The AI and ML specialists? demand is expected to grow by 40% by the year 2027. The will to take up challenges, developing fields and interesting salaries are some of the attractive aspects of Machine learning. The intriguing rewards have attracted a high number of freshers and experienced candidates with the aim of succeeding in the domain. Helping you master the required skillset and learn how to ace interviews by exhibiting your skills, we welcome you onboard to prepare for your journey.
But before that, here are some fundamental details that you must understand:
Machine learning is an advanced field offering accurate and transparent data usage. Contributing to growth and development in different fields, here are some top reasons for its popularity:
The quick responses coupled with accuracy attract the users to utilize Machine Learning.
The data interpretation is accurate and transparent
Allows easy configuration in a cost-effective manner
Offers opportunities for business people to take advantage
Able to perform predictive actions and take insights
A candidate aiming to switch careers to machine learning is expected to have prior knowledge of the field. The transition can occur from both technical and non-technical backgrounds. However, the background affects the time required to learn the skills. To help you know how to switch to machine learning, the following sections have been crafted.
Prerequisites
Machine learning runs on some important subjects or fields, which are linear algebra, statistics, Python and multivariate calculus. Linear algebra and multivariate calculus are of specific importance if candidates want to enter academics with a special emphasis on research. Alternatively, the application part does not require detailed knowledge owing to the presence of pre-built and inbuilt libraries.
The base of Machine Learning is data that requires handling through collection, analysis and presentation. Proficiency in these is a must and should be learned while making a career switch to machine learning. The key concepts to be learned here are Regression, Statistical significance, Bayesian thinking, Probability distribution and other concepts.
Programming languages like Python are widely used, along with the second most popular one being R and Perl. Familiarity with their libraries and the ability to handle their previous and upgraded version is of importance for daily life in the role of Machine Learning. Some of the libraries of significance include Scikit-learn, Keras, TensorFlow and others.
Skills
Besides educational qualifications in relevant fields, programming and algorithms knowledge are some of the important skills looked for in candidates. Updated information about current advancements and familiarity with recent versions of tools and techniques are also sought by recruiters.
Upgraded knowledge
In the current scenario, candidates seeking upgraded knowledge and experience are preferred over individuals who continue to stay stagnant in their career. There are numerous platforms available online and offline to impart knowledge. You can opt for the convenience of learning while pursuing your current job. Candidates aiming to switch careers to machine learning can opt for a Master’s degree or online courses.
Project Experience
Hands-on experience and face-to-face interaction with real-world cases are desired and preferred. It enables employers to believe in your ability to tackle the data and not mess up their important information.
Transition From Different Careers to Machine Learning
Passionate individuals with the aim to upgrade their careers and seek better opportunities can transition to new fields. Here is your guide to enter into Machine Learning Engineering:
Technical Field
Software Engineer: With clarity on basic concepts of programming languages and algorithms, a relevant question that should arise is how to transition from software engineer to machine learning engineer. Progressing with a technical background is advantageous. All you need to add is learning about the basic Machine Learning algorithms and libraries such as PyTorch or TensorFlow. Get familiar with real-life projects and experiences.
Data Engineer: The transition from data engineer to machine learning engineer brings the expertise of strong foundation skills in data handling. The aspirants from this field need to gain proficiency in machine learning frameworks, model deployment and feature engineering to seamlessly transition to deploying complex machine learning models. It aids in building data pipelines and intelligent systems by providing strategic capabilities.
Data Scientist: Further, talking about another related field, the transition from data scientist to machine learning engineer brings forward expertise in data analysis, statistics and modeling. The engineering field requires knowledge from different perspectives. Here, the candidates seeking transition need to emphasize infrastructure management, scalability, production-ready model deployment and optimization. Learning about containerization, deployment on cloud platforms and orchestration tools is recommended.
Non-technical/Non-IT Field
Economics: The core field experience when you switch careers to machine learning from economics can be carried forward for opportunities in fraud detection, risk assessment, algorithmic trading and market analysis. With proficiency in quantitative methods, economists should begin learning programming languages while focusing on clustering, regression, time series analysis and classification. One should proceed with deployment methods, feature engineering and model evaluation.
Marketing: The recruiters expect familiarity with data manipulation tools such as NumPy and Pandas in Python. You need to upskill in customer segmentation, data preprocessing, recommendation systems, sentiment analysis and familiarity with Natural Language Processing to provide the combination of talent as a Machine Learning Engineer.
Graphic Design: Begin your journey with Python and the fundamentals of programming. Proceed forward with image processing techniques, deep learning frameworks for image classification, style transfer and generative models. Additionally, contribute your knowledge to the field with Convolutional Neural Networks (CNN) and transfer learning.
Mechanical Engineering: You also have the option to switch careers to machine learning from Mechanical engineering. Leverage the knowledge of data analysis and simulation while learning about statistical analysis, algorithms, and supervised and unsupervised learning. Further, accomplish your passion for learning programming languages and combine it with gaining familiarity with predictive maintenance.
Transition to Machine Learning with Interview Kickstart
The transition from software engineering to machine learning or from any other field is easy in the presence of clear concepts. Machine learning is the right arena to switch from both technical and non-technical fields and contribute to advancements in new domains. Gain your familiarity and concept clarity, and we are here to make you ready to face the recruiters.
With top recruiters from FAANG+ companies to guide you in your career transition, Interview Kickstart offers you a perfect opportunity to brush up with relevant questions of the field and learn from a customized Machine Learning Interview Course!
Take the first step towards your transition and unlock the path now! Register for our free webinar!
Ans. The average Machine Learning salary is around INR 6 lakhs with an average additional cash composition of INR 46,000. A Machine Learning Engineer can expect high salaries of up to INR 10 lakh per year with further promotion opportunities in both salary and designation.
Q2. Is it difficult to get a job in Machine Learning?
Ans. Based on your proficiency with important concepts crucial for Machine Learning tasks and your ability to contribute to the company’s growth, the difficulty of getting a job in Machine Learning varies. We recommend portraying yourself as the contributing candidate for the company through better research.
Q3. Is Machine Learning a stressful job?
Ans. The complexity of a Machine Learning job depends upon project complexity, the company’s internal environment, working hours and deadlines. Stress in a job can also result from working on challenging projects or difficult clients. However, professional and monetary progress are the rewards in such scenarios.
Q4. Will Machine Learning replace coding?
Ans. Machine Learning is a tool for task automation and learning patterns from data. Coding is not possible through Machine Learning.
Q5. Does Machine Learning require a lot of math?
Ans. A solid understanding of different mathematical concepts is required. You need to learn topics like calculus, linear algebra, probability, and statistics.
Ans. Artificial Intelligence is the broader concept to mimic human intelligence. Machine Learning is the subfield that enables machines to learn from data and generate patterns.
Ans. The Machine Learning algorithm is a set of mathematical instructions that enables pattern learning, predictions, decisions and classification by the computer.
