5 Most important information on your Machine Learning resume

5 Must-Haves On Your Machine Learning Resume

Companies are today hard-pressed to find good machine learning talent, What they want from the pool of candidates, is one who already comes to the table equipped with the skill-sets, theories and coding ability needed for the task.

The skill requirement is not only restricted to the knowledge of machine learning algorithms and when to apply what, but also how to integrate and interface. The core skills required are technical, with a good understanding of mathematics, analytical thinking and problem-solving.

At the same time, these are the top 5 must-haves on your AI resume:

1. Probability and Statistics

The theories of probability are the mainstays of most machine learning algorithms. If you are familiar with probability, you are equipped to deal with the uncertainty of data. Getting a grasp of the probability theories like Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models; is a must if you want to be considered for a machine learning job that centers around model building and evaluation.

Closely linked to probability is statistics. It provides the measures, distribution and analysis methods required for building and validating models. Statistics provides the tools and techniques for creation of models and hypothesis testing.

Together, probability and statistics make the framework of ML model building. So this is the first thing to consider when building your machine learning resume.

2. Computer Science and Data Structures

Machine learning works with huge data sets, so a fundamental knowledge of computer science and the underlying architecture is a compulsory attribute. Expertise in working with big data and analytics, and complex data structures, are a must. Thus a formal course or degree in computer science is compulsory for a machine learning career. Additionally, your resume must display your skills at working with parallel/distributed architecture, data structure like trees and graphs, and complex computations. These are required to apply or implement, at the time of programming. Take additional certification for practicing problems and coding, and hone your ability with big data and distributed computing.

3. Programming Languages – C/C++, R, Python, Java

To apply for a job in ML, you obviously need to learn some of the commonly used programming languages. Although machine learning is largely bound by concept and theory, it implements any language with the essential components and features. Some programming languages are considered especially suited to complex machine learning projects. So a working knowledge of these programming languages makes add to your resume.

C/C++ are used where memory and speed are critical, as they help to speed up the code. Many machine learning libraries are also developed in C/C++ as they are suited for embedded systems. Python, R & Java work very well with statistics. Despite being a general programming language, Python has several machine learning-specific libraries that find a use for efficient processing. Knowledge of Python helps to train algorithms in various computing architecture. R is an easy-to-learn statistical platform, increasingly used for machine learning and data mining tasks.

Having a degree, certificate or online diploma in these languages, make for a good resume. As an engineer or student of science, you may already be skilled in C++, Java, and Python. You can also learn these languages online in your spare time, and practice on projects for special mentions on your CV. Programming languages like Python and R and their packages make it easy to work with data and models. Therefore, it is reasonable to expect a data scientist or machine learning engineer to attain a high level of programming proficiency and understand the basics of system design.

4. Machine Learning Algorithms

Applying machine learning libraries and algorithms is part of any ML job. If you have mastered the languages, then you will be able to implement the inbuilt libraries created by other developers for open use. For instance, TensorFlow, CNTK or Apache Spark’s MLib, are good places to work upon. You can also begin with practicing programming algorithms on Kaggle. This can find mention in your resume as well.

However, to get considered for a machine learning job vis-a-vis other competing applicants, you need to have the know how to implement these effectively and in which scenario.

5. Software Engineering and Design

Software Engineering and System Design, are typical requirements of a machine learning job role. A good system design works seamlessly allowing your algorithms to scale up with increasing data. Software engineering best practices are a necessary skill on your resume. As a ML engineer, you create algorithms and software components that interface well with APIs. So technical expertise in software designing is a must while applying for a machine learning job.


An application for machine learning job role requires careful planning and consideration. Machine learning is all about algorithms, which in turn stems from a good knowledge of big data analytics and requisite programming languages. Sound engineering or technical background is a must. However, the applicant who includes as many of the required skills in the resume stands a better chance of getting selected. So, are you all set for a career in machine learning?

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