Career in

Machine Learning  Engineering 

# Work in progress

 

People call u

Machine Learning Engineer / ML Engineer

What they are

  •  Responsible to study and transform data science                  prototypes.

  • Responsible to design machine learning systems.

  • Responsible for research and implement appropriate ML algorithms and tools.

  •  To develop machine learning applications according to requirements.

  • Select appropriate datasets and data representation methods.

  • Run machine learning tests and experiments.

  • Perform statistical analysis and fine-tuning using test results.

  •  Responsible to train and retrain systems when necessary.

  • To extend existing ML libraries and frameworks.

  • Keep abreast of developments in the field.

What they need

  • Proven experience as a Machine Learning Engineer or similar role.

  • Understanding of data structures, data modeling and software architecture.

  • Deep knowledge of math, probability, statistics and algorithms.

  • Ability to write robust code in Python, Java and R.

  • Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn).

  • Excellent communication skills.

  • Ability to work in a team.

  • Outstanding analytical and problem-solving skills.

  • BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus.

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How Rohan started his dream in Machine Learning :

  • How to learn Machine Learning ?

  • I have seen most of us asking how to learn ML and become a complete ML engineer. Here, I have tried to cover most of the important things in the ML that I have learned the hard way. Learning ML is a step by step process, so must learn it step by step. So this guide will also show how to learn ML step by step. No one can become a complete  ML engineer in 30 days, so it’s a journey. This guide will also show some best ML tutorials.

  • Machine Learning with Python :

  • To download  and install Anaconda :

           Anaconda : Anaconda is a free and open-source distribution of the                         Python and R programming languages for scientific computing, that aims               to simplify package management and deployment.

           Download Anaconda : https://www.anaconda.com/distribution/

 

  • Machine Learning with Scikit.

​          Scikit learn : Scikit-learn is a free software machine learning library for the             Python programming language.It features various classification,                             regression and clustering algorithms including support vectormachines,                 random forests, gradient boosting, k-means and DBSCAN, and is                           designed to interoperate with the Python numerical and scientific libraries               NumPy and SciPy.

  • practise advanced libraries :

         Pytorch , TensorFlow , Dlib. These libraries can be imported to                               python  using  anaconda.

         Pytorch : It is a machine learning library based on the Torch library, used               for applications such as deep learning and natural language                                   processing. It  is free and open-source software .

         TensorFlow : It is a free and open-source software library for dataflow                     and differentiable programming across a range of tasks. It is a symbolic                 math library, and is also used for machine learning applications such as                 neural networks. 

         Dlib :  Dlib is a general purpose cross-platform software library written in                 the programming language C++. Its design is heavily influenced by ideas               from design by contract and component-based software engineering.                     Thus  it is, first and foremost, a set of independent software components.

   Help and suggestions

 Stuck with some problem while developing  the    application ?

            Communication channels
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             https://www.linkedin.com/groups/86481/