If you are a software engineering major, data science researcher, or tech enthusiast in general then you must be aware of the leading sub-field of Artificial Intelligence (AI) known as Machine Learning.

You’re must also be aware of the many fascinating applications of AI ranging from simple pattern detection and speech recognition to smart integrated virtual assistants. These applications and much more are made possible by the efforts of Machine Learning Engineers.

This article goes over who these Engineers are, what they do, and what skills are necessary in order for you to become a proficient ML Engineer.

## What do Machine Learning Engineers do?

Machine Learning (ML) Engineers combine analytical and problem-solving mathematical skills with software programming technology in order to create AI systems to solve real-world problems. An ML Engineer is required to function as a data analyst to work with and transform data according to the given requirement and build, train, validate and test ML algorithms in the form of a model on the given dataset.

Such Engineers may be working with a company in the tech department, independently as a programmer or as a researcher on cutting-edge ML problems. Either way, there are a number of skill requirements that need to meet in order to qualify as an ML Engineer. These skills have been discussed in detail below.

## 5 Must-Have ML Skills

### 1. Probability and Statistical Analysis

One of the prerequisites of ML includes an intermediate understanding of topics including probability and statistics. This is necessary as ML algorithms and models are based on these mathematical principles and cannot be constructed without them.

Probability is especially important when it comes to dealing with inputs, outputs, and real-world uncertainty. Some principles of probability used in ML include conditional probability, Bayes rule, likelihood, and independence. Stats provide us with measures necessary for constructing ML models including mean, median, variance, distributions (uniform, normal, binomial, Poisson), and analysis methods including hypothesis testing.

### 2. Fundamentals of Programming

Another prerequisite of ML is having a basic understanding of programming. This includes a fair understanding of data structures, including stacks, queues, multi-dimensional arrays, trees, graphs, etc., and algorithms, including searching, sorting, optimization, dynamic programming, etc.

#### Choose your language

In terms of programming languages, the best to learn for ML is Python followed by Java. This is because Python has the greatest online support in terms of available code, frameworks, and community help.

#### Get to know your IDE

The next step is familiarizing yourself with an Integrated Development Environment (IDE). Since we are handling fairly larger amounts of data your IDE cannot be a simple Command Line Interface (CLI) rather a tool like Visual Studio Code or Jupyter Notebook. Like Python, Jupyter has the greatest online support and is used by many ML instructors for educational purposes as well.

#### Understand Libraries

Libraries are a collection of resources that need to be imported into a program before using. There are a number of ML libraries like TensorFlow, Keras, PyTorch, Pandas, Matplotlib, Numpy, etc. It is important for an ML Engineer to have a good understanding of ML and data handling libraries to make programming easier and more interactive.

### 3. Data Modeling and Evaluation

One of the essential parts of ML is the process of estimating the underlying structure of a given dataset in order to find useful patterns, i.e. correlations, clusters, eigenvectors. We would also need to predict the properties of data instances including regression, classification, and anomaly detection. An ML Engineer needs to be able to evaluate a given model using an accuracy metric and strategy.

### 4. Applying Machine Learning Algorithms

Another essential part of ML is being able to apply ML algorithms. It is worth noting that building your own model is uncommon as a number of ML models and implementations are already available in libraries like Keras and scikit-learn. However, applying these models in the most effective manner and in accordance with the dataset requires both skill and a good level of understanding of ML models in general.

An ML Engineer also needs to be aware of the relative advantages and disadvantages of different approaches and possible issues like overfitting, underfitting, bias, and variance problems.

### 5. Build Neural Networks

Neural Networks (NNs) are part of a sub-field of ML known as Deep Learning and are a more extended requirement when it comes to essential ML skills. However, in the more practical applications of ML, we do need to have some understanding of NNs to create more powerful models for our AI systems.

An NN makes use of layers and neurons to create powerful ML models. An ML Engineer should be able to build, train, validate and test NNs.

## Conclusion

You should now have a good understanding of who Machine Learning Engineers are, what they do, and what skills are necessary for you to start your journey. You need to be well-versed with probability, statistical analysis, programming, data modeling, algorithms applications, and building neural networks to build powerful AI and ML solutions.

Let us know in the comments if the article was helpful and what you think the most important skill for becoming a proficient ML Engineer is.

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