Machine Learning Resume Sample: how to build a strong ML Resume

Tips to Make Machine Learning Resume

Companies today, are hard-pressed to find good machine learning talent. Any specific skill requisites, of course, depend on the machine learning roles and profiles, but some skills that must be present on your machine learning resume are consistent across profiles. Mostly, companies want candidates who already come with a large pool of diverse machine learning skills, theories and coding ability so that they can cross function on ML projects if need be.
Experts of this domain not only need to have a sound 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. While the specific skill requirements for each profile differ, there are core ML skills that are constant for all roles.

What are the Must-Have Skills for an AI Resume

1.Probability and Statistics: The theories of probability are the mainstays of the most machine learning algorithm. Being familiar with probability enables you to deal with the uncertainty of data. Getting a grasp of the probability theories like Python, 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. It also provides the tools and techniques for the creation of models and hypothesis testing.
Together, they make the framework of the ML model building. 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 fundamental knowledge of computer science and the underlying architecture is compulsory. Expertise in working with big data analytics, and complex data structures, are a must. Thus, a degree or a formal course in these domains is required for a machine learning career. 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. Additional certifications for practising problems and coding will hone your ability with big data and distributed computing. Experience in computer science applications will go a long way in securing you a job in this field.

3. Programming Languages — R, Python, Java: To apply for a job in Machine learning, you are required to learn some of the commonly used programming languages. It implements any language with the essential components and features, even though it is largely bound by concept and theory. Some programming languages are considered especially suited to complex machine learning projects. So, working knowledge of these programming languages adds value to your machine learning resume.

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

Common skills that employers look for on an ML Resume

Apart from the must-have particulars, here is a basic checklist that can enhance your resume.

• A Bachelor’s degree in either computer science or in a related field.
• A good amount of prior experience with GPU computing and data mining.
• A general background in NLP and deep learning, along with their corresponding tools and techniques.
• Basic experience with agile software development practices.

And finally, some character traits that are looked out for also include:

• Analytical and critical thinkers
• Data-driven performers
• Clear communicators to translate and understand complex information.
• Problem solvers and innovators.

How to Master Programming Languages?

A degree, certificate or online diploma in these languages , ensure 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 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.

• 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 practising programming algorithms on Kaggle. You can mention this in your ML resume as well.
• Software Engineering and Design: Software Engineering and System Design, are typical requirements for an ML job. A good system design works seamlessly, allowing your algorithms to scale up with increasing data. Software engineering practices are a necessary skill on your resume. As an 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.

Creating your Machine Learning Resume

Now that you have an idea about the required skills and prerequisites for a career in Machine Learning, the next step is to put it all together into a well-planned resume. It is important to keep some general tips in mind, including:

• There is no need to downplay your achievements and success. If there’s a place to boldly talk about your accomplishments, it’s on your resume.
• There is no need to fill every inch of your resume with text. White spaces provide a cleaner look to the document, making it much easier for the reader to comprehend. A good idea will be to adapt existing templates online, that equate well to your preferences.
• Ensure that the writing is concise and to the point; eliminate any extra verbiage, unless necessary.
• Do not confine your resume to a single page, there is no one-page mandate. As long as there exists relevant experience, the extra room is justified.
• Have it proofread, either online (on tools like Grammarly) or by a family member. This is useful to spot unseen errors and provide an outside perspective.

Important information that your Machine Learning Resume should include are

1. Header
2. Personal Summary
3. Experience
4. Projects
5. Education/Certifications
6. Skills
7. Reference

Machine Learning Sample Resumes for reference

Pro Tip 1: If you are a fresher or an entry-level professional, give detailed information about the projects that you have done. Pro Tip 2: Do not shy away from giving all possible details about your work experience and achievements. Flaunt what you have achieved.

Pro Tip 2: Do not shy away from giving all possible details about your work experience and achievements. Flaunt what you have achieved.

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. By including these skills in your machine learning resume, you are increasing your chances of being selected. So, are you all set for a career in machine learning?
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