Machine Learning is the trendiest career right now, and its prominence is only growing! Machine Learning is also having an impact on almost every other industry, including Quantum Computing, Healthcare, Finance, Robotics, Agriculture, and so on.
“What exactly is Machine Learning?”
Machine Learning is the use of Artificial Intelligence to enable machines to learn a task from experience without being specifically programmed for that task. (In a nutshell, machines learn without human intervention!) This process begins with feeding them good quality data and then training the machines by building various machine learning models using the data and various algorithms. The algorithms we use are determined by the type of data we have and the task we are attempting to automate. If you’re interested in data science, automation, and algorithms, machine learning is the path to take.
Reason to pursue a career in machine learning is that there are numerous career paths available to machine learning engineers in the industry. You can get a high-paying job as a Machine Learning Engineer, Data Scientist, NLP (Natural Language Processing) Scientist, Business Intelligence Developer, or Human-Centered Machine Learning Designer if you have a background in machine learning.
Machine Learning Engineer is one of the top jobs in the world in terms of salary, job growth, and overall demand. People with machine learning skills are in high demand and in short supply, which helps to explain why these positions are so lucrative.
So, now that we’ve established that machine learning is the future as it reduces human effort while increasing machine performance by allowing machines to learn for themselves. As a result, there are numerous popular and well-paying career paths in Machine Learning. Let’s take a look at the various career paths available after completing Machine Learning engineering:
Description: A Data Scientist collects, analyses, and interprets large amounts of data using advanced analytics technologies such as Machine Learning and Predictive Modelling. These are then used by company executives to make business decisions. So, in addition to other skills such as data mining and knowledge of statistical research techniques, Machine Learning is a critical skill for a Data Scientist.
Common Responsibilities: Collect, analyse, and interpret massive amounts of data while applying computer science, mathematics, and statistics concepts.
Salary: ₹ 4.5 Lakhs to ₹ 25.9 Lakhs with an average annual salary of ₹ 10.5 Lakhs.
Machine Learning Engineer
Description:A Machine Learning Engineer is a programmer who runs machine learning experiments in programming languages such as Python, Java, Scala, and others. Machine Learning Engineers analyse data in order to create various machine learning algorithms that run autonomously with little human supervision. In layman’s terms, a Machine Learning Engineer creates the necessary outputs for machines.
Common Responsibilities: Designing ML systems, Researching and implementing ML algorithms and tools, Selecting appropriate data sets, Picking appropriate data representation methods, Identifying differences in data distribution that affects model performance, Verifying data quality, Using results to improve models, Training and retraining systems when needed, Extending machine learning libraries, Developing machine learning apps according to client requirements.
Salary: ₹ 3.5 Lakhs to ₹ 21.9 Lakhs with an average annual salary of ₹ 7.5 Lakhs.
Description: Natural language processing (NLP) is the process of teaching machines to understand human language. This means that machines will eventually be able to converse with humans in our native language. So, an NLP Scientist essentially contributes to the creation of a machine that can learn speech patterns and also translate spoken words into other languages.
Common Responsibilities: An NLP scientist is in charge of the technical development and coding of NLP devices and applications.
Salary: ₹ 5.1 Lakhs to ₹ 52.0 Lakhs with an average annual salary of ₹ 15.0 Lakhs.
Business Intelligence Developer: Large amounts of data are gathered, analysed, and interpreted by a business intelligence developer using data analytics and machine learning to provide practical insights that can be used by company executives to make business decisions.
Common Responsibilities: Designing, developing, and maintaining business intelligence solutions, Creating and executing queries in response to data requests, Using reports and visualisation to present information.
Salary: ₹ 3.4 Lakhs to ₹ 15.6 Lakhs with an average annual salary of ₹ 6.2 Lakhs.
Human-Centered Machine Learning Designer: Human-centered Machine Learning refers to Machine Learning algorithms that are designed with humans in mind. Systems that can execute Human-Centered Machine Learning based on information processing and pattern recognition are developed by Human-Centered Machine Learning Designer. As a result, the machine may “learn” about the preferences of specific users.
Common Responsibilities: Create technology-based programs, applications, and devices that ultimately solve the issues experienced by people using the technology.
Salary: ₹ 3.5 Lakhs to ₹ 22.0 Lakhs with an average annual salary of ₹ 7.5 Lakhs.
Keeping all of this in mind, ChitkaraUniversity provides Computer Science engineering programs with specialization in AI and Machine learning to greatly enhance student’s career prospects. Some of the many reasons to enrol in the program at this reputable college include its accreditations and achievements, infrastructure, cutting-edge labs, faculty, international partnerships, and stellar placement record.
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