Machine Learning Engineer Skills That Will Get You Hired
If you’re thinking about a career as a machine learning engineer, here are two essential things you should know. Research and academic backgrounds are not a requirement. Learning machine language isn’t purely a role in academia. Additionally, either having software engineering experience or data science experience is not sufficient. It is ideal to have both. The critical difference is that the end goal is the key distinguishing factor. A machine learning expert must also understand how data analysts, data scientists, and data scientists differ.
Analyzing data to tell a story gives you actionable insights, as does analyze data for your team members. Humans perform and present the analysis, which use by other human beings to make business decisions based on the outcomes. Your output intends for human consumption. In contrast, one of the outputs of a machine learning engineer is working software (not the analyses or visualizations you may produce along the way). This output typically uses by other software components that run autonomously without any direct human involvement.
Machine Learning Skills to Grasp Hiring Opportunities
Machine learning still requires actionable intelligence, but machines’ decisions now make, and their actions determine how a product behaves. To succeed in Machine Learning, you need software engineering skills. In between the two worlds lives a data scientist. Software engineers should perform data analysis, and insight extraction should perform by software engineers who can collect, clean, and organize data. Their communication skills are also vital to success in the machine learning process.
With that being said, let’s now get down to business. We will further be discussing the fundamental requirements for machine learning engineers. There are two primary parts to these skills, and Languages and libraries. That will cover ideas of the learning process. For now, we’ll focus on the skills, and in a future post, we’ll discuss languages and libraries.
Computer Fundamentals and Programming
Computer science fundamentals important for machine learning engineers include the following:
Many data structures, including stacks, queues, multidimensional arrays, trees, graphs, etc. Various algorithms use to search, sort, optimize, program, etc. The concepts of computing efficiency and complexity — P vs. NP, problems with no solution, Big-O notation, approximate algorithms, etc. Furthermore, other aspects of computer architecture include memory and cache, bandwidth, deadlocks, and distributed processing. Programming requires to implement, adapt or deal with them (as needed). Code competitions, hackathons, and practice problems are all great ways to sharpen your skills.
Probability and Statistics
Many machine learning algorithms deal with uncertainty by recognizing probabilities (conditional probability, Bayesian rule, likelihood, independence, etc.). Implementing techniques derived from these are helping to build great ideas to learn machine learning. Similarly, statistics provides various measures, distributions, and analysis methods that help develop and validate models using observed data. Statistics modeling procedures are the basis of many machine learning algorithms.
Data Modeling and Evaluation
An analysis of data consists of estimating its structure and using that information to find valuable patterns (correlations, clusters, eigenvectors, etc.) and predict properties of previously unknown instances (classification, regression, anomaly detection, etc.). Evaluation of how good a model is is key to the estimation process. If the task at hand is classification, choose an appropriate error measure, and choose an optimal evaluation strategy (e.g., sequential vs. randomized cross-validation).
Even when just applying standard algorithms, leveraging resulting errors to tweak the algorithm is very important (for backpropagation with neural networks). It is essential to understand these measures even if you don’t plan on applying them.
Applying Machine Learning Algorithms and Libraries
Several libraries/packages/APIs provide standard implementations of machine learning algorithms (such as sci-kit-learn, Theano, Spark MLlib, H2O, TensorFlow, etc.). It requires selecting an appropriate model (decision tree, nearest neighbor, neural net, support vector machine, and the like). It is essential to understand how hyperparameters affect the learning process (linear regression, gradient descent, genetic algorithms, bagging, boosting, etc.) and how they affect the data fit.
Aside from knowing how different approaches differ, you should also know how numerous pitfalls can catch you off guard (bias and variance, overfitting and underfitting, missing data, data leakage, etc.). Kaggle challenges like those related to data science and machine learning provide a great way to experience different problems.
Software Engineering and System Design
An engineer who works in machine learning produces software as their primary output. The product or service is often just a tiny part of a larger ecosystem. Have a good understanding is essential of how these different pieces integrate (using calls to libraries, REST APIs, database queries, etc.) and build appropriate interfaces for your component that others will depend on other combines of machine learning. A careful design of your system is needed to avoid bottlenecks and ensure that your algorithms scale well with more data.
Software engineering best practices include learning points such as requirements analysis, system design, modularity, documentation, etc. These are important for productivity, collaboration, quality, and maintainability, which give brief information about the machine learning language. Machine learning engineers will always be in demand as the world is undoubtedly changing rapidly and dramatically. Many challenges are facing the world, and they require complex systems to solve them.
These were some of the in-demand skills that will help you in getting hired. So if you want to be an engineer specializing in machine learning and see this as your future. Then there’s no better time than now to start learning the skills and developing the mindset you’ll need to be successful.