ML professionals are highly sought after in today’s increasingly automated industries. Thus, machine learning certifications are also gaining popularity. Machine Learning is one of the fastest-growing industries today. Many graduates and undergraduates aspire to build a career in machine learning due to such incredible growth. The global market of Machine Learning is estimated to reach $117.19 billion by 2027. It shows how promising a career in machine learning could be.
If the machine learning field interests you, nothing should stop you from getting into the field and building a bright career. After graduation, you can start by enrolling in a machine learning course.
Also, discover the different career options in Machine Learning. After knowing all the job options, you can decide which is right for you and work accordingly.
Machine Learning is a branch of computer science and artificial intelligence. The field involves building models and techniques that allow machines to learn and improve their functions autonomously.
Machine Learning is one of the fastest-growing technologies, which enables machines to learn automatically from past data and perform specific tasks. Some most common examples of ML-based technology include speech recognition, face recognition, email filtering, recommendation engines, etc.
Machine Learning is a versatile field having applications in almost every industry. Along with ML engineering, there are many jobs available in this field. The following are the top career options in the Machine Learning field:
ML Engineer is one of the highest-paying jobs in machine learning globally. Machine Learning engineers design and implement ML algorithms and models. They must work closely with software engineers and data scientists to build machine learning pipelines and train models.
You need a strong knowledge of Python, Java, R, and core mathematical concepts to become an ML engineer. Mathematical concepts will include calculus, statistics, and linear algebra.
Fresh graduates with a background in mathematics, statistics, or computer science, can pursue a career as an ML engineer. Those who are from other backgrounds can learn these subjects by enrolling in a machine learning course.
AI Researcher is another career option to consider in the machine learning field. They advance the knowledge and capabilities of artificial intelligence by creating new algorithms and techniques. Their primary task is to perform research on natural language processing, deep learning, computer vision, and other areas of AI.
After researching, they must develop and test new AI algorithms businesses can use to automate their tasks. Having a background in mathematics, physics, or computer science can help you build a promising career as an AI researcher.
Businesses are using robots to perform various tasks today. To build these robots, companies need robotics engineers. The responsibilities of a robotics engineer include designing, building, and testing robots that can perform specific tasks. Robotics engineers use ML algorithms to enable the robots to learn from their surroundings and make the right decisions.
Robotic engineers also ensure that the robots are functioning safely. Maintaining these robots and implementing modifications (whenever required) are also among their major responsibilities. If you have a background in mechanical engineering, computer science, electrical engineering, etc., you can pursue a career as a robotics engineer.
Natural Language refers to the regular language and conversational tone that humans use to converse with each other. NLP (Natural Language Processing) Engineers build algorithms and models to make computers understand human language. They build NLP models using machine learning algorithms and train them using massive amounts of data from multiple sources.
Some common examples of NLP-based models include chatbots like ChatGPT, and virtual assistants like Alexa, Siri, etc. Fresh graduates with a background in mathematics, computer science, or linguistics, can opt for a career as an NLP engineer.
Computer Vision Engineers are responsible for developing techniques and algorithms through which computers can understand images and videos. They must work with image analysis algorithms and deep learning architectures to enable computers to identify objects, images, faces, and track movements.
The most popular applications of Computer Vision include facial recognition, robotic automation, self-driving cars, etc. If you have a background in computer science, electrical engineering, mathematics, or other quantitative subjects, you can pursue a career as a computer vision engineer.
A machine learning developer is responsible for developing the script for a machine learning model that ML engineers build. They must work closely with the ML engineering team to build ML algorithms and models for various purposes.
The role of ML developers coincides with that of ML engineers to a great extent. Many companies only hire ML engineers to perform the task of Ml developers as well, or vice versa. You can pursue a career as an ML developer if you have a background in mathematics, engineering, computer science, statistics, etc.
Data Engineers design and develop data pipelines to process and analyse massive datasets. They must work with software engineers and data scientists to make sure that enough data are available for analysis.
Responsibilities of a data engineer in an organisation include:
Data Engineer is one of the popular jobs in machine learning in India due to the high demand for professionals in the industry.
Deep Learning Engineers work on the design and development of deep neural networks. These networks aim to solve complex problems that an average AI/ML algorithm cannot. They train ML models using huge amounts of data, optimise hyperparameters, and improve the accuracy of models.
Deep learning engineers work with software engineers, data scientists, and other ML professionals to build new systems that can perform specific tasks. Graduates in programming, mathematics, or engineering can consider this career option.Also Read: Artificial Intelligence: Yes, machines are going to steal your jobs
Machine Learning is considered one of the best career options in the 21st century. The scope of machine learning is not limited to one or two sectors, but it has applications across various industries. From healthcare to banking and the e-commerce industry, almost every business is using ML algorithms to make their business processes more efficient.
With this, the demand for skilled machine learning professionals is continuously on the rise. If you have the necessary skill set to fulfil the requirements of these companies, you can easily secure a high-paying job.
The best way to start building a career in machine learning is to enrol in a machine learning certification program. A well-structured course will keep you organised and develop your core skills first.
At Edvancer, you can choose from the following three industry-recognised courses in machine learning:
These courses cover all the necessary topics under machine learning. Moreover, you can also gain practical experience by working on real industry projects. One of the biggest advantages of these ML courses by Edvancer is that you get to choose one of the two learning options (self-paced and live classes) at your convenience.
The salaries of machine learning professionals depend on various factors including the designation, the industry, experience, location, etc. Generally, machine learning professionals get a decent starting salary.2. Is there a demand for machine learning in India?
Yes, machine learning is highly in demand in India. As more and more businesses are using automation, the significance of machine learning is continuously rising.Share this on