Online courses directory (19947)

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.

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!

This is a survey course designed to provide a broad, forward-facing overview of contemporary health informatics, a specialized field of computing that seeks to improve the quality and efficiency of healthcare delivery. To understand health informatics (HIT) you also need to have at least a basic understanding of the complex and highly regulated US healthcare industry. The course is designed for students from diverse backgrounds and who have not been previously exposed to HIT. It is divided into three sections: 1. The US healthcare delivery and the key role of the federal government in promoting HIT adoption 2. The core technologies that drive all contemporary HIT systems and tools 3. The real world applications of HIT from electronic medical and personal health records to exploiting digital data aggregated from them for research and other purposes

Swift is a language created by Apple specifically for iOS and OS X development. It’s fast, concise, and comes with tools which make it easier than ever to visualize one's code. This course focuses on the syntax of the Swift programming language. By the end of the course, students should be able to apply Swift essentials to building iOS apps and employ Swift's more unique elements, like optional types and switch statements, with confidence.

This course covers the essentials of working with remote repositories. You'll be able to connect to a remote repository, get changes from a remote repository, and send changes to a remote repository. You'll also learn how to work collaboratively. You'll fork another developer's repository, make changes to it, and then send them a pull request. You'll also pick up some Git tips and tricks that make working with collaborators a breeze.

The Problem Solving with Analytics course provides students with the foundational knowledge to use data analytics to create business insights. You will learn:

Extending your app to Android Wear smart watches allows you to reach your users wherever and whenever they'd want or need your app. As you'll learn in this course, you can take your existing Android development experience and apply it to developing for Android Wear. This course will take you through getting started, application structure, and information sharing between the wearable and the companion phone or the cloud. We'll also give you the knowledge necessary for designing great user experiences for wearables including how to take advantage of the always-on display of Android Wear devices. This course is part of the Ubiquitous Computing series. Designed as standalone short courses, you can take any course on its own, or take them all! * **Android Wear Development** [this course] * Android TV and Google Cast Development * Android Auto Development

Without customers, your business does not exist. Marketing helps you understand your potential user and focus your product on their needs. This course will help you organize a strategy of identifying your perfect user, find ways to connect with them and what you’ll say when you find them. This covers research, planning, execution and most importantly how to grow your user base. This course is part of our Tech Entrepreneur Nanodegree Program, click [here to learn more](https://www.udacity.com/course/tech-entrepreneur-nanodegree--nd007).

API (Application Programming Interface) endpoints are the connections between your application and the rest of the developer community. In this course you will learn about writing secure, developer-friendly APIs that will make your back-end application thrive and keep your users happy. At the end of this course you will create the back-end for a social application called "Meet n' Eat" that matches together users based on their location and food interests.

This course is a part of the Android Basics Nanodegree by Google. Android apps are everywhere and learning to build them can be a fantastic career move. Continue on your Android app development education and learn to build multi-screen apps! This course is designed for students who have completed the Android for Beginners course and the Android Basics: Multiscreen Apps course. You don’t need any programming experience besides that course! Learning anything new can be tough. We will walk you through the process of making Android apps, but to get the most out of this course, bring your enthusiasm for learning, and budget time on your calendar to learn with us. It will be an adventure! By the end of the course, you’ll build an app that gets you up to date earthquake information! If you’re curious about the road even farther ahead, these are the free courses that make up the Android Basics Nanodegree, in order: * Android Basics: User Interface * Android Basics: User Input * Android Basics: Multiscreen Apps * Android Basics: Networking (This Course) * Android Basics: Data Storage

This class explores how computation impacts the entire workflow of photography, which is traditionally aimed at capturing light from a 3D scene to form a 2D image. A detailed study of the perceptual, technical and computational aspects of forming pictures, and more precisely the capture and depiction of reality on a (mostly 2D) medium of images is undertaken over the entire term. The scientific, perceptual, and artistic principles behind image-making will be emphasized, especially as impacted and changed by computation. Topics include the relationship between pictorial techniques and the human visual system; intrinsic limitations of 2D representations and their possible compensations; and technical issues involving capturing light to form images. Technical aspects of image capture and rendering, and exploration of how such a medium can be used to its maximum potential, will be examined. New forms of cameras and imaging paradigms will be introduced.

