TOP REQUIREMENTS
We live in a world of people and machines. Humans have been evolving and learning from their past experiences for millions of years. Then again, the period of machines and robots has recently started. The future of machines is huge and beyond our imagination. We leave this big responsibility on the shoulders of a specific individual, namely the machine learning engineer. So let's take a look at some of the machine learning engineer skills needed to become a successful ML engineer.
In this article I will cover the accompanying subjects:
፨ Who is an ML Engineer?
፨ Technical skills
፨ Non-technical skills
፨ Bonus skills
So before we get into the skills of a machine learning engineer, let's first clarify who exactly an ML engineer is.
Who is a Machine Learning Engineer?
AI engineers are modern developers who foster machines and frameworks that can learn and apply information without explicit heading.
Man-made consciousness is the objective of an AI engineer. They are computer programmers, but their focus goes beyond the specific programming of machines to perform specific tasks. They create programs that allow machines to perform actions without being specifically ordered to perform those tasks.
Machine learning engineer skills
Programming languages (R/Java/Python/C++)
The first and most important requirement is to have a good command of a programming language, preferably python because it is easy to learn and its applications are wider than any other language. Generally involved Python is the language for AI.
It is important to have a good understanding of topics such as data structures, memory management, and classes. Although Python is a very good language, it cannot help you by itself. You will probably need to learn all these languages like C++, R, Python, and Java and also work on MapReduce at some point.
10: Statistics
Knowledge of matrices, vectors, and matrix multiplication is required. A good understanding of derivatives and integrals is necessary because even simple concepts like gradient descent can elude you.
Statistical concepts such as mean, standard deviation, and Gaussian distribution are required along with probability theory for algorithms such as naïve Bayes, Gaussian mixture models, and hidden Markov models.
9: Signal Processing Techniques
Also, one of the few skills of a machine learning engineer is to understand signal processing and be able to solve various problems using signal processing techniques because feature extraction is one of the main pieces of AI.
Knowledge of time-frequency analysis and advanced signal processing algorithms such as Wavelets, Shearers, Curvelets, and Bendlets will help you solve complex situations.
8: Applied Mathematics
A lot of machine learning techniques out there are just fancy types of function approximations. Having a good understanding of algorithm theory and understanding subjects like gradient descent, convex optimization, quadratic programming, and partial differentiation will help you a lot.
7: Architectures of Neural Networks
Brain networks are a class of models in general AI writing. Brain networks are a particular arrangement of calculations that have upset AI.
We need machine learning for tasks that are too complex for humans to code directly, that is, tasks that are so complex as to be impractical. Neural networks are themselves approximations of general functions and thus can be applied to almost any machine learning problem of learning a complex mapping from an input to an output space.
Neural networks have been by far the most accurate way to approach many problems such as translation, speech recognition, and image classification.
6: Language, Audio, and Video Processing
Because natural language processing combines two main fields of work, i.e. Linguistics and Computer Science, it is likely that at some point you will work with either text or audio, or video. So it is necessary to have good control over libraries like Genism, NLTK, and techniques like word2vec, sentiment analysis, and summarization.
Voice and sound examination include extricating helpful data from the sound signs themselves. Being well-versed in math and Fourier transform concepts will get you far in this.
Now that we've seen the technical skills of a machine learning engineer, let's look at the skills of a non-technical machine learning engineer
5: Knowledge of The Field
The most successful machine learning projects will be those that address real pain points. No matter what industry you work for. You should know how the industry works and what will benefit the business.
If a machine learning engineer does not have the business acumen and know-how of the elements that make up a successful business model, all these technical skills cannot be channeled productively. You will not be able to recognize the problems and potential challenges that need to be solved in order for the business to sustain and grow. You won't actually be able to help your organization explore new business opportunities.
4: Effective Communication
You will need to explain ML concepts to people with little or no expertise in the field. It is likely that you will have to work with the engineering team as well as many other teams. Communication will make it all much easier.
Companies looking for a strong ML Engineer are looking for someone who can clearly and fluidly translate their technical findings to a non-technical team such as marketing or sales.
3: Rapid Prototyping
Finding one that works requires iterating ideas as quickly as possible. In machine learning, this applies to everything from choosing the right model to working on projects like A/B testing.
You need to perform a group of techniques used to quickly create a scale model of a physical part or assembly using three-dimensional Computer-Aided Design (CAD) data.
2: Keep Up To Date
You must be informed of all upcoming changes. New neural network models are released every month that outperform the previous architecture.
It also means staying up to date with developments in the theory of tools and algorithms through research papers, blogs, conference videos, etc. The online community is changing rapidly.
1: Machine Learning Engineer bonus skills
Perhaps you are in a situation where you would like to apply machine learning techniques to systems that will interact with the real world. Having some knowledge of physics will take you a long way.
Reinforcement learning was the driver behind many of the most exciting developments in deep learning and artificial intelligence in 2017. This will be essential to understand if you want to pursue robotics, self-driving cars, or any other field related to artificial intelligence.
Computer vision and machine learning are two fundamental branches of computer science that can operate and power very sophisticated systems that rely solely on CV and ML algorithms, but when you combine them, you can achieve even more.
So this finishes us off of this article. I hope you have understood the various technical and non-technical skills of a machine learning engineer that are required to become a successful ML engineer.
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