How to Become A Machine Learning Engineer: Ultimate Learn Guide | Careervira

Machine Learning (ML) is a revolutionary sub-field of Artificial Intelligence (AI) that focuses on developing algorithms and models capable of automatically learning and making predictions or decisions from data without being explicitly programmed. By leveraging the power of statistical techniques and computational tools, ML algorithms can extract valuable insights, recognize patterns, and solve complex problems across various domains.

In recent years, the widespread availability of vast amounts of data, along with advancements in computing power and various advantages of Machine Learning has propelled its growth and applications. ML has become a vital part of our daily lives, from self-driving cars and virtual personal assistants (Siri and Alexa) to recommendation systems and fraud detection. It includes everything from video surveillance to facial recognition on your smartphone.

Today, companies across industries seek professionals who can help them make data-driven decisions and gain a competitive edge. The advantages of Machine Learning are abundant in industries such as banking & finance, retail and healthcare, paving the way for more opportunities. Thus, becoming an expert in machine learning is increasingly important for individuals looking to secure high-paying jobs and advance their careers. Many cutting-edge businesses have hired Machine Learning engineers to help them accelerate their growth.

Educational Requirements

Machine Learning is a constantly growing field where data and algorithms are used to imitate how humans learn to improve accuracy. The transient skills that it offers and the lucrative opportunities that one can explore have made a lot of software developers switch careers to Machine Learning. The reason is that both are technical fields.

Let’s look at the first set of prerequisites that one must fulfill if they want to become an ML Professional:

• Bachelor’s Degree
  A Bachelor’s degree in Computer Science, Information Technology or a related field.
• Master’s Degree
  A master’s degree in machine learning, although important, is not necessarily required for all jobs or roles. It can demonstrate to employers that you have a deep understanding of the field and are committed to developing your skills and knowledge.
• Certifications
  Having machine learning engineering course certifications helps in having a good profile in front of recruiters. Professional Certificate Program in Machine Learning & Artificial Intelligence, Certificate in Machine Learning, IBM Machine Learning Professional Certificate, etc., are preferred.

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Top Skills Required to Become ML Engineer

Now, look at the skills required for Machine Learning engineer. While the first two are foundational, and you may have even acquired them in high school or your undergraduate degree, they become more sophisticated and domain-specific as you proceed down the list.

• Applied Maths
  A machine learning engineer’s set of crucial skills includes math. Machine learning can make extensive use of mathematics. The right ML algorithm for your data can be chosen using a variety of mathematical formulas. It’s very simple to understand them if you have a strong background in math. It is one of the basic requirements for most entry-level machine learning jobs.
• You should be familiar with important math concepts for data science and AI, including linear algebra, probability, statistics, multivariate calculus, and distributions like Poisson, normal, and binomial. A basic understanding of physics concepts is also helpful if you wish to work as a machine learning engineer.
• Basics of Computer Science and Programming
  Computer science concepts, such as data structures (stack, queue, tree, and graph), algorithms (searching, sorting and dynamic programming), space and time complexity, etc., must be acquainted with you. You should know programming languages, including Python and R for Machine Learning, Spark and Hadoop for distributed computing, SQL for managing databases, Apache Kafka for pre-processing data, etc. Furthermore, it is ideal if you are familiar with Python’s libraries, such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc.
• Machine Learning Libraries and Algorithms
  Several API Packages and libraries are available, including Google TensorFlow, Apache Spark’s MLlib, and Microsoft’s CNTK. Utilizing these technologies will require clear comprehension of how they might be connected with various systems and their potential dangers. Apriori Algorithm, Naive Bayes Classifier, K Means Clustering, Support Vector Machine, Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc., are some of the most popular ML algorithms. Before starting your career as an ML developer, understanding all these methods is a good idea.
• Data Evaluation and Modeling
  Machine learning must process enormous volumes of data and use predictive analytics to make decisions. When working with large data quantities, data modeling and evaluation are crucial for creating efficient algorithms and determining how effective the final model will be.
• Neural Networks
  Neural networks are a branch of AI and are a succession of algorithms that set out to identify relationships in vast amounts of data. Machine learning has undergone a revolution thanks to neural networks. We require machine learning for jobs that are too difficult for humans to code directly or so complex that it is unfeasible.
• Natural Language Processing (NLP)
  Natural Language Processing is a vital skill for Machine learning that tries to instruct computers in the complexity of human language, improving our understanding of human communication. The foundation for Natural Language Processing is the Natural Language Toolkit. These libraries can help computers understand natural language by breaking the text down according to its grammar, extracting key phrases, deleting unnecessary words, etc.
• Software Development
  Machine Learning should be used with different technologies to realize its full potential. You must create algorithms and systems for machine learning that are simple to integrate and connect with other technologies. You must therefore possess good knowledge of Application User Interfaces (APIs) of many varieties (web APIs, dynamic and static libraries, etc.), as well as the ability to build interfaces that can withstand modifications in the future.
• Communication Skills
  Your career trajectory can be greatly impacted by your ability to communicate effectively. This is because, even though you are the best at understanding the data and the insights produced by machine learning, it is equally crucial that you can communicate these findings to a non-technical team, your shareholders, or clients.
• This may also mean employing data storytelling, wherein you must present your data narratively with a beginning and an end at specific outcomes you have discovered by applying machine learning to the data. This is so that a firm may focus more on the actionable insights drawn from the data, which are ultimately more essential to a company than the data analysis.

To gain or hone such skills, you can easily take help of online machine learning engineer courses offered by leading course providers.

Final Take

Machine Learning is being used in practically most of the industry these days. This includes industries such as pharmaceuticals, cybersecurity, marketing, etc. The size of the global market for machine learning as a service, projected at USD 15.47 billion in 2021, is expected to increase at a compound annual growth rate (CAGR) of 39.3% from 2022 to 2030, as per a report by Precedence Research.

It clears the fact that being a Machine Learning engineer and studying more about ML are excellent ideas and extremely good career choices. Thus, you should begin your journey in Machine Learning to build a rewarding and exciting career for yourself.

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