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8 Most Essential Skills Needed to Become a Machine Learning Engineer in 2020

This blog will help you understand the technical and non-technical skills required to become a Machine Learning (ML) Engineer in a renowned organization in 2020.

You must register for a Machine Learning online course to acquire these skills and become a Machine Learning Engineer. You will learn the key ML concepts and attain all the skills needed to become a successful ML Engineer.

First, let’s discuss what Machine Learning is and who Machine Learning Engineers are.


What is Machine Learning?

Machine Learning (ML) is an advanced technology that allows systems to automatically acquire knowledge and make necessary improvements without being explicitly programmed from past data and experiences. It is a subset of Artificial Intelligence (AI) that allows you to develop computer programs that can access the organization’s data and use it to learn independently with the help of deep learning algorithms and technologies.

Now, let’s learn about Machine Learning Engineers and their role.

Who is a Machine Learning Engineer?

Machine Learning Engineers are programmers who build systems and machines that can learn and apply knowledge without receiving any specific guidance. They use numerous ML algorithms and libraries to develop these systems.

Let’s read in detail about the various roles of a Machine Learning Engineer.

Part of a Machine Learning Engineer

The major functions of a Machine Learning Engineer in an organization include:

  • Research and design ML systems
  • Understand and further transform the Data Science prototypes
  • Verify the quality of data and ensure it through data cleaning
  • Perform experiments and tests on ML models
  • Develop ML models as per the business requirements
  • Perform various training models before tuning the hyperparameters
  • Fine-tune the results of testing and perform statistical analysis
  • Implement the correct ML algorithms
  • Select the right training data sets to develop the Machine Learning models

7 Popular Jobs in Machine Learning

There are numerous jobs available for Machine Learning professionals today. Following are some of the most popular job opportunities for these professionals:

  • Machine Learning Engineer
  • Data Scientist
  • Software Engineer
  • AI Engineer
  • NLP Engineer
  • Machine Learning Quality Analyst
  • Deep Learning and AI Researcher

In recent years, the demand for ML Engineers has grown immensely. As per a report by Gartner, there will be over 2.3 million Machine Learning job opportunities in 2020.

Skills Required to Become a Machine Learning Engineer

Numerous technical and non-technical skills are required for you to become a successful Machine Learning Engineer.

5 Technical Skills that you need to have to Become an ML Engineer

Let’s read about the five technical skills you need to become a Machine Learning Engineer.

Programming Skills and Fundamentals of Computer Science

To become a Machine Learning Engineer and have a promising career in this technology, you must have a Computer Science field background. Graduates or professionals like you who are looking for Machine Learning jobs must have a good understanding of the following:

  • Programming languages such as Python, Java, R, and C++
  • Data Structures concepts like queues, stacks, trees, graphs, etc.
  • Searching, sorting, dynamic programming, optimization, and other such algorithms
  • Complexity and computability of big-O notation, P vs. NP, NP problems, approximate algorithms, and more
  • The computer architecture, including cache, memory, deadlocks, bandwidth, and distributed processing

You should have the ability to apply and implement these skills while programming.

Statistics and Probability

You must be familiar with mathematical and statistical concepts such as algebra, calculus, mean, median, mode, vectors, standard deviation, matrices, matrix multiplication, etc. Further, you must understand conditional probability, likelihood independence, Bayes rule, and techniques such as Markov Decision Processes, Gaussian Mixture Models, Bayes Nets, and Hidden Markov Models. Also, knowledge of mean, median, mode, and analysis methods like hypothesis testing and ANOVA is required.

Data Modeling and Evaluation

Data Modeling is used to estimate and find useful patterns like clusters, correlations, etc., from the dataset’s underlying structure or interpret properties of instances like their classification, anomaly detection, and regression. The main focus of this process is to evaluate the model given continuously. Based on the given task, you must choose the suitable error measure—sum-of-squared errors for regression, log-loss for classification, etc.—and evaluation strategies such as sequential vs. randomized cross-validation and training-testing split.

Redundant learning algorithms generally use resulting errors directly to tweak the data model. This is why you must learn these measures in-depth and apply standard algorithms.

Applying ML Libraries and Algorithms

You would not have to keep developing new algorithms as an ML Engineer. Rather, it would help if you learned to use the existing libraries and algorithms. There is a range of libraries and APIs like Microsoft’s Cognitive Toolkit (CNTK), Google TensorFlow, and Apache Spark MLlib. To apply these technologies, you must understand all these concepts and learn how to integrate them with various systems.

System Design and Software Engineering

To have a promising ML career, you must have a strong base in system design and software engineering. You should have the ability to develop suitable interfaces for your system component. Besides, you should also know about modularity, requirements analysis, testing, version control, and documentation to improve quality, productivity, and maintainability.

Three. Must-have Non-Technical Skills to be a Machine Learning Engineer

You will now learn about the must-have non-technical skills to become a Machine Learning professional.

Knowledge of Industry

Those projects that address real-life issues are the most successful ML projects. Further, you must know your industry and how it works for your business organization’s benefit. If you do not understand the elements that help your organization succeed, you will not channel your technical skills effectively and productively. Moreover, you may not detect the issues and challenges that must be solved for the business’s growth and sustainability.

Effective Communication Skills

You will explain various ML concepts to employees with negligent knowledge of this technology. With good communication skills, you can deliver your knowledge more thoroughly, allowing your team to clearly understand ML concepts.

Organizations that hire ML Engineers look for professionals who can translate their technical knowledge fluently and clearly to technical and non-technical teams.

Keep Yourself Up-to-date

It would help if you stayed updated concerning any new and upcoming technologies and technological changes. New neural network models are released every month, and they all outperform the previous version and architecture. You must also know the various news based on developing new algorithms and tools via blogs, research papers, and conferences.

About author

I work for WideInfo and I love writing on my blog every day with huge new information to help my readers. Fashion is my hobby and eating food is my life. Social Media is my blood to connect my family and friends.
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