Undeniably emerging as one of the most influential and powerful technological advancement today, Machine Learning has paved the way from information to knowledge. In the last 50 years, we have witnessed an explosion of data. This ginormous data is jargon, until we learn to analyse and find recurring patterns hidden within. This is where the wide subject of Machine Learning comes in – it identifies the underlying patterns within complex data that humans are incapable of understanding. These hidden patterns can be used to predict future events and perform all kinds of complex decision-making, without human interference. In other words, it is ‘the study of computer algorithms that can improve automatically through experience and the use of data’.
Relevant to our daily lives
We are probably unaware of the fact that we are already using Machine Learning while performing our daily activities. It is the driving mechanism whether we are looking for information on Google or any other search engine, using an application to listen to songs and even for online shopping. It is constantly “learning” and “improving” from every single interaction that it has with us, from showing us a possible list of search options, or guessing the mood for our music playlist or even displaying options for e-commerce. Additionally, Machine Learning is also the technology associated with world-changing inventions like detecting cancer, creating new life-altering drugs and self-driving vehicles.
Offering ample Career opportunities
Specializingin Artificial Intelligence and other disruptive technology can lead to successful careers as Data Analysts, Data Scientists as well as Machine Learning Engineers. While a data analyst works with enormous amounts of data and produces actionable insights, a data scientist is a creative researcher who carries out experiments with data and models. Machine Learning Engineers on the other hand are ‘tech specialists’ who design, maintain and upgrade AI systems on which these models operate. They have to develop an all-round understanding of the entire ecosystem that they will be designing the application for. For example, if working for a clothing chain, if the company wants to generate targeted coupons according to previously purchased items, the engineer has to collect the historical data, perform the analysis, figure out trends and then propose strategies. The machine learning approach would then be used to write an automated coupon generation system. To make the algorithm work, the engineer also has to understand the whole ecosystem of technology vis-à-vis the inventory, catalogue, pricing, purchase orders, bill generation, Point of Sale software used as well as the CRM software.
Pre-requisites for a career in Machine Learning
- Computer Science: Having sound knowledge of data structures, coding, algorithms and computer architecture are a must. The engineer must be able to apply these technologies to use and even troubleshoot if there are errors.
- Mathematics: A clear understanding of statistics and probability is mandatory. Understanding of concepts viz. conditional probability, Bayes Rule, likelihood, independence and techniques derived from it are of utmost importance.
- Data modelling and evaluation: The idea is to discover the hidden patterns in a sea of data. Using correlations, clusters, eigenvectors and predicting properties of previously unseen instances with classification, regression and anomaly detection are an integral part of the job.
- Machine Learning Algorithms and Libraries: Implementations of Machine Learning algorithms through libraries, packages or APIs such as Theano, Spark MLlib, H2O, TensorFlow and applying them effectively is an important skill. Understanding how to fit the data with linear regression, gradient descent, genetic algorithms, bagging, boosting and comprehending hyper-parameters is also another critical aspect for a successful career as a Machine Learning engineer.
- Software Engineering with System Design: Since the responsibility of the engineer is to provide an Artificial Intelligence software that automatically derives user behaviour, understanding of software and systems is very vital. Communicating with the modules of the programme is also of paramount importance.
We stand for quality education
The NAAC-accredited and MAKAUT-affiliated OmDayal Group of Institutions is responsible for churning out young engineers equipped to face the ever-changing landscape of disruptive technologies such as Machine Learning. The industry-centric engineering courses offered by us include:
- Civil Engineering
- Electrical Engineering
- Mechanical Engineering
- Computer Science and Engineering
The niche training and skills required for bagging engineering jobs both in India and abroad has inspired us to continuously upgrade the teaching and learning curriculum in sync with global standards. We empower our students with a competitive edge and required aptitudes to excel in the new-age technology spectrum. Our facilities include flexible timings and partnership with National Small Industries Corporation (NSIC) to provide on-job training and relevant placement opportunities.
For more information, visit www.omdayal.com.