Machine Learning (ML) is an application or a subset of Artificial Intelligence (AI). It is among the most powerful and highly influential technologies today. This technology helps instill the ability to learn and function in machines and computer systems without the need for any human supervision.
What is Machine Learning?
Machine Learning allows systems to learn and improve from past data and experiences without having to be programmed. It generally focuses on the overall development of numerous computer programs to make them capable of accessing and processing large amounts of business data using Machine Learning techniques and Deep Learning algorithms, a significant concept, and a subset of Machine Learning.
Machine Learning Process
Machine Learning helps machines analyze and identify patterns from historical datasets, and on the basis of that data, these systems learn to make classifications and decisions without any human supervision. Similar to how we humans learn from our experiences and make better and improved future decisions, the Machine Learning technique trains computer systems to gain knowledge from the pre-existing data to perform better in the future.
When compared to the old and traditional programming methods, Machine Learning differs in a lot of ways. In the case of traditional programming, it required humans to feed necessary data into the system and build codes and programs to transform this data into the required output. This traditional method requires a lot of human involvement and is also limited to the human abilities of structuring data and defining it.
In Machine Learning, on the other hand, we need to feed data into the system, and on the basis of the earlier fed data, the system builds up its logic to get the desired result. In this process, the system is developed to learn without any human intervention or guidance.
Almost every industry makes use of Machine Learning concepts and its applications. It is adopted at a wide range by social media networks to build an enjoyable and more customized user experience. However, Machine Learning has other practical applications as well in the field of telecommunication, video surveillance, healthcare, and more.
As per McKinsey, a leading firm in consulting, Machine Learning will generate more than US$100 billion annually in just healthcare.
Why should you learn Machine Learning?
In this digital era of Machine Learning and Artificial Intelligence, at every turn, you will see these technologies surrounding you. For economic and technological growth, the future will be filled with AI devices around the world. Due to this, numerous companies across all industries are adopting these technologies. Thus, they will require ML and AI professionals who can help them implement these and become technologically advanced.
As per the Machine Learning Market Research report, this technology is predicted to grow over US$8.81 billion by the year 2022 with a compound annual growth rate or CAGR of about 44.1%.
Without even noticing, we pass by several applications of Machine Learning on a daily basis. From this, we can gather that we are so consumed and used to these technologies that they have become an essential and regular part of our day-to-day lives.
Over the years, Machine Learning has seen exponential growth. Hence, there is a high demand for these professionals in numerous organizations who have the skills and ability to implement Machine Learning in their systems and use it in the most profitable and effective manner. This technology has become extremely important and essential in enriching our lives which is why organizations are filling their IT positions with professionals who have knowledge and experience in Machine Learning.
Before you read about how to become a Machine Learning Engineer, let’s understand who these professionals are and what they do.
You must also check out this comprehensive Machine Learning Tutorial to know more!
Who is a Machine Learning Engineer?
According to the Machine Learning Engineer definition, these professionals are programmers who build systems and machines that have the ability to learn as well as apply the gained knowledge without being specifically programmed. They develop codes and programs that allow machines to take necessary actions in certain situations.
Let’s now learn in detail about what these professionals do and how they do it.
What does a Machine Learning Engineer do?
Machine Learning Engineers generally work with Big Data. They mainly feed the business data into models that are designed by Data Scientists. Further, they are also responsible for adapting various theoretical models of Data Science and sending them for production such that they can handle large amounts of real-time data.
Now, let’s get into how you can become a successful Machine Learning Engineer.
How to become a Machine Learning Engineer?
To become a Machine Learning Engineer, you need to have certain skills and also understand the various roles and responsibilities of the professionals in this field. Here are some of the must-have skills and the roles and responsibilities of Machine Learning Engineers that you can refer to.
To learn more about this technology, register for our machine learning courses in Hyderabad and become proficient in it!
Machine Learning Engineer Skills
Some of the necessary skills you need to acquire to become a Machine Learning Engineer are as follows:
- Experience in programming languages, namely, C++, Python, Java, or R
- Understanding of probability and statistical concepts
- Knowledge of applied mathematics, along with algorithms and models
- Basic understanding of distributed computing
- Work experience in at least one UNIX tool
- Knowledge of Hadoop and its numerous applications
Now, let’s discuss the various roles and responsibilities of a Machine Learning Engineer.
Roles and Responsibilities of a Machine Learning Engineer
Some of the major roles and responsibilities of Machine Learning Engineers include:
- Making use of the fundamentals of computer science such as algorithms, data structures, complexity, and computer architecture
- Using numerous mathematical skills to perform calculations and computations, as well as to work with algorithms based on programming
- Producing project results and also isolating resolution-requiring problems to bring more effectiveness in codes and programs
- Collaborating with data to develop various data model pipelines
- Managing the data pipelines and infrastructure to produce the code
- Creating algorithms on the basis of various statistical models and processes
- Making use of data evaluation and data modeling strategies to identify patterns and depict future instances
- Applying relevant Machine Learning libraries and algorithms
- Analyzing large and complex sets of structured and unstructured data to gain insights and decide techniques
- Researching and further implementing the latest techniques and practices to improve the organization’s infrastructure
Top 10 Machine Learning Engineer Jobs
There are numerous job opportunities and Machine Learning Engineer career paths available for professionals in this domain. Leading organizations from all over the world hire these professionals to get a step ahead in technology and business and compete with the current digital market trend. Some of the highest-paid and most popular Machine Learning jobs are as follows:
- Machine Learning Engineer
- Lead Machine Learning Engineer
- Data Scientist
- Research Scientist
- AI Engineer
- AI and Deep Learning Researcher
- Software Engineer
- Machine Learning Scientist
- Senior Software Engineer
- Machine Learning Expert
Gartner predicts that, by the end of 2020, there will be more than 2.3 million new career opportunities available just in the field of Machine Learning.
Future Scope of Machine Learning Engineers
Machine Learning will help you and your organization automate redundant and mundane tasks that do not necessarily require human efforts. This is why most organizations are implementing this technology and are looking to hire experts in this domain who can help them improve their business.
In this blog, you got in-depth knowledge about the various skills that you need to acquire to become an ML Engineer. You also learned about the job roles and responsibilities of the ML professionals and the job opportunities available in this profession.