49 Machine Learning developers in 27 agencies found

Machine Learning Engineer
• experience developing open-source ML projects, data analysis, cleaning and preparation data for further processing, ETL pipelines; marketing and financial modeling. • experience with actual Python...
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Updated: 16 Jan 2018
Python Engineer, Machine learning enthusiast
I have 5+ years in IT industry. Started as 3D artist and worked with Autodesk Maya and MentalRay, used Python and MEL(Maya Embedded Language) for scripting shaders. Later worked on different projects...
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Updated: 16 Jan 2018
Data Scientist specialized in Computer Vision and Machine Learning
An accomplished Data Scientist with thorough knowledge of machine learning, computer vision, and data mining. He also has a solid background in Python programming. Being particularly interested in Con...
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Updated: 16 Jan 2018
Machine learning engineer and software developer with extensive experience.
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Updated: 26 Mar 2018
MO
A CTO and co-founder of an AI company. Extensive machine learning and deep learning experience
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Updated: 28 Mar 2018
Specialized in iOS and Ruby on Rails development. Passionate about AI & Machine Learning with one year experience in Deep Learning.
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Updated: 02 Apr 2018
Data Scientist specialized in NLP and Machine Learning
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Updated: 16 Jan 2018

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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.

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