CL Data School

Machine Learning for Faculty

A workshop towards industry 4.0 readines


Faculty: Sujit Bhattacharyya Language: English

At Career Launcher, we believe that in the next decade India will play a crucial role globally by providing high calibre and highly skilled youth to the world. Transformation of higher education, including that of technical institutions through new era courses and digital tools, is one of the value-adds we offer across India.
We are delighted to partner AICTE in delivering a faculty development program in Machine Learning. We look forward to offering such programs to institutions and students in a win-win manner, as we move into the future.
Happy Machine Learning to all of you!
- Satya Narayan R
(Chairman -Career Launcher)

We are focused on enabling developers to simplify skill development and providing the best skills for Alexa users in India. It is exciting to see Career Launcher offer skills that will help students in an engaging way. - Dilip RS
(Country Manager, India, Alexa Skills)

The AI Age:

With the world around us becoming more & more digital, the exponential growth of data is constantly feeding AI improvements. A study by EY & NASSCOM has revealed that by 2022, an estimated 46% of the workforce will be engaged in jobs that do not exist today, or will be deployed in jobs that have radically changed skillsets.
In sum, AI is becoming mainstream; a growth driver; a unique competitive advantage. All these combine to magnify its importance.

Data is to AI what food is to humans.
- Barry Smyth
(Professor of Computer Science University College Dublin)

Industry 4.0

The commonly used term for the Fourth Industrial Revolution, Industry 4.0 depicts the current trend of automation and data exchange in manufacturing technologies.

Computing technology has emerged as the key differentiator and also, curiously, the key leveler between developed and emergent economies. In such a scenario, it is imperative for emergent economies to leverage to the fullest their abundant human capital. This can be best reaped by creatively engaging the most productive age groups of their population.

2-Day Workshop

An introductory program to Machine Learning, this exclusive workshop has been designed for faculty members, who wish to understand the applications of ML hands-on.

Objectives
  1. Get introduced to the domain of machine learning
  2. Develop an understanding of its practical applications
  3. Exposure to practical, industry-relevant projects
  4. Enable students' internships & placements in ML

Note: No prior programming knowledge is necessary.

Topics
  1. Introduction to ML, Job Trends, Industry Opportunities
  2. Introduction to Python, Data Handling using Pandas
  3. Data Visualization
  4. Hands-on ML: Solving a Supervised Problem
  5. Hands-on ML: Solving an Unsupervised Problem
  6. Introduction to ANN (Artificial Neural Networks)
  7. Voice Technology Trends
  8. Building a Voice Bot on Amazon Alexa devices (Demo)
  9. Internship Ideas for Students

Our ML & DS-led team in Canada can be strengthened through programs such as these from Career Launcher and AICTE. - Vijay Shekhar Sharma
(Founder, Paytm)

We are happy to offer this Machine Learning for Faculty at Technical Institutions program in partnership with Career Launcher. - Dr. MP Poonia
(Vice Chairman, AICTE)

We’d be happy to consider bright, outgoing students of this program for final placements in our organization... - Krishna Kumar
(CIO, Sundaram AMC)

Mentor Profile

Sujit Bhattacharyya

Chief Innovation Officer (CIO), Career Launcher

Sujit holds a bachelor of technology degree in electrical engineering from IIT, Kharagpur and a post graduate diploma in management from IIM, Bangalore. Adept at Digital Marketing, Search Engine Optimization and Social Media Marketing, he has a keen interest in tech, AI-ML & Data Sciences.

LinkedIn profile

FAQ

Thanks to the media, most of us are aware of the promise and the potential of AI. We even have a somewhat good idea of what it is being used for today. But as the development of artificial intelligence progresses it is no longer something that we can just appreciate from afar. While AI is expected to one day be able to replace all of our jobs, the more immediate impact is in its ability to analyse and bring meaningful insights and predictions from the vast stockpiles of data available to companies today. The sub domain of artificial intelligence that deals with this field is machine learning. The biggest differentiator going forward for businesses will be in their mastery of the possibilities and limitations of machine learning.

Unlike other innovations in the past, being on top of the machine learning revolution is not as straightforward as having machine learning specialists in every company. Everyone will need to be well versed in the application of machine learning. Just as one cannot in this day and age function without being able to read and write, in tomorrow’s world machine learning will become the new literacy. According to the World Economic Forum1, over half of the jobs in the world will be impacted by artificial intelligence in the next five years and many of those people will need to be reskilled.

Along with the great advances of machine learning came the democratization of machine learning. A vast array of powerful tools are available for everyone and they are free to use free to take apart, learn from and improve upon. It is now easier than ever before to get your toes wet in machine learning. No longer is it solely the domain of the upper echelons of mathematicians and computer scientists. Not only can anyone learn machine learning, everyone should learn it.

The bar of entry to become proficient in machine learning is surprisingly low. You can start today with a knowledge of grade school mathematics, a computer and a willingness to learn.

Should I learn Online ? While online learning is the dominant platform to learn machine learning today, it’s not for everyone. While many, including our in house data science team have learnt from online courses, we feel it is inferior to the experience of classroom coaching. One needs a very strong drive to complete an online course as evidenced by the very high dropout rates among the viewers of Andrew Ng’s videos. There is also little to no recourse for doubt clarification if you are stuck with something.

There are many applications of machine learning to sales roles. For example machine learning can be used to interpret and gain meaningful insights from customer data. Having a data driven understanding of your customer base is gives your company an edge in making marketing decisions as you can know what your customers want with data rather than intuition. Forecasting is another sales task that is falls squarely in the domain of things machine learning excels at. Given enough data, machine learning models can make very accurate sales forecasts with little human intervention or effort. Sales communication is another forte of machine learning as many simple to moderately complex enquiries about sales or promotions can be handled by AI chatbots or machine learning enabled email reply bots leaving the humans to handle the more creative and complex tasks. Machine learning can also be employed to track the discussions and opinions of your brand on social media and even make intelligent responses to maintain a positive atmosphere around the brand.

There are a number of HR functions that can either be enhanced or done entirely by AI. Attrition detection is one of the most important functions of HR, understanding why employees stay at or leave a job is a good job for machine learning. Given the data, machine learning algorithms may find patterns and insights into employees decisions that HR professionals may overlook. This data can then be used to make corrections in HR policies. Processing prospective employees is another major task. It requires HR professionals to dig through a vast number of CVs to find the right candidate. Often times the best candidates get offers elsewhere before you can get a chance to see his or her resume, so speedy turnaround times are also necessary. Machine learning algorithms make combing through large number of resumes an easy task so you can get the perfect candidate before everyone else.

Definitely! While a background in mathematics or programming will no doubt be an invaluable asset to a data scientist, one does not need it to apply machine learning to their businesses. The most essential ingredients in becoming a good data scientist are creativity and a mastery of the data.

Contact Us

Have questions? Our data science team is more than happy to take them:-

Tathagat Jha - tathagat.jha@careerlauncher.com
Mythreya Lingala - mythreya.lingala@careerlauncher.com

At a glance

  • 2 Days; Feb 7&8
  • Offline
  • Hands-on & project oriented