Machine Learning resume some tips
Today, the company is difficult to find good talent machine learning. Of course, any requirement of specific skills are required depending on the use and machine learning programs, but your machine learning curriculum vitae must possess certain skills are consistent across the various program requirements. Often, companies want the interviewer has a wealth of machine learning skills, theory and coding ability, to be able to participate in inter-departmental machine learning projects when needed.
Experts in the field not only has a solid level of machine learning algorithms to understand when and algorithm of the application, but also need to know how to integrate and interface. The required core skills are specialized and requires a good understanding of mathematics, analytical thinking and problem-solving skills. Although the specific skills of each project documentation requirements vary, but for all roles, the core of machine learning skills are the same.
Must appear on the resume skills
Probability and Statistics
Probability theory is the main content of most machine learning algorithms. Familiar probability can enable you to deal with the uncertainty of the data. If you're in the model building machine learning and assessment work-related, such as probability theory to master Python, Gaussian mixture model and hidden Markov models and so on, is very necessary.
Closely related to the probability theory is statistics. It provides the necessary measures to build and validate the model, distribution and analysis. It also provides tools and techniques for creating models and testing hypotheses.
Together, they form the framework for machine learning model. This is the first thing the production of machine learning resumes to be considered.
Computer science and data structures
Machine learning to use large data sets, it is necessary to master the basics of computer science and the underlying architecture must also have the expertise and complex data analysis of large data structure. Therefore, the degree or formal curriculum in these areas is in machine learning profession requires. You must resume show you parallel / distributed architecture, data structures (e.g., trees and FIG.) And the complex computing skills. These skills are in project implementation or application need. For practical problems and additional coding certification will enhance your ability to handle big data and distributed computing. Experience in computer science applications will provide you with great help in the work in this area.
Programming language
R, Python, Java: To get a job as a machine learning, you need to learn some common programming languages. Although it is largely bound by concepts and theories, but it has in any language are essential components and functions. Some programming languages are considered particularly suitable for sophisticated machine learning project. Therefore, knowledge of these programming languages can highlight the parts of your machine learning curriculum vitae. .
When both the speed and memory needed both, using C / C ++ code that helps to improve the speed. Because many machine learning library is developed using C / C ++, so they are also suitable for embedded systems. Java, R and Python in statistical data do well. Although Python is a general-purpose programming language, but it has several libraries specific to machine learning, they can effectively have to deal with machine learning project. Python knowledge helps training algorithm in a variety of computing architectures. R is an easy to learn platform statistics, it is increasingly used in machine learning and data mining tasks.
Common skills resume
In addition to the essential details, there's a basic list, you can make your resume more exciting
- Bachelor's degree in computer science and related fields
- Rich GPU computing and data mining experience
- Natural language processing and general background of deep learning, as well as the appropriate tools and techniques
- Have general experience in agile software development practices.
Finally, some of the character traits attention, including: - Analytical and critical thinkers
- Data-driven performer
- Translation ideas and understand complex information clear communicator
- Solvers and innovators problem.
How to master a programming language
Degree, certificate or diploma online use these languages, you can make sure you have a good resume. As an engineer or science student, you probably already proficient in C ++, Java and Python. You can also learn these languages in their spare time online, practice on the project and resume specifically mentioned. And R such as Python programming language model and the processing data becomes easy. Therefore, the expected data scientist or engineer machine learning can achieve a higher level of programming and understand the basic design of the system is reasonable.
Machine learning algorithms
Application of machine learning algorithms and libraries are part of any machine learning to work. If you have mastered these languages, you can achieve for open use built-in library created by other developers. For example, TensorFlow, CNTK or Apache Spark of MLib is a very good machine learning platform. You can also start practicing programming algorithms on Kaggle. You can also mention in your resume that machine learning.
Software Engineering and Design
Software engineering and system design are typical requirements of machine learning work. A good system design is seamless, so that your algorithm can be expanded with the increase of data. Software engineering practices are essential skills on their resumes. As machine learning engineer, you are able to create good interaction with the API algorithms and software components. Therefore, when applying for jobs machine learning, software design expertise is required.
How to create a good resume
Now that you know the machine learning skills and prerequisites needed for occupation, then the next step is to put all these elaborate plans to resume. It is important to keep in mind some general tips include:
- You do not need to underestimate the achievements and success. If there is a bold talk about your achievements place it on your resume.
- No need to fill in all the text of every resume. Spaces make the document look neater, to make it easier for readers to understand. Online is a good idea to adapt existing templates to make it exactly to your preferences.
- Ensure that the text concise; unless necessary, otherwise remove any excess crap.
- Do not limit your resume on a page, not a requirement. As long as relevant experience, additional space is reasonable.
- Or online proofreading by their families. This discovery invisible errors and provides other person's point of view is useful.
Some important information your resume should include machine learning is
- beginning
- Personal summary
- experience
- project
- Education / Certificate
- skill
- reference
Machine Learning resume template
Pro Tip 1: If you are a beginner or an entry-level professionals, please provide detailed information on completed projects. 
Pro Tip 2: Do not avoid all possible details about your work experience and achievements. Show off the achievements you have made. 
Apply machine learning job requires careful planning and consideration. And machine learning algorithms are all about, and the algorithm has a wealth of knowledge from large data analysis and the necessary programming languages. Good engineering or technical background is necessary. Machine learning history is included in these skills, you can increase the chance of being selected.
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