5 min
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With big companies dealing with ever-increasing amounts of data, the applications of machine learning are growing across industries. As AI-driven solutions become a high priority and key differentiator in enterprises, these companies are increasingly looking for Data Scientists and ML Engineers to translate business objectives into valuable AI-based applications to help them achieve their core strategic objectives. Consequently, machine learning has emerged as one of the hottest areas for IT professionals to upskill themselves.
Currently, there is a need for Data Scientists, Data Analysts and Machine Learning engineers to build effective and scalable ML solutions across search, recommendation etc. and improve product outcomes. In addition to this, most companies need Data Scientists to develop products and applications, oversee AI development in teams and push the boundaries of AI techniques and technology.
Even though leading tech behemoths have open-source tools like AutoML that take the heavy-lifting out of machine learning and come with pre-built algorithms, there is always a lack of understanding about how data is used in algorithms. When it comes to the kind of skills that these companies are looking for, having a working knowledge of Python or the tools to make data work aren’t enough to get one up to speed in fast-paced teams. So, how does one learn the science behind implementing complex algorithms and building models that can be put into production?
Image Source: Great Learning[/caption]
With big companies dealing with ever-increasing amounts of data, the applications of machine learning are growing across industries. As AI-driven solutions become a high priority and key differentiator in enterprises, these companies are increasingly looking for Data Scientists and ML Engineers to translate business objectives into valuable AI-based applications to help them achieve their core strategic objectives. Consequently, machine learning has emerged as one of the hottest areas for IT professionals to upskill themselves.
Currently, there is a need for Data Scientists, Data Analysts and Machine Learning engineers to build effective and scalable ML solutions across search, recommendation etc. and improve product outcomes. In addition to this, most companies need Data Scientists to develop products and applications, oversee AI development in teams and push the boundaries of AI techniques and technology.
Even though leading tech behemoths have open-source tools like AutoML that take the heavy-lifting out of machine learning and come with pre-built algorithms, there is always a lack of understanding about how data is used in algorithms. When it comes to the kind of skills that these companies are looking for, having a working knowledge of Python or the tools to make data work aren’t enough to get one up to speed in fast-paced teams. So, how does one learn the science behind implementing complex algorithms and building models that can be put into production?
Great Learning PGP in ML
Great Learning’s PGP in Machine Learning is an exhaustive 7-month learning program delivered in two formats — Classroom with online content or Online with weekend mentorship sessions. The program focuses on providing learners with skills that make them job-ready through a series of 8 hands-on projects and a Capstone project guided by a mentor. The program curriculum has been co-created with leading industry leaders from Flipkart, IBM and Zensar Technologies to ensure it is industry-relevant. The program is delivered in collaboration with experienced industry professionals working in organizations— Cognizant, MuSigma, Accenture, Genpact, American Express and Fybr among others to ensure the curriculum is industry-oriented and students can benefit from expert mentorship. The exhaustive program is helmed by India’s best machine learning faculty with leading names like Prof. Abhinanda Sarkar, Prof. Mukesh Rao, Dr. P. K. Viswanathan among others from Great Learning capturing concepts from the entire machine learning cycle, from the foundation to ML techniques (feature extraction, model selection & tuning) to concepts of Deep Learning. Each module is followed by an hands-on project so that students can easily apply what they have learned in the classroom. Candidates will get trained on the latest Machine Learning techniques, popular Python libraries like Pandas, Numpy, Scipy, Matplotlib & Seaborn for data visualization. The program is designed in such a way that a beginner or an IT professional can take it up and embark on a new career in Machine Learning upon program completion. Talking about the program experience, Sravan Malla, Senior Analyst - AI/ML Engineer, Accenture shared the following “ I had just three and a half years of experience when I took up this program but the way course is defined suits all levels of experience. The professors who teach the machine learning program are highly experienced academicians and industry practitioners. Later when I was on the verge of completing the course, I had multiple job offers in the field of Data Science and Machine Learning from a couple of companies. I have accepted the role and decided to work with Accenture as a Sr. Analyst – AI/ML Engineer which looked more promising and was aligned to my career goals. I firmly want to say that I came to the right place and my career goals have been fulfilled because of the proper strategy, plan, course structure, and the excellent faculty which Great Learning had in place.”Hard Facts
Format
- Blended: This format features 120 hours of classroom learning, coupled with 100+ hours of online content. Classroom sessions will be held in 6 metros - Bangalore, Mumbai, Chennai, Gurgaon, Hyderabad & Pune
- Online: This format features 50 hours of self-learning aided by assessments and quiz and these learning sessions are accompanied by online mentorships over the weekend to help clear doubts and provide industry exposure.
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