Site announcements

(No announcements have been posted yet.)


Available courses

Your Brain Studio  

 The fitness studio for your brain

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.

Obj:

  • Participants will complete this module with an understanding of the affordances and limitations of leveraging social media in formal learning spaces.

EQ:

  • 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...

In this module participants will look at ways to create a classroom workflow that includes both collaboration between teacher and students and between students.  

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.

  1. Definition, 
  2. Description, 
  3. Creation and
  4. Tree Traversal.

BST Gif

Image Source: https://commons.wikimedia.org/wiki/File:Binary_search_tree_example.gif

Advanced instructional design course.

Instructor: Doug Holton <doug.holton@usu.edu>

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.

Engineering Health: Introduction to Yoga and Physiology This course gives you access to an exploration of physiological systems from the perspective of overall health and wellness. In particular, a focus on yoga, meditation and mindfulness as a therapeutic intervention in chronic illness and long term treatment. This course is intended for yoga practitioners and teachers, as well as college students and medical practitioners looking for a deeper understanding of the physiological benefits of yoga. The value of taking this course is to understand the impact that yoga can have on reducing stress, and aiding in healing or preventing physiological pathologies. Throughout this course, we will learn about different physiological systems and highlight yoga practices that can influence different systems and reduce pathology. Reading material will include analysis of scientific studies that have successfully utilized yoga practice as a tool for treatment of various illnesses such as: hypertension, stress, diabetes, insomnia, chronic pain and PTSD. In order to understanding these conditions, lectures will provide a complete understanding of the correlating physiological system. The weekly course assignment will include physiology lectures, a weekly yoga practice, suggested readings, and optional discussions for a total of 3-5 hours per week. The course will provide a tremendous amount of information and hands on experience for those interested in alternative health perspectives and a more in depth scientific understanding of this ancient healing method.

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.

Learning Portal for Certificate in Artificial Intelligence and Cognitive Technology 

This course is designed to give overview of machine learning to New Learners . 

Level:  Beginner

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?