In recent years, the use of Artificial Intelligence (AI) and Machine Learning in every possible life sphere has been a highly relevant topic. Companies around the world are investing billions of dollars into the quest for exploring newer avenues where machine learning can be applied. From the Google maps route assistant to the Alexa in our homes, the applications of machine learning are so ubiquitous that we hardly realize it. So, what is machine learning all about? And how does one pursue a career in machine learning in India?
What is it like to build a Career in Machine Learning?
Machine learning is one of the branches of Artificial Intelligence. It is aimed at enabling computers to learn from experience and perform such tasks that they were not originally programmed to do. Essentially, it allows computers to think like humans. Through machine learning, computers use statistics and algorithms to process data. From the raw data that the computers are fed, they then learn on their own and perform functions like predictions and analytics, which were not included in their initial code. In sum, machine learning focuses on allowing computers to enhance human capacity by automating several daily operations.
Machine learning is applied in every field that one can think of. It is responsible for, inter alia, classifying our spam emails, the face recognition in our smartphones, voice assistants like Siri and Alexa, video recommendations in YouTube, financial fraud detection, predictive financial analysis, and portfolio management. As we all know, technology is invading every possible facet of life and its extensive use instigates many tech-minded people around. which is eventually probing their interest to build a career in machine learning.
What is the Scope of Machine Learning in India?
Research by firms around the world corroborates the fact that machine learning is set to revolutionize the way our world operates. Gartner, a multinational research and consultancy company, reports AI and Machine learning alone will lead to the creation of a mammoth 2.3 million jobs in 2020. Accenture predicts that AI has the potential to add $957 billion to the Indian economy by “changing the nature of work and creating better outcomes for businesses.” The report titled ‘Rewire for Growth’ shows that AI innovation can increase India’s annual growth rate of gross value added by 1.3% points. So much so that PricewaterhouseCoopers found that the chances of “complete automation” in the next 5 years are 38% and 31% in the manufacturing and finance sector respectively. Surely, these figures highlight that building a career in machine learning is a fruitful move.
Furthermore, companies PAN-India have invested in machine learning in order to optimize, standardize, and scale-up their operations across departments. Machine learning offers an unparalleled edge in that it enables firms to develop long-term predictions and plans, create newer products or augment existing technology, and democratize large volumes of data. Neural networks, natural language processing, and deep learning are all sub-fields of machine learning that are becoming both increasingly relevant and popular by the day.
Machine learning has applications in the following fields:
- FinTech
- EdTech
- Computing and Software
- Product and Software development
- Supply Chain Management
- Wearables and IoT
- Healthcare
- Insurance
- Investment Banking
The above research begs the question: is machine learning the future? From what current trends show, machine learning is certainly at the forefront of the upcoming technological paradigm, and any industry that fails to recognize the tremendous potential that this field brings with itself is bound for obsolescence. Just a decade ago, it would have been inconceivable to think that there would exist a human-robot called Sophia. Today machine learning allows us to use robots in surgeries, manufacturing processes, and even COVID-19 testing and quarantine enforcement.
Computing skills, which are one of the foundational skills used in machine learning, will become increasingly valued in the years to come. The New Education Policy (NEP), 2020 makes it mandatory for students to learn coding after class 6—a clause that speaks volumes for what the government believes the future holds. Today, the technology around us has unprecedented cognitive skills. It aims to constantly learn, unlearn, and relearn to provide a better and more personalized end-user experience. If one is to open YouTube on two different smartphones, for instance, no two people will have the same video recommendations. This difference is because each of us has a distinct identity and through machine learning, it is this very individuality that technology seeks to emulate.
Prospects of Building a Career in Machine Learning
Machine learning has extensive applications in every industry that exists. Following are some of the career prospects that machine learning offers:
- Machine Learning Engineer: A machine learning engineer designs and implements complex machine learning algorithms. Algorithms help recognize the complex patterns in the huge volumes of data through clustering, anomaly detection, classification, and prediction to address business challenges.
- Data Engineer: Data engineers are credited with the job of processing an organization’s big data. They construct, test, and maintain complex data management systems, in addition to customizing analytics applications. Data engineers must be familiar with NoSQL, SQL, Hadoop, MySQL, Cassandra, MongoDB, MapReduce, Hive, and Data streaming.
- Data Scientist: This role involves designing programs to process large amounts of unstructured data. Through the analysis, data scientists suggest methods to frame optimal future strategies. Data scientists are also proficient in R, SAS, Python, SQL, and Spark.
- Data Analyst: A data analyst is required to use Maths, statistical science, and programming to derive meaningful insights from raw data. This includes developing advanced algorithms to extrapolate data, using ETL tools, Hadoop-based analytics, and business intelligence concepts for data visualization and warehousing.
- Automotive Industry: Self-driving cars employ extensive machine learning mechanisms to automate the driving process. Machine learning algorithms, IoT sensors, high-definition cameras, and natural language processes are fundamental to the functioning of these cars.
- Stock Analyst (FinTech): The predictive capabilities of machine learning algorithms foster their use in the FinTech industry. The algorithms recognize patterns in the financial markets and help investors in deciding where they should invest their money.
- Marketing: Companies are always interested in analyzing and predicting consumer behavior. Machine learning algorithms allow companies to analyze consumer activity online or in-store. This data is then used by companies to improve their go-to-market strategies for new products, and targeted marketing outreach for existing products.
- Application/ Product Development: Wearable technology products like Google glasses, Fitbit, et al are all investing heavily in machine learning in order to provide a personalized consumer experience. Even the EdTech sector is employing machine learning to provide students with a personalized learning experience.
How to start a Career in Machine LearningHow to start a Career in Machine Learning
It goes without saying that mathematical, statistical, and computing proficiency is necessary for building a career in machine learning. However, executives around the world emphasize the importance of excellent communication skills for success in this upcoming field. R, C++, Java, Python, and other computer programming courses will be beneficial for people wanting to start early. Even more so, a business acumen, data preparation, visualization, and organization is essential for making a career in machine learning. It is noteworthy that these skills must be acquired through adequate hands-on experience. A strong academic underpinning, coupled with a diverse work experience in real-world problems and applications is the ideal way forward in this sector.
Of late, many Indian colleges have introduced newer undergraduate courses under B.Sc. and B.Tech. that offer specialization in machine learning and AI in the final year. A majority of these courses are in association with corporate giants like IBM, Tata Consultancy Services, et al. For a student who is certain to craft a career in machine learning, an undergraduate course is a great head start into the field. On the other hand, an undergraduate degree in Computer Science, Data Science, Data Analytics, Statistical Science, Mathematics, Economics, or even Management can be a stepping stone to an eventual career in machine learning.
Premier institutions around the world and in India offer machine learning courses at the postgraduate level. These courses combine Data Science, Cybersecurity, Financial and Business Analytics, Health Analytics, New Media Sense, and Statistical Science to provide a well-rounded curriculum. Apart from these conventional paths, MOOCs on Coursera, Udemy, and the like can be considered to whet one’s interest and skill in machine learning. These courses offer the added advantage of varying levels of difficulty and personalized learning sessions.
Conclusion
No one could have imagined that our machines would one day begin to ‘learn’, or that our phones would become ‘smart’. Truly, the technological landscape is evolving like never before. The world is yet to experience the full potential of AI and machine learning, and for a generation that is at the forefront of this revolution, it becomes necessary to be abreast of recent developments. There is only one way to succeed in machine learning, which is to keep improving every day; for, as Van Loon (CEO, Intelligent World) puts it, “AI never stops learning, so you can’t either.”
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