Udacity

Reinforcement Learning (Udacity)

Offered by Georgia Institute of Technology, Udacity,

You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.

Class Deals by MOOC List - Click here and see Udacity's Active Discounts, Deals, and Promo Codes.

This course will prepare you to participate in the reinforcement learning research community. You will also have the opportunity to learn from two of the foremost experts in this field of research, Profs. Charles Isbell and Michael Littman.

Prerequisites and requirements
Before taking this course, you should have taken a graduate-level machine-learning course and should have had some exposure to reinforcement learning from a previous course or seminar in computer science.
Additionally, you will be programming extensively in Java during this course. If you are not familiar with Java, we recommend you review Udacity's Object Oriented Programming in Java course materials to get up to speed beforehand.

Go to Class

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Udacity

Udacity,Google Cloud

Introduction to Image Generation with Google Cloud (Udacity)

Robotics & Computer Vision

Learn what diffusion models are, how they work, and real use-cases for them. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space.

Self Paced

Self-Paced

Artificial Intelligence Machine Learning AI

Udacity

Udacity,Google Cloud

Encoder-Decoder Architecture with Google Cloud (Udacity)

Robotics & Computer Vision

Learn about the main components of the encoder-decoder architecture and how to train and serve these models. This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering.

Self Paced

Self-Paced

Artificial Intelligence Machine Learning AI

Coursera

University of Washington

Practical Predictive Analytics: Models and Methods (Coursera)

Statistics & Data Analysis Data Science

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Jun 22nd 2026

4 Weeks

Machine Learning Models Methods

Coursera

University of Washington

Machine Learning: Regression (Coursera)

Statistics & Data Analysis Data Science

Case Study - Predicting Housing Prices. In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.

Jun 22nd 2026

5-12 Weeks

Python Algorithms Machine Learning

Udacity

Udacity,AWS

AWS DeepRacer (Udacity)

Data Science

Learn the fundamentals of machine learning and reinforcement learning in a fun and engaging way through autonomous driving with AWS DeepRacer. This course will prepare you to create, train, and fine-tune reinforcement learning models in the AWS DeepRacer 3D racing simulator. You will be able to utilize the car's tech specs, assembly, and calibration to train and deploy your racing model using AWS in both simulated and real-world tracks.

Self Paced

Self-Paced

Machine Learning Reinforcement Learning AWS DeepRacer

Coursera

University of Washington

Machine Learning: Clustering & Retrieval (Coursera)

Statistics & Data Analysis Data Science

Case Studies: Finding Similar Documents. A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?

Jun 22nd 2026

5-12 Weeks

Machine Learning Clustering MapReduce

Coursera

University of Washington

Computational Neuroscience (Coursera)

Robotics & Computer Vision

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning.

Jun 22nd 2026

5-12 Weeks

Neuroscience MATLAB Brain

Udacity

Segmentation and Clustering (Udacity)

Statistics & Data Analysis Data Science

Use machine learning to create segments. The Segmentation and Clustering course provides students with the foundational knowledge to build and apply clustering models to develop more sophisticated segmentation in business contexts. In this course, you'll learn how to use an advanced analytical method called clustering to create useful segments for business contexts, whether its stores, customers, geographies, etc. You'll learn this through improving your fluency in Alteryx, a data analytics tool that enables you prepare, blend, and analyze data quickly.

Self Paced

Self-Paced

Machine Learning Clustering Segmentation

Coursera

University of Washington

Machine Learning: Classification (Coursera)

Statistics & Data Analysis Data Science

Case Studies: Analyzing Sentiment & Loan Default Prediction. In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.

Jun 22nd 2026

5-12 Weeks

Python Machine Learning Classification

Udacity

Data Science Interview Prep (Udacity)

Personal and Professional Development Data Science

Confidently take on the tech interview. Data science job interviews can be daunting. Technical interviewers often ask you to design an experiment or model. You may need to solve problems using Python and SQL. You will likely need to show how you connect data skills to business decisions and strategy. In this course, you'll review the common questions asked in data science, data analyst, and machine learning interviews.

Self Paced

Self-Paced

Data Structures Machine Learning Interview

Udacity

Machine Learning Interview Preparation (Udacity)

Personal and Professional Development Data Science

Prove your qualifications in your machine learning interviews. In this course, you’ll learn exactly what to expect during a machine learning interview. You’ll cover all the common questions and technical strategies, and review a range of important topics, from machine learning algorithms to image categorization. You’ll also learn best practices for data structure questions and whiteboard problems, and at the end of the course, you’ll get unlimited access to mock interviews on Pramp.

Self Paced

Self-Paced

Machine Learning Career Interview

Coursera

Google Cloud

Preparing for the Google Cloud Professional Data Engineer Exam (Coursera)

CS: Information & Technology Computer Science

From the course: "The best way to prepare for the exam is to be competent in the skills required of the job." This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job.

Jun 27th 2026

5-12 Weeks

Machine Learning Data Engineer Google Cloud