apply reinforcement learning to a real-world problem.

Contribute to open-source reinforcement learning projects

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Make contributions to open-source reinforcement learning projects to gain practical experience and deepen understanding.

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Identify a suitable open-source reinforcement learning project.
Contribute to the project by fixing bugs, adding features, or improving documentation.
Interact with the project community and other contributors.

Develop a machine learning workflow for a real-world project

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Build a complete machine learning workflow for a real-world project to gain practical experience in the entire machine learning process.

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Define the problem and gather data.
Preprocess and explore the data.
Select and train a machine learning model.
Evaluate and deploy the model.

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 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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Business Analyst

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.

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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.

See salaries and explore the career path for Marketing 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.

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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.

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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.

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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.

See salaries and explore the career path for Financial Analyst

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.

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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.

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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.

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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.

See salaries and explore the career path for Writer