arpit-agrawal-git Goto Github PK

100-days-of-machine-learning

demo_diagnostic_app

downstream_tasks_with_clip

Notebooks demostrating use of CLIP for various multimodel or image based downstream tasks. Also demonstrates zero shot capabilities of CLIP

duplicationcheck-patternrecognition-imagehashing

everyllm-everywhere-all-at-once

Notebooks on most popular and useful LLMs with ways to use them for specific downstream tasks

fine_tune_layoutlmv2

Fine Tuning LayoutLMv2 on FUNSD dataset for Form Understanding

glove

GloVe model for distributed word representation

image_fundamentals

langchain-notebooks

Stuff one can do with Langchain and LLMs

logistic-regression-lead-scoring-case-study

X Education sells online courses to industry professionals. The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%. Business Goal - X Education needs help in selecting the most promising leads, i.e. the leads that are most likely to convert into paying customers. The company needs a model wherein you a lead score is assigned to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance.The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.

nature_of_defect_nod_classifier

Classification Task using Sentence Transformers

nlp_with_transformers

power-bi-dashboards

semantic_processing

some-basic-python-concepts

Some intriguing concepts simulated with Python

spark_zero_to_hero

syntactic-processing

testrepo

the-best-classifier

In this notebook I have tried to use all the classification algorithms that I have learned in Machine Learning with Python course authorized by IBM.

topic-modelling-ticket-classification