Automation has made AI and ML skills essential in all industries, resulting in a high demand for tech-savvy workers. This prompts many engineers to pursue careers in Machine Learning. Whether you're exploring tech jobs or aiming to shift into ML full-time, it's a promising path.
In this article, we will discuss how you can get into the field of machine learning and the most in-demand jobs of 2024. Along with the salary prospect of a machine learning professional!
What is Machine Learning?
Machine learning finds patterns in giant amounts of data. This data can be numbers, words, pictures, or anything digital. These patterns are used by many things we use every day. For example, Netflix and Spotify use them to recommend things you might like. Machine learning also helps search engines and social media feeds. In all these cases, the machines learn about you by collecting data on what you click on or watch. Then they use this to guess what you might want next. Voice assistants use machine learning to understand what you say by finding patterns in sounds. Machine learning might seem hard, but it finds and uses patterns.
How to Build a Career in Machine Learning
Building a career in machine learning involves developing a strong foundation in computer science, mathematics, statistics, and of course, machine learning itself. Here's a roadmap to get you started:
Foundational Knowledge
Earn a bachelor's degree in computer science, mathematics, or a related field. Many machine learning engineers pursue advanced degrees (master's or Ph.D.) for in-depth knowledge.
Programming Languages
Python is the go-to language for machine learning due to its ease of use and powerful libraries. Python is highly recommended due to its simplicity and versatility. R is also popular, especially for statistical analysis and data visualization tasks. While C++ isn't as common, it can provide a performance boost for complex tasks.
Machine Learning Course
Enrol in online courses or pursue formal education in machine learning and deep learning. You'll learn about algorithms, neural networks, and analysing data. These courses have projects to help you practice. Online platforms provide flexibility, allowing you to learn at your own pace from anywhere. Reputable institutes like Felix-IT Systems offer both online and offline machine learning courses for students to pursue.
Personal Projects
Work on personal projects to apply your learnings and build a portfolio showcasing your skills.
Working on personal projects is essential for aspiring machine learning professionals. These projects let you apply what you've learned, prove your skills, and gain practical experience. Personal projects improve your problem-solving skills and build a portfolio to impress employers or clients. Consistently working on such projects enhances your skills and boosts your credibility in the field.
Open-Source Contribution
Contributing to open-source machine learning lets you learn from and give back to the field. You'll gain experience by working on real projects alongside a supportive community. It's a win-win for you and the advancement of machine learning!
Entry-Level Jobs
Look for internships or entry-level jobs in data science or related fields to gain practical experience with machine learning applications.
Best Career Paths in Machine Learning
Machine Learning Engineer
This is a highly sought-after role that focuses on building and deploying machine learning models in production. This role demands adept programming abilities, coupled with a deep comprehension of machine learning algorithms and cloud infrastructure. Machine learning engineers need strong programming skills, along with a solid understanding of machine learning algorithms and cloud platforms.
Natural Language Processing (NLP) Scientist
NLP scientists focus on computer-human language interaction. They develop ways for machines to understand and process human language. Their work has applications in machine translation, chatbots, and sentiment analysis. NLP is a subfield of AI. It deals with computers understanding human language. Scientists in this field create techniques for machines to process language. These techniques are used in various areas like translation and chatbots.
Machine Learning Cloud Architect
As more and more machine learning models are deployed in the cloud, there's a growing demand for cloud architects with expertise in ML. These professionals design and manage cloud infrastructure that can support large-scale machine learning models.
Data Engineer
The unsung heroes of machine learning, data engineers build and maintain the data pipelines that feed machine learning models. They need expertise in data wrangling, data storage, and data security. Data engineers' role is pivotal for ML success, as they lay the groundwork for accurate predictions and insights.
AI Ethics Officer/Researcher
AI Ethics Officers/Researchers ensure AI technologies are developed and used ethically. They create guidelines for responsible AI use. As machine learning advances, their role grows. They address concerns like bias and privacy. Their work promotes fairness and accountability in AI. They develop and implement ethical frameworks. These frameworks guide AI development and usage. Their role is crucial in shaping a responsible AI future.
Robotic Engineer with a focus on AI
As robots become more sophisticated and integrated with AI. There's a growing demand for engineers who can design and develop robots that can learn and adapt. This field is essential for the future as robots become more integrated, and skilled engineers are needed. They shape the future of robotics and AI.
Data Scientist
Analyse data to extract insights and build ML models. You'll be the data guru, cleaning, manipulating, and interpreting data to fuel ML projects. With a keen eye for patterns and trends, you transform data into actionable intelligence, driving ML initiatives forward. Your role as a data guru is pivotal in harnessing the power of information to unlock innovation and drive informed decision-making.
Machine Learning Cloud Architect
Design and manage cloud infrastructure for deploying and scaling ML models. As cloud computing is essential for ML, your expertise will be key. You connect machine learning and cloud computing, making sure they work well together for top-notch performance.
Machine Learning Salary in India
According to AmbitionBox, Machine Learning Engineer salary in India ranges between ₹ 3.0 Lakhs to ₹ 22.0 Lakhs with an average annual salary of ₹ 10.0 Lakhs.
Moreover, Machine Learning Engineer's salary in Pune ranges between ₹ 3.0 Lakhs to ₹ 15.0 Lakhs with an average annual salary of ₹ 8.4 Lakhs.
Your salary in this field will vary depending on factors like location, skills, experience, position, industry, skills and the firm you are working for.
Here's a list of top skills to learn:
Machine Learning
Deep Learning
Artificial Intelligence
SQL
Data Science
Computer Vision
NLP
Natural Language Processing
AWS
Data Structures
Computer science
Analytical
C++
Monitoring
Top Companies Hiring for Machine Learning Professionals in India
Google
Microsoft Corporation
PayPal
ShareChat
Schlumberger
Verse Innovation
JPMorgan Chase & Co.
Qualcomm
Fractal Analytics
Samsung Research
Advance in Your Machine Learning Career with a Course
Machine Learning careers attract many due to great opportunities and evolving trends. Regardless of background, every individual can pursue this field for a better future. The machine learning course by Felix-IT Systems can help not only beginners but also professionals to develop relevant skills and advance in this direction.
The detailed programme helps the students to learn the fundamentals and gradually become an expert in the field. The best thing about the machine learning course by Felix-IT Systems is the placement support. Once you are done with the course, the support team at Felix-ITs will help you land a promising career!