79 Machine Learning developers in 44 agencies found

MK
Digital Consultant: Data Scientist, Business Analyst
All rates are indicative, annual rate includes 20% discount
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Updated: 17 Jun 2019
Senior Mobile Developer (iOS, Android), ML&CV Engineer
All rates are indicative, annual rate includes 20% discount
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Updated: 14 Jun 2019
Machine learning engineer
- optimization enthusiast, - clean code lover - computer vision fan - have a will to work - determined in solving problems
1 Project completed
GMT+1 West/Central Europe
All rates are indicative, annual rate includes 20% discount
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Updated: 06 Jun 2019
All rates are indicative, annual rate includes 20% discount
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Updated: 07 Jun 2019
Over 7 years of scientific research experience and more than 4 years of working experience in IT industry.
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Updated: 11 Jun 2019

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Machine Learning Developer: 12 Must Have Technical Skills

Machine learning and artificial intelligence are now the most exciting and challenging domains of information technology. Machine learning requires results that are as exact as possible, so machine learning engineers should be able to think logically and be familiar with complex mathematical calculations. Below you can find a list of other technical skills that are typically required from machine learning developers.

12 Key Technical Skills to Help You Choose a Professional Machine Learning Engineer

  1.   Understanding of data structures, such as stacks, arrays, trees, graphs, queues, etc.
  2.   Familiarity with computer architecture fundamentals, such as bandwidth, deadlocks, distributed processing, memory, cache, etc.
  3.   Hands-on experience with at least one of the following programming languages: Java, Python, R, Matlab, or C++.
  4.   Understanding of computability and complexity concepts, such as P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.
  5.   Expertise in machine learning techniques and algorithms, such as Naive Bayes, K-means, regression, decision tree, ANN, support vector machine, neural networks, or maximum entropy algorithms.
  6.   Knowledge of Unix tools, such as awk, cat, cut, find, grep, sed, sort, tr, and so on (because the machine learning activities are typically carried out in a Linux environment).
  7.   Familiarity with big data database tools, such as Hadoop. Ability to create distributed applications by using Hadoop and other solutions.
  8.   Knowledge of probability and statistics, because the machine learning algorithms are usually derived from statistical models and predictions.
  9.   Understanding of data science project lifecycle, data acquisition, and data collection.
  10.   Proficiency in software design, such as web APIs, static and dynamic libraries, etc.
  11.   Knowledge of advanced signal processing techniques.
  12.   Understanding of data modeling and evaluation.

In addition to technical (hard) skills, machine learning engineers should also demonstrate a range of soft skills, such as intellectual curiosity, analytical thinking, decision-making, proactivity, and strong communication skills.

To sum up, because there is a high demand for machine learning developers nowadays, we wish you good luck in finding a perfect candidate for your project before someone else finds them.