How will India Train 40Cr People by 2022 under Skill Development Initiative?

In India, young people who will soon be entering the labor and manufacturing market, would constitute the largest segment of the demographic structure. In recent years India has rapidly expanded the capacity of educational institutions and enrolments but educational attainment remains low. While India has a well-institutionalized system of vocational training, it has not sufficiently prepared its youth with the skills that today’s industries require.

Thus, to speed its economic growth and take advantage of its “demographic dividend,” Prime Minister Narendra Modi has launched four initiatives as a part of which over 40 crore people will be trained in various skills by 2022. As much as it is important to drive such initiatives, we must accept that the on-going techniques and implementations could be treacherous to track outcome oriented initiatives.

Over all, here are some of the challenges pertaining in skill development initiatives:

  • Lack of common certification systems
  • Lack of awareness and mentoring process due to poor mobilizations
  • Lack of replicable mobilization model
  • Lack of basic infrastructure, training facilities and a sense of social disapproval for women attending skill training courses
  • Lack of soft skill training and its practices
  • Lack of effective assessment criteria

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Some of the up-gradation and changes which must be inculcated:

  • Robust monitoring & evaluation mechanism to ensure successful implementation of the policy initiatives
  • Unless modern technological tools like internet are used to impart vocational training, skilling cannot be scaled up in a country where millions of youths are to be covered in our diverse land scape
  • Mobile based portable authentication solutions to ensure the scheduled training are delivered in all the rural, semi-rural areas and urban area
  • Mechanism for on-the-field assessments of the trained candidates
  • Solutions driven by scalable and portable technology for trainers and skill development intuitions to have strong hold on their trainee to avoid surprise drop out, lack of interest or social disapproval
  • National as well International level Industrially approved common certification systems
  • Mass awareness and counselling channels with technically advanced tool for replicable mobilization
  • To formalize convenient yet effective career counselling bodies
  • Formulating outcome oriented policies
  • Impart mechanisms to reward the success and penalize the failures

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How to begin your career in Computer Vision and Machine Learning Field?

This post is for Computer vision enthusiasts. We are highlighting the focal point of resources and computer vision tutorials for all CV aficionados and get themselves started in this emerging field. As a passionate beginner in the field of computer vision, we hope you find this useful.
Some prior knowledge about linear algebra, calculus, probability and statistics would definitely be a plus but its not always required. The most important thing is to get started and you can learn other essential things on the fly.
Courses:
  • Computer Vision – Mubarak Shah (UCF) : All the materials related to the course are available online and what is more interesting is that even the video lectures are available.
  • Computer Vision – Subhransu Maji (UMass Amherst) : Provides access to all the lecture materials and assignments but there are no video lectures.
  • Visual Recognition – Kristen Grauman (UT Austin) : This Provides links to some of the interesting and fundamental papers in computer vision.
  • Language and Vision – Tamara Berg (UNC Chapel Hill) : This course is basically aimed towards exploring topics straddling the boundary between Natural Language Processing and Computer Vision.
  • Convolutional Neural Networks for Visual Recognition – Fei-Fei Li and Andrej Karpathy (Stanford University) : This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for computer vision tasks with a main focus on image classification.
Some additional resources:

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Computer Vision
In addition to this, it is very useful to be aware of most of the basic image processing techniques presented in this book Digital Image Processing – Gonzalez 2007
OpenCV Programming
Software Packages:
You can find an exhaustive list of links presenting code which implements some of the standard vision algorithms at http://www.cvpapers.com/rr.html
Major Conferences: 
Below are some of the major conferences listed in their ranking order.
Most of the papers published in the above mentioned conferences can be accessed at http://www.cvpapers.com/index.html
Nice way to keep track of the conferences deadline is via http://conferences.visionbib.com/Iris-Conferences.html
Now that you have acquired some knowledge of computer vision and Deep Learning (from the previous post), please feel free to compete in Kaggle competitions (best way to put your learning into practice).
If you would like to have any guidance/support in CV domain or have any additional resource information, we would love to hear it without judging you. You may drop your comments below.

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