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HomeBlogData ScienceTop 30 Machine Learning Skills for ML Engineer in 2024
Embarking on a journey in the highly demanded field of Machine Learning (ML) opens doors to diverse career opportunities. The avenues to acquire the essential skills for a career in ML are plentiful, ranging from Machine Learning online courses and certifications to formal degree programs. However, transitioning from being interested to working in the field requires more than just accumulating theoretical knowledge. Time and again, I come across college freshers and people looking to change their steams asking about machine learning skills in demand. In this post, I've put together the essential information to guide you in cultivating not only the necessary machine learning skills but also the mindset crucial for a machine-learning engineer.
Understanding the core principles and honing specific skills are pivotal steps toward realizing your aspirations in the dynamic realm of machine learning. In this comprehensive blog, we delve into the foundational aspects and intricacies of the machine learning landscape. We will demystify the path from curiosity to a fulfilling career by touching up all the machine learning engineer skills required.
Look at the stats that show a positive trend for machine learning projects and careers.
With so much happening around machine learning, it is no surprise that any enthusiast who is keen on shaping their career in software programming and technology would prefer machine learning as a foundation to their career. This post is specifically aimed at guiding such enthusiasts and gives comprehensive information on machine learning skills that are needed to become an ML engineer, who is ready to dive into the real-time challenges.
Machine learning, in simple terms, is an offshoot of artificial intelligence. It is the realm where algorithms self-educate themselves to predict outcomes by uncovering data patterns. It has no manual coding; it is all about smart algorithms doing the heavy lifting. In traditional machine learning, data combined with statistical tools generate predictions that offer practical insights for decision-making.
Machine learning drives advancements in image and speech recognition, Natural Language Processing (NLP), recommendation systems, and applications like fraud detection. It is the intelligence behind autonomous vehicles, drones, and robots, adapting seamlessly to changing environments. From Netflix recommendations to online stores, machine learning crafts personalized suggestions by delving into your history, ratings, and preferences. It is made possible by what is called reinforcement learning. The algorithms learn from environmental feedback to enhance recommendations based on your current habits. Industries love personalized recommendations for e-commerce, social media, and online advertising. It is not only accurate but also like hiring a personal assistant, streamlining experiences, and boosting engagement.
Needless to say, the applications of machine learning go far beyond recommendation engines and into many other fields. Some of the applications with promising career prospects include healthcare, finance, insurance, pharmaceuticals, farming, retail, oil and gas, and more. With machine learning skills in resume, you can significantly boost your chances of building a lucrative career in any of these fields.
I would define a Machine Learning Engineer as a technically proficient programmer who delves into the intricacies of self-running software and predictive models. These professionals, with their ML engineer skills, have expertise in research, building, and designing to develop AI systems that harness expansive datasets. Machine learning engineers work with data science teams on a diverse range of tasks. Their Data Science training comes in handy in roles that extend from fundamental research and development and designing AI for machine learning to the ongoing maintenance of AI systems.
Beyond their machine learning skills, effective communication is at the core of what they do. Serving as a crucial intermediary, they facilitate collaboration between data scientists shaping models and the technical professionals implementing and managing the models. Their responsibilities include implementing machine learning algorithms, conducting experiments and tests on AI systems, and designing and developing machine learning systems, along with a range of different tasks.
Organizations are showing massive interest in using machine learning in their products, which would in turn bring plenty of opportunities for machine learning enthusiasts.
When you ask machine learning engineers the question – “What do you do as a machine learning engineer?”, chances are high that individual answers would differ from one professional to another. This may sound a little puzzling, but yes, this is true!
Hence, a beginner to machine learning needs to have a clear understanding that there are different roles that they can perform with machine learning skills. And accordingly, the skill set that they should possess, would differ. This section will give clarity on machine learning skills that are needed to perform various machine learning roles.
Broadly, three main roles come into the picture when you talk about machine learning skills:
One must understand that data science, machine learning and artificial intelligence are interlinked. The following quote explains this better:
Data science produces insights. Machine learning produces predictions. Artificial intelligence produces actions.
A machine learning engineer is someone who deals with huge volumes of data to train a machine and impart it with the knowledge that it uses to perform a specified task. However, in practice, there may be a little more to add to this:
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Thus, gaining machine learning skills should be a task associated with clarity on the job role and of course the passion to learn them. As it is widely known, becoming a machine learning engineer is not a straightforward task like becoming a web developer or a tester.
