Online courses directory (19947)
Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.
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.
In this course, you’ll learn how to use Firebase. Firebase is a cloud backend, and one of the leading choices for Backend as a Service. It enables you to quickly get synchronized data up and running for multi-user apps. This is important because nearly every mobile app these days requires authentication and real-time data updates. We’ll begin by showing you how easy it is to read and write almost any data to Firebase. After that, we’ll teach you how to allow users to login and have data associated with them. We’ll then cover how to write queries and filters for your data. You’ll discover how to take advantage of Firebase's offline capabilities, and master efficient database design for lightning-fast data retrieval. Lastly, you’ll learn how to use Firebase’s Security and Rules language to secure and add permissions to your data. By the end of this course you will have an Android application that can store and share data between different users in real time as well as authenticate and authorize those users.
Prototyping allows you to spend ten minutes -- instead of ten hours -- finding an amazing design for your product. This course will guide you through the iterative process of prototyping an app and conducting user research. You will start by making a low fidelity paper prototype, conducting research with users, and using that research to inform your next iteration. Together with InVision we’ll guide you through creating interactive prototypes. This means you don’t have to code an app before putting it in front of users! Along the way, you’ll learn how and where prototyping fits into your app design process and how you can use prototyping to become a better entrepreneur. This course is part of our Tech Entrepreneur Nanodegree Program, click [here to learn more](https://www.udacity.com/course/tech-entrepreneur-nanodegree--nd007).
Introduction to Operating Systems is a graduate-level introductory course in operating systems. This course teaches the basic operating system abstractions, mechanisms, and their implementations. The core of the course contains concurrent programming (threads and synchronization), inter process communication, and an introduction to distributed operating systems. The course is split into four sections: (1) Introduction, (2) Process and Thread Management, (3) Resource Management and Communication, and (4) Distributed Systems.
The summary for this course comes from the [course web site at Georgia Tech](http://www.cc.gatech.edu/fac/traynor/cs6262/s14/index.html). Note that watching this course on Udacity does not count for Georgia Tech credit unless you are enrolled in the [Georgia Tech OMSCS](http://omscs.gatech.edu/) program. This course provides an introduction to computer and network security. Students successfully completing this class will be able to evaluate works in academic and commercial security, and will have rudimentary skills in security research. The course begins with a tutorial of the basic elements of cryptography, cryptanalysis, and systems security, and continues by covering a number of seminal papers and monographs in a wide range of security areas. Topics covered include network security, authentication, security protocol design and analysis, security modeling, trusted computing, key management, program safety, intrusion detection, DDOS detection and mitigation, architecture/operating systems security, security policy, group systems, biometrics, web security, and other emerging topics. Most of the course readings will come from seminal papers in the field. Links to these papers will be provided on the course pages. In addition, links to critical reference materials will also be provided.
Helping you build human-centered design skills, so that you have the principles and methods to create excellent interfaces with any technology.
In this class, you will learn fundamental algorithms and mathematical models for processing natural language, and how these can be used to solve practical problems.
In this course you will learn several fundamental principles of algorithm design: divide-and-conquer methods, graph algorithms, practical data structures (heaps, hash tables, search trees), randomized algorithms, and more.
In this course, we will explore how to wrangle data from diverse sources and shape it to enable data-driven applications. Some data scientists spend the bulk of their time doing this! Students will learn how to gather and extract data from widely used data formats. They will learn how to assess the quality of data and explore best practices for data cleaning. We will also introduce students to MongoDB, covering the essentials of storing data and the MongoDB query language together with exploratory analysis using the MongoDB aggregation framework. This is a great course for those interested in entry-level data science positions as well as current business/data analysts looking to add big data to their repertoire, and managers working with data professionals or looking to leverage big data. This course is also a part of our Data Analyst Nanodegree.
This course will teach you how to start from scratch in answering questions about the real world using data. Machine learning happens to be a small part of this process. The model building process involves setting up ways of collecting data, understanding and paying attention to what is important in the data to answer the questions you are asking, finding a statistical, mathematical or a simulation model to gain understanding and make predictions. All of these things are equally important and model building is a crucial skill to acquire in every field of science. The process stays true to the scientific method, making what you learn through your models useful for gaining an understanding of whatever you are investigating as well as make predictions that hold true to test. We will take you on a journey through building various models. This process involves asking questions, gathering and manipulating data, building models, and ultimately testing and evaluating them.
You will learn how to optimize any website for speed by diving into the details of how mobile and desktop browsers render pages. In this short course, you’ll learn about the Critical Rendering Path, or the set of steps browsers must take to convert HTML, CSS and JavaScript into living, breathing websites. From there, you’ll start exploring and experimenting with tools to measure performance and simple strategies to deliver the first pixels to the screen as early as possible. You’ll learn how to dive into recommendations from [PageSpeed Insights](https://developers.google.com/speed/pagespeed/insights/ "PageSpeed Insights") and the Timeline view of Google Chrome’s Developer Tools to find the data you need to achieve immediate performance boosts! This course is also a part of our Front-End Web Developer Nanodegree.
Ten minutes into this class you will make your own completely personalized version of the insanely addictive game 2048. Even if you've never coded before. Pretty cool, no? You'll do this by making small (but important) modifications to the source code for the original 2048 game. If this sounds intimidating, don't worry. We'll teach you the basics of HTML and CSS and how they interact with Javascript (don't worry if that sentence doesn't mean anything to you yet. It will soon). The purpose of this class is to have fun learning how to quickly and easily take an existing open source program, make some changes, and create something that looks and feels completely new. The game you create will be mobile friendly (you can play it on your phone!) and totally shareable.
Learn about the inner workings of cryptographic primitives and how to apply this knowledge in real-world applications!
In this class, you will learn how to think with models and use them to make sense of the complex world around us.
In this course, you will learn how to formalize information and reason systematically to produce logical conclusions. We will also examine logic technology and its applications - in mathematics, science, engineering, business, law, and so forth.
This course will discuss the major ideas used today in the implementation of programming language compilers. You will learn how a program written in a high-level language designed for humans is systematically translated into a program written in low-level assembly more suited to machines!
This class is offered as CS7641 at Georgia Tech where it is a part of the [Online Masters Degree (OMS)](http://www.omscs.gatech.edu/). Taking this course here will not earn credit towards the OMS degree. Machine Learning is a graduate-level course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. In part two, you will learn about Unsupervised Learning. Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? Such answers can be found in this section! Finally, can we program machines to learn like humans? This Reinforcement Learning section will teach you the algorithms for designing self-learning agents like us!
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