subject: Android web 3.0 python technologies for the best to be the best [print this page] Android web 3.0 python technologies for the best to be the best
The objectives of the course are:
Build your own Android application.
Understand how Android applications work, their life cycle, manifest, Intents, and using external resources
Design and develop useful Android applications with compelling user interfaces by using, extending, and creating your own layouts and Views and using Menus
Introduction to advanced Android features like GPS access, mapping, and the camera
Project: Building an Android Application using Location Based Service. The Android application should be able to remind you of the offerings like theatre, movies, malls, libraries that you can avail at a particular location.
This Android Development Course has been specially designed for beginners.
The course will be covered with the help of an example followed by a discussion of the application of the relevant concept.
Attendance
It is essential that you attend all the sessions in order to fully comprehend the topics and for maximum take away from the work shop.
Text Book:
The Trainees will be provided with relevant books in softcopies for the course.
Our faculty comes from esteemed organizations of yahoo,google Tmobile and motorola
We are in the process out our sucessful model and our efficient training mechanism in bangalore and allahbad and in the process to break all barriers in other cities as well.
Our aim is to gain momentum into machine learning.
Machine Learning is a natural outgrowth of the intersection of Computer Science and Statistics. We might say the defining question of Computer Science is "How can we build machines that solve problems, and which problems are inherently tractable/intractable?" The question that largely defines Statistics is "What can be inferred from data plus a set of modeling assumptions, with what reliability?" The defining question for Machine Learning builds on both, but it is a distinct question. Whereas Computer Science has focused primarily on how to manually program computers, Machine Learning focuses on the question of how to get computers to program themselves (from experience plus some initial structure). Whereas Statistics has focused primarily on what conclusions can be inferred from data, Machine Learning incorporates additional questions about what computational architectures and algorithms can be used to most effectively capture, store, index, retrieve and merge these data, how multiple learning subtasks can be orchestrated in a larger system, and questions of computational tractability.
