Machine learning training for faculty is a hands on and results oriented training program for lecturers and professors. We have flexible options like long term courses and short and intensive workshops at your premises.
Intel® is our software and hardware partner for all of our data science programs. Intel® provides several optimized libraries including special distributions of Python, Keras and Tensorflow that make machine learning algorithms fly on Intel® hardware.
We are currently offering courses that can be tailored to fit the constraints and unique industry problems of your industries. You will get access to a team of experts who will for guidance and support and a vast amount of proprietary machine learning content for learning along with unique data and problems tailor made for your company and industry.
As in Karate, the colour of the belt can easily judge about the rank and the level of expertise of any ML professional. White represents the entry level while Black represents the true expert at the highest rank. Yellow, Orange, Red, Green, Blue and Brown are the intermediate colours representing the growing levels of expertise.
White belt : (14 hours - Starter)
Usually, this is given at the end of a 14-hour program that covers the basics of statistics, understanding of python and solving some open data problems as exercises. This belt depicts you as a person who has started to learn Machine Learning.
Green Belt : (35 hours - Professional)
A 35-hour program comprising sound understanding of all three elements of knowledge of math/statistics, comfort with handling python code and voice-bot coding gets you to be a GreenBelt. A 75pc assessment score is a pre-requisite for Green Belt certification. This program is delivered over a 5-day period and also counts for the in-service training in govt institutions.
Brown Belt : (60 hours - Expert)
Usually, this level of expertise signifies that you have reached the half expertise level by expanding knowledge, learning new techniques, submitted capstone/real world problems with adequate levels of success. About 60 hours of coaching and learning with mock/masked real data and over 75 percent in assessments earns you a Brown Belt. From an academic benchmarking stand-point this is equal to completing 6 credit course.
Black belt : (120 hours - Master Certification)
This colour signifies that you have mastered all the skills enough for the machine learning world to consider you her master. An evidence of your expertise in machine learning comes from organisations and institutions seeking your guidance to solve their real world problems. From an academic institutions stand-point, this Blackbelt Master certification program would be equivalent to 12-credit points in engineering or Management.
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.
We can split AI and ML as a very important course in our curriculum; and this type of workshop is just a start. If we have this type enthusiastic workshop and create similar culture among our other faculties, students, etc., then India will become No.1 in AI & ML very soon.
In India, it is a healthy time to absorb the Industry 4.0 phases like AI, ML, Block Chain, Data Science, IoT, etc. AICTE is now stressing upon to spread this information across the country. First, we need to develop the confidence among our teachers in these areas, and then only the knowledge can be transferred. Thus, we are partnering with CL Educate Ltd, who has really helped us train on these subjects.
The workshop on Machine Learning organized by AICTE on February 7-8, 2019 at their HQ was even better than I had imagined. Sujit Bhattacharyya is great as a presenter; and has sound knowledge in Machine Learning. He beautifully explained predictive analytics, using different real-life examples. The hands-on sessions with Python, Pandas, Matplotlib were excellently demonstrated.
AI, ML, and DS are the parts of Industry 4.0. This first batch of ML workshop conducted by AICTE at their HQ is very strong, diverse, and rich. The excitement, excellent inputs of the participants encourage us to take this initiative to different cities, states; train few thousands of faculty members; and accelerate the real-world problem-solving capabilities to them and students on their campuses.
The AI/ML course participants were very motivated and remained focused on the program throughout both the days. I am very happy that each and every one of them were able to solve the numerous problems we had provided as a part of the course. They scored well in the assessments; and showed high enthusiasm in the class. The unique hands-on design of the program was validated by the excellent response from the teachers.
The sessions were a good mix of concepts, practical exposure, meaningful discussions, and hands-on problem-solving. Mr. Bhattacharyya was instrumental in making someone like me, who has no background in Machine learning, understand the nuances quite easily. Everyone at the workshop were discussing his ability to use real-life examples while explaining concepts. Such workshops help immensely in absorbing the knowledge on ML; and share it with others
Very meticulous in his preparations and articulate in his communication, Sujit sir has the ability to instill confidence and comfort in his students. He is a teacher par excellence!
It was a very nice session organized by AICTE and CL Educate Ltd. We learnt a lot. The way Sujit sir and his team described Alexa with a live practical session was awesome. Actual machine learning implementation was discussed in practical terms, which is rarely done in such workshops.