The jobs in the sector of Computer and Information are expected to grow by 23% till the year 2032, according to the report by BLS. LinkedIn India further indicates more than 13,000 jobs for Machine Learning in India. With such intriguing prospects, won’t you want to give machine learning a try? Here is a guide to Machine Learning career curated specifically for beginners or those looking for a career switch.
Here’s What We’ll Cover in this Article:
Machine Learning Career Path
Relevant Skills for Machine Learning Career
Machine Learning Jobs and Salary
Machine Learning Transition From Technical and Non-Technical Fields
Frequently Asked Questions on Machine Learning Engineer Career
Machine Learning Career Path
Career transition to Machine Learning from a different background requires time and will. Depending on one’s prior profession, one might be required to begin learning from basics, or one can be already proficient with core requirements and may wish to learn the new introductions.
Begin with Basics:
Gain familiarity with machine learning and deep learning concepts through online or offline mode, as per the suitability of your current job. Work to gain proficiency in programming, which is significant for the candidate's option for IT roles. However, non-IT roles do not necessarily require it. The knowledge of concepts like linear algebra, calculus, probability and statistics is important for facing hypothesis-based tasks, testing and other such works.
Move Towards Advanced:
The previously mentioned are basic requirements. Coming towards specific ones in Machine Learning, one should be familiar with working on data. Further, one must also be able to handle unstructured data with the ability to model and evaluate. Machine Learning algorithm is the base of Machine Learning. Hence, information on supervised, unsupervised and reinforcement algorithms and their sub-types is crucial before stepping into the real world to work on projects.
Practice through Machine Learning Projects:
Working on Machine Learning projects is the best way to apply your knowledge efficiently and creatively to overcome problems commonly encountered in the profession. It gives an opportunity to get familiar with the recruiter’s and the organization’s expectations. Gaining practical application of gained knowledge is possible through the Rossmann Store Sales Project, Zillow Home Value Prediction ML project and similar others.
Get a Specialization:
Machine Learning encompasses a wide number of choices through specializations. Be it computer vision, natural language processing or any other, an informed choice helps focus your attention and energy in a specific direction.
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Enroll in Advanced Courses:
The competitive world might give you a hard time landing the jobs of choice. Practical experience through internships, when coupled with advanced courses, will help you understand your career approach, your area of interest and confidence for paving a new path or opportunities.
Collaborate and Network:
The action should be taken from the beginning of choosing Machine Learning. Interact with professionals for guidance and insights into the chosen field. Network to remain updated about the trends and opportunities.
What do Experts Say?
“Machine Learning and deep learning will create a new set of hot jobs in the next 5 years.”
-Dave Waters
Relevant Skills for Machine Learning Career
Entering a new field requires the presence of a specific set of skills critical to that field. The skills of relevance in the machine learning engineer career path are:
Technical Skills
Programming languages: Python
Mathematics and Statistics: Linear algebra, hypothesis testing, probability, calculus
Machine Learning Algorithms: Supervise, unsupervised, reinforcement and deep learning
Deep Learning: Neural Network Architectures such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and transformers.
Frameworks: TensorFlow, PyTorch and others relevant to your area
Data preprocessing: Clean, preprocess and transform data for machine learning tasks.
Model evaluation and validation: Model performance through ROC curves, cross-validation and metrics like accuracy, precision, recall and F1-score.
Machine Learning Libraries and Tools: Scikit-learn, LightGBM, XGBoost, Jupyter notebooks and data visualization libraries such as Seaborn and Matplotlib.
Soft Skills
Problem-solving: To meet unannounced and novel challenges during tasks with a strong knowledge of concepts and the right mindset
Communication skills: For efficient communication in both verbal and written manner
Teamwork: For effective workability with team members of different groups
Domain knowledge: Familiarity with the domain you are working in for efficient results
Continuous learning: Constant advancements require regular updates with updated versions of technologies, tools and frameworks
Project management: For effective planning and execution of machine learning projects
Machine Learning Jobs and Salary
The designation Machine Learning Engineer can expect to earn between INR 7 lakhs to 16 lakhs. The average base pay is approximately INR 10 lakhs per year. Candidates can also expect additional cash compensation ranging between INR 32,000 to 3,48,000. The average expectation should lie at INR 1,57,500.
Heading over to foreign nations, Indians can also explore the job opportunities there. The salary range in some of the commonly chosen nations is:
Machine Learning Transition From Technical and Non-Technical Fields
Machine learning career transition is possible regardless of the candidates' background. The technical background holders are expected to hold experience in programming languages and computer fundamentals, which makes the candidates ahead of candidates unfamiliar with such concepts. The edge helps you smooth the career transition curve. To brush up on these skills, you need to ace up the transition process with data handling and learning about the algorithm and frameworks.
Candidates from non-technical backgrounds can opt for either IT or non-IT sector. After the choice, depending on your passion, will to learn and career goals, take the next path suiting your choice. The time required to become familiar with all the concepts will vary depending on the career choice. The contribution of domain knowledge is again another benefit you bring to the organization, often lacking in people with technical backgrounds. So don't assume you are lagging behind.
Machine Learning and Interview Kickstart: Final Thoughts
Machine Learning career is widely chosen among the candidates due to increasing opportunities and trends. The advancements have fascinated so many professionals that the previous background is no bar now.
Helping you through the career switch process from scratch, our guidance helps to brush up your skills while preparing you for interviews from dream companies of candidates. We have recruiters on board to help you in the endeavor while providing personalized guidance. To learn more about our offerings or how to start your transition process, feel free to connect with our executives for a walkthrough. Register for our free webinar and gather insights!
Frequently Asked Questions on Machine Learning Engineer Career
Q1. Is Machine Learning Oversaturated 2023?
Ans. With the above-mentioned data indicating thousands of jobs in September 2023, the signs of oversaturation for career seekers are nowhere to be seen.
Ans. Machine Learning engineers are well paid owing to high demand, the contribution of services to the world, complexity and continuous learning.
Q3. Will Machine Learning replace coding?
Ans. Machine learning is capable of task automation. However, coding is essential for trading and the creation of models. The replacement is still a task to achieve.
Ans. AI engineering focuses on multiple aspects, including machine learning. At the same time, ML engineers focus on the development, training and deployment of ML models.
Ans. The time required to become a Machine Learning Engineer depends on one’s current career stage and path towards Machine Learning. It also depends on their learning curve and ability to apply and showcase their skills.
Ans. Data scientists focus on knowledge and data extraction through different tasks like data analysis. ML and statistics. ML engineers work on ML models for specific applications like task automation and others.
