AI Skills (ML, Data Science and NLP)
- Knowledge of Python/R
- Ability to acquire, clean process data
- Understand the concept of supervised learning and its applications
- Understand the concept of unsupervised learning and its applications
- Build a simple predictor from scratch
- Build a simple classifier from scratch
- Build a simple recommendation system
- Modify some of the existing open source projects in prediction, classification, recommendation
- Model training (split the data into test and training set)
- Host the model as a web application (backend)
- Iteratively improve the model
- Understand overfitting and underfitting and how to fix the problems
- Good understanding of various available model building techniques and explain where they are applicable.
- Good understanding of various tools available in the space and their capabilities)
- 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)
- Core NLP Concepts and Terminology
- A brief introduction to Neural Networks
- Building examples of NLP tasks
- Auto Summarization
- Question/Answering Systems
- Creating and refining chat bots
- Named Entity Extraction
- Topic Modelling
- Creating Knowledge Graphs
- Text Analytics
and more…