Irrespective of the role, a learner is expected to have solid knowledge on data science. Besides, many other subjects are intricately intertwined in learning machine learning and for a learner, it requires a lot of patience and zeal to learn skills and build them up as they move ahead in their career.
The following diagram shows the machine learning skills that are in demand year after year:
In the coming sections, we would be discussing each of these skills in detail and how proficient you are expected to be in them.
Learning machine learning skills is getting costlier day by day yet KnowledgeHut’s Data Science course fee has been providing the highest ROI (Return on Investment) for its learners. We recommend learners investigate the course curriculum before enrolling. Make sure the curriculum includes the skills we are going to delve deeper into below -
Mathematics plays an important role in machine learning, and hence it is the first one on the list. If you wish to see yourself as a proven machine learning engineer, you ought to love math and be an expert in the following specializations of math.
It depends on the level at which a machine learning engineer works. The following diagram gives an idea about how important various concepts of math are for a machine learning enthusiast.
Source: towardsdatascience
This concept plays a main role in machine learning. One has to be skilled in the following sub-topics of linear algebra:
The core aim of machine learning is to reduce the probability of error in the final output and decision making of the machine. Thus, it is no wonder that probability and statistics play a major role.
The following topics are important in these subjects:
In calculus, the following concepts have notable importance in machine learning:
The scalability and the efficiency of computation of a machine learning algorithm depend on the chosen algorithm and optimization technique adopted. The following areas are important from this perspective:
Besides, the ones mentioned above, other concepts of mathematics are also important for a learner of machine learning. They are given below:
Neural networks are the predefined set of algorithms for implementing machine learning tasks. They offer a class of models and play a key role in machine learning.
The following are the key reasons why a machine learning enthusiast needs to be skilled in neural networks:
The following are the areas of neural networks that are important for machine learning:
Having an idea of physics definitely helps a machine learning engineer. It makes a difference in designing complex systems and is a skill that is a definite bonus for a machine learning enthusiast. Machine learning has drawn inspiration from statistical physics since its inception. Variational inference and maximum entropy are examples of modern machine learning tools that were developed by physicists.
Machine learning has to work with huge amounts of data and leverage them into predictive analytics. Data modeling and evaluation is important in working with such bulky volumes of data and estimating how good the final model is.
For this purpose, the following concepts are worth learnable for a machine learning engineer:
The crux of signal processing is to minimize noise and extract the best features of a given signal.
For this purpose, it uses certain concepts such as:
All these concepts find their application in machine learning as well.
The importance of natural language processing in artificial intelligence and machine learning is not to be forgotten. Various libraries and techniques of natural language processing used in machine learning are listed here:
This differs from natural language processing in the sense that we can apply audio and video processing on audio signals only. For achieving this, the following concepts are essential for a machine learning engineer:
Though reinforcement learning plays a major role in learning and understanding deep learning and artificial intelligence, it is good for a beginner of machine learning to know the basic concepts of reinforcement learning.
Machine learning, ultimately, is coding and feeding the code to the machines and getting them to do the tasks we intend them to do. As such, a machine learning engineer should have hands-on expertise in software programming and related concepts. Here is a list of programming skills a machine learning engineer is expected to have knowledge on:
Let us look into each of these programming skills in detail now:
It is important that a machine learning engineer apply the concepts of computer science and programming correctly as the situation demands. The following concepts play an important role in machine learning and are a must on the list of the skillsets a machine learning engineer needs to have:
Whatever a machine learning engineer does, ultimately it is a piece of software code – a beautiful conglomerate of many essential concepts and the one that is entirely different from coding in other software languages.
Hence, it is quintessential that a machine learning engineer have solid knowledge of the following areas of software programming and system design:
A machine learning engineer may need to work with multiple packages, libraries, algorithms as a part of day-to-day tasks. It is important that a machine learning engineer is well-versed with the following aspects of machine learning algorithms and libraries:
A thorough idea of various learning procedures including linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods.
Working as a machine learning engineer means working with huge sets of data, not just focused on one isolated system, but spread among a cluster of systems. For this purpose, it is important that a machine learning engineer knows the concepts of distributed computing.
Most clusters and servers that machine learning engineers need to work are variants of Linux(Unix). Though randomly they work on Windows and Mac, more than half of the time, they need to work on Unix systems only. Hence having sound knowledge on Unix and Linux is a key skill to become a machine learning engineer.
