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Your Brain Studio
IEDC's LMS is a the part of Virtual Classroom, Virtual Classroom provides a whole new real time experience of learning on demand at any place & at any time, by our experts.
LMS is a Moodle based system for managing all your learning needs.
This course will give you overview of how to use IEDC LMS
Welcome to our course on environmental problems. Your and your childrens' future is at risk if we do not change our habits and take action for a better planet.
This unit deals with various environmental aspects and you will also meet people who decided to take some action.
Using SAMR Model for integrating technology into teaching and learning process.
Participants will complete this module with an understanding of the affordances and limitations of leveraging social media in formal learning spaces.
How can social media help teaching and learning in my classroom?
What resources are available for guiding and informing my thinking around the use of social media in the classroom?
- What are important considerations and implications of social networking in teaching and learning?
How to use Technologies in my course?
Best practices of using the various technologies around and implementing them into your Moodle course.
Multimedia, Flash, Java, Video, Animation...
Fate of the World
The year is 2020. Climate change has been ignored. Cities are underwater. People are starving. Nations brace for war. Species are dying. And you’ve got to solve the crisis. The fate of the world is in your hands
The audience for this course is University level science students with an interest in climate science and climate change. The aim is to give a broader view of the topic of climate change.
In this course teachers will be able to explain the various componets of a mobile workflow and begin to create and manage a professional mobile workflow of their own. be able to confidently manage and create a professional mobile workflow.
Learning Management System for B. Tech IT Sem VII
Subject : Artificial Intelligence
This is a STTP on Basics of the non linear data structure, Tree.
- Creation and
- Tree Traversal.
Advanced instructional design course.
Instructor: Doug Holton <firstname.lastname@example.org>
This is the introductory course for Python for Beginners. Please start here if you have no experience coding in Python. This course is self-paced; you can proceed through the course, but need to complete each unit before moving on to the next unit.
Sickle Cell Anemia
These resources have a Creative Commons licence and you can use these resources however you see fit (in a non-profit way)
These resources are aimed at the Higher Educational student or for practitioners level
This course is a B1 Intermediate Level Course. It is recommended for students who have a good level of English.
This is a free view and only has ONE unit . The full course has 10 units with written assignments that are marked by your tutor.. At the end of the course, there is final B1 level assessment. All students receive a detailed language profile report with a recommendation for further courses.
By the end of the full course students:
- Can understand the main points of clear standard input on familiar matters regularly encountered in school, leisure, etc.
- Can deal with most situations likely to arise while at school in an area where the language is spoken.
- Can produce simple connected text on topics that are familiar or of personal interest.
- Can describe experiences and events, dreams, hopes and ambitions and briefly give reasons and explanations for opinions and plans.
Demo AI Course
Learning Portal for Certificate in Artificial Intelligence and Cognitive Technology
This course is designed to give overview of machine learning to New Learners .
Duration :30 Hrs (Video + Exercise)
Learn and apply fundamental machine learning concepts with the Crash Course, get real-world experience with the companion Kaggle competition. The course is designed using public domain content of Google AI.
After Completing the course you will able to understand
How does machine learning differ from traditional programming?
What is loss, and how do I measure it?
How does gradient descent work?
How do I determine whether my model is effective?
How do I represent my data so that a program can learn from it?
How do I build a deep neural network?