# Machine Learning - Level 1 Module 4 - Sachitt Arora

**URL:** <http://staging.stemaway.com/t/machine-learning-level-1-module-4-sachitt-arora/9111>\
**Category:** Machine Learning Pipeline (NLP) (CE) 2021\
**Created:** [August 1, 2021, 12:55am UTC](http://staging.stemaway.com/t/machine-learning-level-1-module-4-sachitt-arora/9111 "2021-08-01T00:55:11Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Sachitt\_Arora](https://d3rzmptxz7fqzy.cloudfront.net/user_avatar/staging.stemaway.com/sachitt_arora/32/3405_2.png) [@Sachitt\_Arora](http://staging.stemaway.com/u/Sachitt_Arora)\
**Post date:** [August 1, 2021, 12:55am UTC](http://staging.stemaway.com/t/machine-learning-level-1-module-4-sachitt-arora/9111/1 "2021-08-01T00:55:11Z")

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Technical Area:

- Learned how to train BERT, xlnet, roberta, distilbert models using the Simple Transformers library
- Was able to combine various models such as BERT and Random Forest and see their overall effectivness.
- Learned how BERT operates and how it can function

Tools:

- Simple Transformers
- tokenizers
- Docker
- SKlearn
- Jupiter Notebook

Achievement Highlights:

- Was able to train various models and see what their relative efficiencies were.
- Tried using different types of data, such as cleaned, cleaned, and not cleaned, to see which one was more effective.
- Tried dockerization of ML App with the advanced models → currently still having issues with this and is one of my main challenges.

Challenges:

- As the modules are becoming more complicated, a better understanding of the ins and outs of machine learning is needed to progress. Even though I had a lot of trouble at first, I was eventually able to obtain the knowledge I needed to complete this module.