Machine learning engineers need to code to train machines. Several programming languages can be used to do this. The list of programming languages that a machine learning expert should essentially know are as under:
In this section, let us know in detail why each of these programming languages is important for a machine learning engineer:
Python Programming Language has several key features and benefits that make it the monarch of programming languages for machine learning:
There are various components of Python that make it the preferred language for machine learning. Such components are discussed below:
Jupyter offers excellent computational environment for Python based data science applications. With the help of Jupyter notebook, a machine learning engineer can illustrate the flow of the process step-by-step very clearly.
NumPy or Numerical Python is one of the components of Python that allows the following operations of machine learning in a smooth way:
Of late, NumPy is gaining attention because it makes an excellent substitute to MATLAB, as it coordinates with Matplotlib and SciPy very smoothly.
Pandas is a Python library that offers various features for loading, manipulating, analysing, modeling and preparing data. It is entirely dedicated for data analysis and manipulation.
Built on NumPy, SciPy, and Matplotlib, it is an open-source library of Python. It offers excellent features and functionalities for major aspects of machine learning such as clustering, dimensionality reduction, model reduction, regression and classification.
TensorFlow is another framework of Python. It finds its usage in deep learning and having a knowledge of its libraries such as Keras, helps a machine learning engineer to move ahead confidently in their career.
It is widely known that machine learning is a non-linear process that involves many iterations. Weka or Waikato Environment for Knowledge Analysis is a recent platform that is designed specifically designed for applied machine learning. This tool is also slowly gaining its popularity and thus is a must-include on the list of skills for a machine learning engineer.
This is a basic programming language that was used for simulation of various engineering models. Though not popularly used in machine learning, having sound knowledge in MATLAB lets one learn the other mentioned libraries of Python easily.
Technical skills are relevant only when they are paired with good soft skills. And the machine learning profession is no exception to this rule. Here is a list of soft skills that a machine learning engineer should have:
Let us move ahead and discuss how each of these skills make a difference to a machine learning engineer.
1. Domain knowledge
Machine learning is such a subject that needs the best of its application in real-time. Choosing the best algorithm while solving a machine learning problem in your academia is far different from what you do in practice. Various aspects of business come into picture when you are a real-time machine learning engineer. Hence, a solid understanding of the business and domain of machine learning is of utmost importance to succeed as a good machine learning engineer.
2. Communication Skills
As a machine learning engineer, you need to communicate with offshore teams, clients and other business teams. Excellent communication skills are a must to boost your reputation and confidence and to bring up your work in front of peers.
3. Problem-solving skills
Machine learning is all about solving real time challenges. One must have good problem-solving skills and be able to weigh the pros and cons of the given problem and apply the best possible methods to solve it.
4. Rapid Prototyping
Choosing the correct learning method or the algorithm are signs of a machine learning engineer’s good prototyping skills. These skills would be a great saviour in real time as they would show a huge impact on budget and time taken for successfully completing a machine learning project.
5. Time management
Training a machine is not a cakewalk. It takes huge time and patience to train a machine. But it’s not always that machine learning engineers are allotted ample time for completing tasks. Hence, time management is an essential skill a machine learning professional should have to effectively deal with bottlenecks and deadlines.
6. Love towards constant learning
Since its inception, machine learning has witnessed massive change – both in the way it is implemented and in its final form. As we have seen in the previous section, technical and programming skills that are needed for machine learning are constantly evolving. Hence, to prove oneself a successful machine learning expert, it is very crucial that they have a zeal to update themselves – constantly!
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The world of machine learning engineering is dynamic and demanding. It requires a unique blend of technical prowess, which is a combination of data skills, software engineering skills, and machine learning skills. It is also essential that they possess soft skills such as adaptability and effective communication to work with teams across challenging projects.
Whether you are entering the field fresh or making a career transition, how to improve machine learning skills is matter of systematic approach to learning the right topics. I would recommend starting by taking up a certification path, such as KnowledgeHut's Machine Learning online course, which offers an exhaustive syllabus and experienced instructors who can guide you at your skill level. With the practical experience gained from the course, you can be confident in your machine learning engineer skills.
The four basics, which are also the skills needed for machine learning include understanding algorithms, proficiency in programming languages, navigating extensive datasets, and designing models for learning and prediction.
The three types of machine learning include
To build the skills needed for machine learning engineer, start by mastering programming languages (Python, R), understanding algorithms, and gaining proficiency in data handling. Pursuing relevant education in computer science and software engineering can go a long way in establishing a path. For others, certifications or practical experience in machine learning can work as a feasible path.
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