Tag: ML

Leveraging Blockchain, AI , ML, Cloud Computing for digital transformation

 Union Education Minister Shri Ramesh Pokhriyal ‘Nishank’ chaired the 54th Meeting of the Council of Indian Institutes of Technology (IITs) today through online medium. Minister of State for Education Shri Sanjay Shamrao Dhotre; Secretary, Higher Education Shri Amit Khare was also present in the meeting.

Union Minister congratulated Heads of all IITs and Chairpersons (BoG) for successfully continuing the academics during the challenging times of COVID-19 and for their remarkable contribution in combating the COVID-19 by way of new scientific research. He exhorted the IITs to become a driving force behind realizing the vision of Atmanirbhar Bharat, as envisioned by Prime Minister Shri Narendra Modi.

Shri Pokhriyal asked IITs to develop Institute Development Plan as envisioned in NEP 2020 to improve the mobility of faculty between institution and Industry. The mobility of faculty members and industry experts between technical institutes and industry will promote collaborations between industry and academia, he added. Earlier, a committee was constituted under the chairmanship of Dr K. Radhakrishnan, Chairman, Standing Committee for IIT Council for suggesting recruitment of faculties in IITs (Industry interaction and mobility of faculty).

Union Minister also urged IITs to adopt One IIT – One Thrust Area approach based on local needs.

Digital Transformation using Blockchain, AI, ML, and Cloud Computing at IITs also came up for discussion during the meeting. It was recommended to constitute a taskforce to review use of technology at all IITs and also to accelerate deployment of digital tools. It was also recommended to undertake rationalization of staff from current standards to a lower number.

On the basis of the recommendation of the Chairman of Standing Committee for IIT Council, four Working Groups were constituted on the following issues related to NEP-2020:

Group-1: Graded Autonomy, Empowered and accountable BoG and Director

Group-2: Grooming distinguished academics for directorship of IITs

Group-3: Reform and restructuring of the Academic Senate

Group-4: Innovative funding mechanisms

The reports of the these groups and also of a new group which will work on faculty development will be presented in a meeting to be chaired Union Education Minister.

The Council also put up for consideration a suggestion to arrange Online IIT R&D fair to showcase the quality research work being done by IITs to showcase the R&D work of IITs to the industry. This may be followed by a physical fair in after normalization of present situation.

Highlighting several initiatives undertaken by the government, Shri Pokhriyal mentioned about approval of funds to improve the overall infrastructure of the 4 IITs at Madras, Delhi Kharagpur and Bombay. He spoke about making IITs as multi-disciplinary institutions for holistic growth in all disciplines of education, without losing its main focus on Scientific Research and Technology Development. He called upon IITs to make full use of the recently announced scheme National Research Foundation. He hoped IITs will strive to achieve the global rankings by 2022, the 75th Year of India’s Independence. He assured IITs of every possible help and support in terms of finance, administrative guidance, issues relating to various other Ministries and agencies of the Government.   

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internet of things (IoT)

The internet of things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

A thing in the internet of things can be a person with a heart monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert the driver when tire pressure is low or any other natural or man-made object that can be assigned an Internet Protocol (IP) address and is able to transfer data over a network.

Increasingly, organizations in a variety of industries are using IoT to operate more efficiently, better understand customers to deliver enhanced customer service, improve decision-making and increase the value of the business.

How IoT works

An IoT ecosystem consists of web-enabled smart devices that use embedded systems, such as processors, sensors and communication hardware, to collect, send and act on data they acquire from their environments. IoT devices share the sensor data they collect by connecting to an IoT gateway or other edge device where data is either sent to the cloud to be analyzed or analyzed locally. Sometimes, these devices communicate with other related devices and act on the information they get from one another. The devices do most of the work without human intervention, although people can interact with the devices — for instance, to set them up, give them instructions or access the data.

Machine learning: How to learn

Let’s assume that computers can learn something new without explicitly programmed or without any human interference. Isn’t this sound interesting? So, let’s talk about how this could be possible. This is where the concept of machine learning comes into the frame.

It is the application of Artificial Intelligence(AI) which gives computers the ability to learn itself by data stored, observations made, and examples. The computer gets the idea of how to react by using this data. Machine learning aims to make computers more self-dependent so that they can learn themselves.

Now what you have to learn to make your computer smart enough to learn itself.so, the top 10 languages for machine learning are

  • Python
  • c++
  • Java
  • Java Script
  • C#
  • R
  • Julia
  • Go
  • TypeScript
  • Scala

ML is a growing area of AI and there are a lot of languages which support the ML libraries and frameworks, but still, python is one of the most chosen and learned language for ML followed by C++, Java, and others.

This is all about which language you should use or prefer to learn for this purpose. Now if you are a beginner then one of the most important questions is how to learn this concept? You don’t have to pay a large sum of money for this, it’s is not mandatory that you have to have a good and prior knowledge of any above-mentioned programming language. You can simply learn them anytime so if you are a fresher and an enthusiast of learning ML, let’s begin.

First of all, don’t confuse this with data science, AI, predictive analysis, etc. although many concepts may overlap they are not the same.

And trust me guys the self-starter way of learning this is doing this. The companies don’t care about the proofs all they want to know how you can turn their data into gold. So instead of spending a lot of time in textbooks and theory and ultimately get frustrated and start considering this a very hard to learn the topic. Start switching between theory and practical, make projects, do experiments. You will surely have more fun and have something good for presenting on your portfolio.

In a nutshell, the self-starter way is better, practical, and faster.

The four steps to learn machine learning are:

  • Prerequisites -Build a foundation of statistics, programming, and a bit of math.
  • Sponge mode-Immerse yourself in the essential theory behind ML.
  • Targeted Practice-Use ML packages to practice the 9 essential topics.
  • ML projects-Dive deeper into interesting domains with larger projects.

You should definitely forge these aspects to start your learning journey but here it is just a brief way of how to learn and from where so I have not encompassed these topics as a whole here but once you start exploring you would surely get to know about them.

Now being a beginner it’s very easy to distract from your goals and you might think to drop the idea to learn in this lockdown so the tip which I would like to share is to nip the idea of giving up in the bud and be keen as mustard to explore this.

Please learn to walk before you run. Try to get focused on the core concepts first so don’t get fascinated by the advanced concepts. The advanced topics will get much easier to learn once you master the core ones.

Seek different perspectives. The way a statistician explains an algorithm will be different from the way a computer scientist explains it. Seek different explanations of the same topic.

And the most important try to alternate between practice and theory. And Don’t believe the hype. Machine learning is not what the movies portray as artificial intelligence. It’s a powerful tool, but you should approach problems with rationality and an open mind. ML should just be one tool in your arsenal!

Here is a rundown of some resources from where you can learn ML:

  • CS50’s Introduction to Artificial Intelligence with Python.
  • Python programming tutorials by Socratica.
  • Google’s machine learning crash course. 
  • ML and Big Data Analytics course. 
  • Machine learning course from Stanford.
  • Elements of AI. 
  • Machine learning with Python

So, all the best for your learning journey guys. Hope you guys enjoyed it!