Guru Tegh Bahadur Institute of Technology +91 011 455 999 00
Admission 2022-23 : : Notices / CircularsLast Date for Choice Filling is 27 August 2022

Artificial Intelligence Machine Learning (AI-ML)

Machine Learning is one of the promising and hottest career choices today as there is an urgent need for professionals who are trained in Deep Learning and AI jobs and match machine learning requirements. Both Data Science and Machine Learning are generating more jobs than candidates right now, making these two areas the fastest growing tech employment areas today. As per the recent report from Gartner, AI will create 2.3 million Machine Learning jobs by 2020. If you want to be one of those professionals, prepare yourself by getting certified and industry-ready because the sooner you get your training started, the sooner you will be working in this exciting and rapidly changing field.

These jobs in demand include research and development of algorithms that are used in adaptive systems across Amazon. ML scientists build methods for predicting product suggestions and product demand and explore Big Data to automatically extract patterns. Companies recruit for positions like Machine Learning Engineer, Machine Learning Analyst, NLP Data Scientist, Data Sciences Lead, and Machine Learning Scientist. The companies hiring Data Scientist and Machine Learning Scientist are Amazon, Deloitte, Google Uber, Microsoft, Apple etc. .

Salary Structure:

ML jobs for freshers may vary between 5- 9 LPA. With a sound knowledge of data analysis and algorithms and a few years of experience, you may expect a package of 10 LPA per annum. From smart phones to chat bots, demand for these jobs is at an all-time high, so it is just the right time to get in on the ground floor of a growing industry.

~ Responsibilities As a machine learning engineer, you'll need to:

  • Understand and use computer science fundamentals, including data structures, algorithms, computability and complexity and computer architecture.

  • Use exceptional mathematical skills, in order to perform computations and work with the algorithms involved in this type of programming produce project outcomes and isolate the issues that need to be resolved, in order to make programmes more effective

  • Collaborate with data engineers to build data and model pipelines Manage the infrastructure and data pipelines needed to bring code to production

  • demonstrate end-to-end understanding of applications (including, but not limited to, the machine learning algorithms) being created.

  • Build algorithms based on statistical modeling procedures and build and maintain scalable machine learning solutions in production.

  • Use data modeling and evaluation strategy to find patterns and predict unseen instances.

  • Apply machine learning algorithms and libraries lead on software engineering and software design.

  • Communicate and explain complex processes to people who are not programming experts.

  • Liaise with stakeholders to analyse business problems, clarify requirements and define the scope of the resolution needed analyze large, complex datasets to extract insights and decide on the appropriate technique

  • Research and implement best practices to improve the existing machine learning infrastructure

  • Provide support to engineers and product managers in implementing machine learning in the product.

  • Emphasis is given to:

    Development of live projects by the students and improving the programming skills of students.

    Strengthening of research and project development.

    Faculty development and training in latest softwares like Rational Rose,, Data Warehousing and Mining Packages etc.

    To impart quality engineering education to the students by providing effective teaching learning, research and application based innovative environments.

    The department takes immense interest in conducting professional activities such as organizing workshops, seminars and expert lectures to meet the challenges in the IT industry.

    This department has strong Computer Labs in the form of state-of-the-art IBM computers, with the latest softwares and internet facilities. Student computer ratio is 1:1.