Nothing Can Stop You to Land on A Highly-Paid Machine Learning Job. If you own these 5 Skills

# 5 Skills that make you an out of the crowd machine learning engineer

‘Machine learning engineer’ is the hottest job of 2021. Yes, the buzz of data science career transition has now entered its deeper branching. Among all other data science branches, machine learning (ML) leads the data science job market and offers the most highly paid opportunities.

Although ML is holding high demand and millions of aspirants, have already landed in the competitive job market. Still, very few can secure a successful entry to the machine learning industry. But why so?

Well, it’s because of a lack of machine learning skills. Yes, at present, the machine learning domain is suffering from a severe skill shortage. The focus of the majority of aspirants are on the generic skill development for ‘data science career switch’, but the skill required for a core machine learning job differs a bit from that generic skill crowd.

Are you also running the race of grabbing successful Machine learning career transitions?

If so, this blog will be the key to the hidden treasure box for you. Because today I am going to tell you the five secret skills requirements that each machine learning job interviewer searches for within the candidates on their very first meet. The efficacy level of these skills decides your machine learning career growth.

So without wasting more time, let’s have a look at those skills.

**Probability and statistic**

Applied mathematics- probability and statistics is the brain of the machine learning field. You might be wondering, while every machine learning problem is solved by programming algorithms and several pre-programmed tools and applications, then why applied mathematical skill becomes the topmost priority for a machine learning expert?

Yes, you are right, ML gets solved with algorithms, but first, you have to identify which algorithm is the best fit for your problem. What type of architecture does your algorithm demand to solve an identified problem? And here comes the need for probability and statistics.

The application of different mathematical and statistical formulas help you identify the correct algorithm. Besides, a deep understanding of mathematics and statistics helps you set the right degree of confidence level and parameters of your identified machine learning algorithm model.

And do you know the majority of basic ML algorithms are derived on the foundation of several statistical modelling processes?

Now the question comes on which modules/ chapters of applied mathematics you need to concentrate on?

The expansion of applied mathematics is too vast. It’s all possible to master every aspect of this subject, especially when you have already reached the midpoint of your career path and are trying for a transition, not a fresh start.

To be extremely proficient in identification and applications of the right ML algorithms, you need to explore the expertise level of different types of distribution (e.g. Poisson, normal, linear), statistical models (‘Hidden Markov Model’, ‘Bayes model’, ‘Gaussian Mixture model’).

Apart from these, as we all know, data visualization and data modelling is a cruel part of the field, so the additional effort has to be provided on learning different statistical analysis techniques such as ‘ANOVA’, testing of different hypotheses etc.

But wait, this is not the end. While everyone will suggest you focus on only statistical and complex mathematical problem-solving techniques, no one will tell you to spare a few of your learning time on different physics concepts. But knowledge of several physics concepts will help you as a pro.

In actuality, additional knowledge in physics concepts works like the victory trump card for you during an interview. And do you know the widely used machine learning tools ‘variational inference ’ is derived from physics consent?

**Machine learning Algorithms**

The ability necessary for being a machine learning engineer is a critical analysis of several existing basic ML algorithm models to evaluate the reinforced version of the same. To make sense of machine learning, you must understand all of the algorithms. A variety of supervised, unsupervised, and reinforcement learning algorithms fall into the ML category.

As noted above, the machine learning classifiers commonly used in credit card fraud include Naïve Bayes classifier,

K-Nearest neighbour, Support Vector Machine, Apriori algorithm, Logistic Regression, etc. If you know all the math algorithms, it will be easier for you to start as a machine learning engineer.

Hyperparameter effect on learning standard algorithms are available in libraries like ‘scikit-learn’ and ‘Spark ML’. Still, there are additional parameters to take into consideration like ‘learning procedure’ (‘linear regression’, ‘gradient algorithms’, ‘support vector machine’, ‘ensemble’).

You must keep in mind the benefits and drawbacks of different methods, as well as various pitfalls like bias and overfitting. Different kinds of data science and machine learning challenges are perfect for experimenting with problems, like those on Kaggle.

**Neural Network Architecture and NLP processing**

Neural network? Although it’s fall into the deep learning subdomain of data science, the advancement of Machine Learning has also made the neural network the base of the same.

At present, lack of neural networks architecture knowledge is the biggest cause due to which aspirants lost their candidature at the first level of machine learning engineer job interviews. A neural network is mimicked on a network of our brain cells.

Multiple-input layers to an intermediate layer that passes the input data on to hidden layers, which processes it and transforms it into more useful output data. The types of operations shown here represent parallel and sequential processing in which the results are closely related to the input.

Many neural networks, such as Feedforward, Recurrent, Convolution, Modular, Radial, and others, are under development. Although it is not required that you know the entirety of these neural networks, it is critical that you understand the fundamentals to become an out of the crowd ML job interview candidate.

Along with the neural network architecture, a proficient ML engineer has to be expert enough on natural language processing.

As the name ‘machine learning’ ‘suggests learning a machine, as an ML engineer, the key responsibility of yours is to help a machine to learn the human language, that is, natural language. Until machines remain incapable of interpreting human language, then there lies no existence of machine learning, more clearly no existence of artificial intelligence.

To provide the basis for Natural Language Processing, there are several different libraries. These libraries can be used to parse the text according to its structure, find the key phrases, and remove unnecessary words. You will work with these tools if you are comfortable with one or more Natural Language Processing (NLP) APIs like the Natural Language Toolkit.

**Distributed Computing**

The volume of data with which you need to work on an ML project is too vast to handle by a single human being or even by a single machine process is impossible. Hence for effective analysis of such gigantic data sets needs effectual distribution across the entire ML clusters. And for such clusterial distribution of data set to need skill on cloud computing tools like ‘EC2’ by Amazon,’ Apache’, ‘Hadoop’, etc.

##### 5. Assessment Data Modeling

Modelling of data is a tool for evaluating a dataset structure to identify useful models (eigenvectors, clusters, correlations, etc.) and/or forecasting the properties of experienced instances (classification, regression, anomaly detection, etc.).

A continuous evaluation of the goodness of a particular model is a key element in this assessment process. You need to select an appropriate measure of precision (e.g. loss of classification log, number of squared errors, etc.) and a strategic assessment measure according to the job you are doing (training-testing split, sequential vs randomized cross-validation, etc.).

It is also very important to take these measures, even when using the regular algorithms if it is used to change the model directly (i.e. context propagation of neural networks).

Now you are well aware of the top five skills that interview panels wait to explore within a candidate while taking an interview for the post of ML engineers. So start preparing yourself from today. In case you need any guidance, visit www.skillslash.com and join our Full Stack AI and ML Program With Live Industry Experience.

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