AI Skills (ML, Data Science and NLP)

 

  1. Knowledge of Python/R
  2. Ability to acquire, clean process data
  3. Understand the concept of supervised learning and its applications
  4. Understand the concept of unsupervised learning and its applications
  5. Build a simple predictor from scratch 
  6. Build a simple classifier from scratch
  7. Build a simple recommendation system
  8. Modify some of the existing open source projects in prediction, classification, recommendation 
  9. Model training (split the data into test and training set)
  10. Host the model as a web application (backend)
  11. Iteratively improve the model
  12. Understand overfitting and underfitting and how to fix the problems
  13. Good understanding of various available model building techniques and explain where they are applicable.
  14. Good understanding of various tools available in the space and their capabilities)
  15. Ability to say whether ML is needed or not for a certain application (we found this lacking in many of the protosemers we talked to)
  16. Core NLP Concepts and Terminology
  17. A brief introduction to Neural Networks
  18. Building examples of NLP tasks
  19. Auto Summarization
  20. Question/Answering Systems
  21. Creating and refining chat bots
  22. Named Entity Extraction
  23. Topic Modelling
  24. Creating Knowledge Graphs
  25. Text Analytics

 

and more…