How to become a Machine Learning Engineer?

  • Sorting, searching, optimization, memory, cache, deadlocks, and bandwidth are concepts that you may need to master to excel in your Machine Learning profession.
  • Mathematics & Statistics
  • Mathematical play an important role in understanding the working of Machine Learning, and the use of different algorithms for business problems.
  1. Linear Algebra — Matrices (To Perform Simple Operations) & Vectors (To Analyse Customer Behaviour)
  2. Calculus — Differential Calculus and Integral Calculus. These helps in determining the probability of events.
  • Python & R are the best languages for coding a Machine Learning algorithm.
  • Python is Object-Oriented and R is a Functional-Oriented language.
  • For a Machine Learning professional, it is a bonus to know about basic algorithms, memory management, and the linking process.
  • It is also recommended to have been familiar with Libraries like Tensorflow, PyTorch, Scikit-learn, and Jupyter.
  • Tensorflow — An open-source, free software library used for differentiable programming, and data flow, across a wide range of tasks. It is used for Machine Learning applications, like neural networks.
  • PyTorch — An open-source Machine Learning library that is based on the Torch library. It is used in several applications, like natural language processing, and computer vision.
  • Jupyter — It is a project that helps support execution environments, in several programming languages.
  1. Computational Linguist
  2. Data Scientist
  3. Designer in Human-Centred Machine Learning
  4. NLP Scientist
  5. Business Intelligence Developer
  6. Software Developer/Engineer (AI/ML)
  7. Data Analyst
  8. Data Architect
  9. Cloud Architect



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Ankit Gupta

Ankit Gupta

Data Science Enthusiast | Top 50 finalist from Jharkhand at Startup India Innovation Challenge 2018 | Digital Content Creator | Photography | Traveller