This course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, multiview geometry including stereo, motion estimation and tracking, and classification. We’ll develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment (e.g. panoramas), tracking, and action recognition. We focus less on the machine learning aspect of CV as that is really classification theory best learned in an ML course. The focus of the course is to develop the intuitions and mathematics of the methods in lecture, and then to learn about the difference between theory and practice in the problem sets. All algorithms work perfectly in the slides. But remember what [Yogi Berra](http://yogiberramuseum.org/just-for-fun/yogisms/) said: In theory there is no difference between theory and practice. In practice there is. (Einstein said something similar but who knows more about real life?) In this course you do not, for the most part, apply high-level library functions but use low to mid level algorithms to analyze images and extract structural information.

This course is designed to teach you about managing application containers, using Kubernetes. We've built this course in partnership with experts such as Kelsey Hightower and Carter Morgan from Google and Netflix’s former Cloud Architect, Adrian Cockcroft (current Technology Fellow at Battery Ventures), who provide critical learning throughout the course. Mastering highly resilient and scalable infrastructure management is very important, because the modern expectation is that your favorite sites will be up 24/7, and that they will roll out new features frequently and without disruption of the service. Achieving this requires tools that allow you to ensure speed of development, infrastructure stability and ability to scale. Students with backgrounds in Operations or Development who are interested in managing container based infrastructure with Kubernetes are recommended to enroll! In this course you will learn to: - Containerize an application by creating Docker config files and build processes to produce all the necessary Docker images - Configure and launch an auto-scaling, self-healing Kubernetes cluster - Use Kubernetes to manage deploying, scaling, and updating your applications - Employ best practices for using containers in general, and specifically Kubernetes, when architecting and developing new microservices

This course provides a one-semester overview of information security. It is designed to help students with prior computer and programming knowledge — both undergraduate and graduate — understand this important priority in society today. The technical content of the course gives a broad overview of essential concepts and methods for providing and evaluating security in information processing systems (operating systems and applications, networks, protocols, and so on). In addition to its technical content, the course touches on the importance of management and administration, the place information security holds in overall business risk, social issues such as individual privacy, and the role of public policy.

This course will introduce you to the world of data analysis. You'll learn how to go through the entire data analysis process, which includes: * Posing a question * Wrangling your data into a format you can use and fixing any problems with it * Exploring the data, finding patterns in it, and building your intuition about it * Drawing conclusions and/or making predictions * Communicating your findings You'll also learn how to use the Python libraries NumPy, Pandas, and Matplotlib to write code that's cleaner, more concise, and runs faster. This course is part of the [Data Analyst Nanodegree](https://www.udacity.com/course/data-analyst-nanodegree--nd002).

Any meaningful experience on the web has a form. Whether it's a form made of text boxes, toggles, buttons, checkboxes, or touchable widgets, web developers need to be purposeful about forms to make users happy and increase [conversions](https://en.wikipedia.org/wiki/Conversion_marketing). In this course, you'll learn best practices for modern forms as taught by Google's Ido Green and Udacity's Cameron Pittman. You'll practice your skills along the way with a few self-directed projects, including an e-commerce checkout and an event planner app! As a special treat, you'll also watch a series of interviews with Luke Wroblewski, Google Product Director and author of [Web Form Design](http://www.lukew.com/resources/web_form_design.asp), to get his take on interactions for the modern web.

*This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. Taking this class here does not earn Georgia Tech credit.* Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning! Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how you can use Unsupervised Learning approaches -- including randomized optimization, clustering, and feature selection and transformation -- to find structure in unlabeled data. **Series Information**: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences. - [Machine Learning 1: Supervised Learning](https://www.udacity.com/course/ud675) - [Machine Learning 2: Unsupervised Learning](https://www.udacity.com/course/ud741) (this course) - [Machine Learning 3: Reinforcement Learning](https://www.udacity.com/course/ud820) If you are new to Machine Learning, we suggest you take these 3 courses in order. The entire series is taught as an engaging dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).

This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations.

This course is focused squarely on user experience, and seeks to show developers how thinking offline-first is the best way to ensure that applications perform their best in all scenarios, not just ideal ones. You'll learn to recognize the differences between good, poor, intermittent, and missing connectivity for your users , and master how to make applications that navigate these conditions with ease. Using the skills you master in this course, you'll conclude by building an app that that works both online and offline, and loads in new data when it can. You'll be a master of the cache! Your web apps will interact with the network just like native apps do. This will lead to better user experiences even in traditionally challenging connection scenarios like being stuck in a train tunnel, having to rely on over-crowded conference Wi-Fi, or traveling through a cellular “dead zone!”