Live Instructor Led Online Training Computer Science courses is delivered using an interactive remote desktop! .

During the course each participant will be able to perform Computer Science exercises on their remote desktop provided by Qwikcourse.

Select among the courses listed in the category that really interests you.

If you are interested in learning the course under this category, click the "Book" button and purchase the course. Select your preferred schedule at least 5 days ahead. You will receive an email confirmation and we will communicate with trainer of your selected course.

A basic guide to using computers that run Windows®. Download the entire book as a PDF File. This can be done in two ways: you can right-click on the link "PDF version" and choose "Save target as" to save the PDF on your computer for viewing at any time or left-click on the link to view it without saving.

Contents

- Computers and Peripherals
- Operating Systems and Controls
- The Desktop
- File Systems
- Concepts and Settings
- Networks and the Internet
- FTP

- Email, Chat-rooms, and IM
- Appendices
- Switching the Control Panel to Classic View
- Connecting to the Internet
- Dual Booting

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

FreeNAS is an embedded open-source NAS (Network-Attached Storage) distribution based on FreeBSD. FreeNAS can be installed on Compact Flash/USB key, hard drive or booted from LiveCD.

- Base System
- Network Protocols supported
- Extra services
- Hard drive / volume management
- Network
- Monitoring

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

ADRIANE stands for "audio desktop reference implementation and networking environment" and is an acoustic menu system, which aims to facilitate blind and severely visually impaired people use the computer and the launch of accessible programs (low barrier). Adriane is integral part of the KNOPPIX live GNU/Linux system and can be started running the boot option “Adriane” within the download version. Alternatively, the KNOPPIX versions containing “Adriane” as part of their file name will start running the audio desktop without special boot options. The main menu system by Adriane contains a selection of programs for Internet applications (e.g. E-Mail, WWW), text processing, creating notes, contact management and SMS-functionality for some mobile phones. If you are using the Adriane version of Knoppix it starts automatically. Otherwise you will have to type "Adriane" when the boot screen is shown. First you will hear the speech output reporting the first menu item "Help with enter key, arrow key down for next menu". By pressing enter you will reach a help window, which explains the basic language output functions. Here you can also select individual functions and components. With the ARROW down key you can reach the other programs and step through the menu. The menu system is designed "flat", which means that programs are located at the first level. In most cases pressing the ENTER key will open the program, in some cases it’s a window with further information or options. This is to keep operation as clear and easily as possible. You can navigate via the ARROW keys and the ENTER key to select an option. The marked menu item will be highlighted and spoken by speech output. The ESCAPE key will always lead you one menu and level higher. Normally this key is located on the upper left corner of the keyboard.

7 hours

¥287,598

7 hours

¥287,598

7 hours

¥287,598

Support vector machines (SVMs) are a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. The original SVM algorithm was invented by Vladimir Vapnik and the current standard incarnation (soft margin) was proposed by Corinna Cortes and Vladimir Vapnik [1]. The standard SVM is a non-probabilistic binary linear classifier, i.e. it predicts, for each given input, which of two possible classes the input is a member of. Since an SVM is a classifier, then given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Intuitively, an SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on. More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. Whereas the original problem may be stated in a finite dimensional space, it often happens that in that space the sets to be discriminated are not linearly separable. For this reason it was proposed that the original finite dimensional space be mapped into a much higher dimensional space presumably making the separation easier in that space. SVM schemes use a mapping into a larger space so that cross products may be computed easily in terms of the variables in the original space making the computational load reasonable. The cross products in the larger space are defined in terms of a kernel function K ( x , y ) {\displaystyle K(x,y)} which can be selected to suit the problem. The hyperplanes in the large space are defined as the set of points whose cross product with a vector in that space is constant. The vectors defining the hyperplanes can be chosen to be linear combinations with parameters α i {\displaystyle \alpha _{i}} of images of feature vectors which occur in the data base. With this choice of a hyperplane the points x in the feature space which are mapped into the hyperplane are defined by the relation: ∑ i α i K ( x i , x ) = c o n s t a n t {\displaystyle \sum _{i}{\alpha _{i}K(x_{i},x)}=constant} Note that if K ( x , y ) {\displaystyle K(x,y)} becomes small as y {\displaystyle y} grows further from x {\displaystyle x} , each element in the sum measures the degree of closeness of the test point x {\displaystyle x} to the corresponding data base point x i {\displaystyle x_{i}} . In this way the sum of kernels above can be used to measure the relative nearness of each test point to the data points originating in one or the other of the sets to be discriminated. The set of points x {\displaystyle x} mapped into any hyperplane can be quite convoluted as a result allowing much more complex discrimination between sets which are far from convex in the original space.

7 hours

¥287,598

7 hours

¥287,598

In the field of Computer Science learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.

For now, there are tremendous work opportunities for various IT fields. Most of the courses in Computer Science is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.

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