Machine Learning: 10 Must Have Machine Learning Engineer Skills That Will Get You Job In 2024

Introduction 

We are living in the world of humans and machines. The humans have been evolving and learning from the past experience since millions of years ago. On the other hand, the era of machines and robots have just begun.

You can consider it in a way that currently we are living in the primitive age of machines while the future of machines is enormous and is beyond our scope of imagination. So we leave all of these responsibilities on the shoulder of a particular individual which is the machine learning engineer. 

So let's have a look at the top 10 skills which are required to become a successful machine learning engineer. 

10. Programming Language 

So starting with programming languages python is the lingua franca of machine learning. You may have had exposure to python even if you weren't previously in programming or in a computer science related field. However, it is important to have a solid understanding of classes and data structures.

Sometimes python won't be enough often you'll encounter projects that need to leverage hardware for speed improvements. 

Now make sure you're familiar with the basic algorithms as well as the classes memory management and linking now, if you want a job in machine learning, you will probably have to learn all of these languages at some point C++ can help in speeding code up whereas our works great in statistics and plots and hadoop is Java based. So you probably need to implement mappers and reducers in Java. 

9. Linear Algebra 

Now, next we have linear algebra. You'll need to be intimately familiar with matrices vectors and matrix multiplication. If you have an understanding of derivatives and integrals, you should be in the clear otherwise even simple concept like gradient descents will elude you.

Statistic is going to come up a lot, at least make sure you are familiar with the quotient distributions, means, standard deviation and much more. Every bit of statistical understanding beyond this helps the theories, help in learning about algorithms, great samples are naive bias, caution mixture models and hidden Markov models. You need to have a firm understanding of probability and statistics to understand these theories.

8. Advanced Signal Processing 

Next. We have advanced signal processing techniques. The feature extraction is one of the most important parts of machine learning, different types of problems need various solutions. You may be able to utilize really cool advanced signal processing algorithms such as wavelets, shearlets, curvelets and badless.

You need to learn about the time frequency analysis and try to apply it in your problems. Now this skill will give you an edge over all the other skills. Note, this skill will give you an edge while you're applying for a machine learning engineer job or others.

7. Applied Mathematics and Algorithm 

Next we have applied maths a lot of machine learning techniques out there, are just fancy types of functional approximation. Now these often get developed by theoretical mathematician and then get applied by people who do not understand the theory at all. 

Now the result is that many developers might have a hard time finding the best techniques for the problem. So even a basic understanding of numerical analysis will give you a huge edge having a firm understanding of algorithm theory and knowing how the algorithm works. You can also discriminate models such as SVM's.

You need also to understand subjects such as gradient descent, convex optimization, lag range, quadratic programming, partial differentiation equations and much more. 

Now all this math might seem intimidating at first if you're been away from it for a while. Just machine learning is much more math intensive than something like front-end development. Just like any other skill getting better at math is a matter of focus practice.

6. Neural Networks Architecture 

The next skill in our list is the neural network architectures. We need machine learning for tasks that are too complex for human to code directly,  that is, tasks that are so complex that it is impractical. 

Neural networks are a class of models within the general machine learning literature. They are a specific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks and the current so-called deep neural networks have proven to work quite well. The neural networks are themselves general function approximations, which is why they can be applied to almost any machine learning problem about learning a complex mapping from the input to the output space.

Of course, there are still good reason for the search in the popularity of neural networks, but neural networks have been by far, the most accurate way of approaching many problems, like translation, speech recognition and image classification. 

5. Natural Language Processing 

Now coming to our next point, which is the natural language processing. Since it combines computer science and linguistics, there are a bunch of libraries like the NLTK, Jansism and the techniques such as sentimental analysis and summarization that are unique to NLP. Now audio and video processing has a frequent overlap with the natural language processing.

However, natural language processing can be applied to non audio data like text, voice and audio. Analysis involves extracting useful information from the audio signals themselves being well-versed and math will get you far in this one. And you should also be familiar with the concepts such as the fast Fourier transforms.

Now these are the technical skills that are required to become a successful machine learning engineer. 

So next I'm going to discuss some of the non-technical skills or the soft skills which are required not only to become a machine learning engineer but also to make you fruitful in all types of jobs.

4. Industry Knowledge 

So first of all among the non technical skills, we have the industry knowledge. Now the most successful machine learning projects out there are going to be those that address real pain points. Whichever industry you are working for, you should know how that industry works and what will be beneficial for the business if a machine learning engineer does not have business acumen and the know-how of the elements that make up a successful business model or any particular algorithm. 

Then all those technical skills cannot be channeled productively. You won't be able to discern the problems and potential challenges that needs solving for the business to sustain and grow.

You won't really be able to help your organization explore new business opportunities. So this is a must-have skill.

3. Effective Communication 

Now next, we have effective communication. You'll need to explain the machine learning concepts to the people with little to no expertise in the field. Chances are you will need to work with a team of Engineers as well as many other teams. 

So communication is going to make all of this much more easier, companies searching for a strong machine learning engineer, looking for someone who can clearly and fluently translate the technical findings to a non-technical team, such as marketing or sales department.

2. Rapid Prototyping 

The next on our list, we have rapid prototyping. So iterating on ideas as quickly as possible is mandatory for finding one that works in machine learning. This applies to everything from picking up the right model to working on projects such as AB testing.

You need to do a group of techniques used to quickly fabricate a scale model of a physical part or assembly using the three-dimensional computer design which is the cat. 

1. Keep Yourself Updated 

So last but not the least, we have the final skill and that is to keep yourself updated. You must stay up to date with any upcoming changes, every month new neural network models come out. It also means being aware of the news regarding the development of the tools, the change log, the conferences and much more you need to know about the theories and algorithms. Now this you can achieve by reading the research papers, blogs, videos and attending conferences. You also need to focus on the online community with changes very quickly. So expect and cultivate these changes.

Bonus Skills 

Now, this is not the end, we have certain skills called the bonus skills, which will give you an edge over other competitors or the other persons who are applying for a machine learning engineer position in the same industry with you.

We have physics, now you might be in a situation where you would like to apply machine learning techniques to a system that will interact with the real world. Having some knowledge of physics will take you far.

Next, we have reinforcement learning. So this reinforcement learning has been a driver behind many of the most exciting developments in the deep learning and the AI community from the Alpha Go Zero to the openAI Dota 2 pot. This will be a critical to understand if you want to go into robotics, self-driving cars or other AI related areas. 

And finally, we have computer vision, out of all the disciplines out there, there are by far the most resources available for learning computer vision. This field appears to have the lowest barriers to entry, but of course, this likely means you'll face slightly more competition. 

So having a good knowledge of computer vision, how it works will give you an edge over other competitors. 

Conclusion 

So with this we come to an end of this post, I hope you got acquainted with all the skills which are required to become a successful machine learning engineer. And if you have any queries related to this post, please leave them in the comment section below and we'll answer you as soon as possible. Thank you. 

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