How to classify text using Word2Vec
Word2Vec vectors are basically a form of word representation that bridges the human understanding of language to that of a machine.
Word2Vec vectors are basically a form of word representation that bridges the human understanding of language to that of a machine.
Transfer learning is one of the most important breakthroughs in machine learning! It helps us to use the models created by others.
BERT stands for Bidirectional Encoder Representations from Transformers and it is a state-of-the-art machine learning model used for NLP tasks like text classification, sentiment analysis, text summarization, etc.
This is probably one project which every organisation can benefit from! A lot of human effort is spent unnecessarily every day just to re-prioritize the incoming support tickets to their deserving priority, because everyone just creates them either as Priority-2 or Priority-1. This issue can be solved to some extent if we had a predictive …
Support Ticket Classification using TF-IDF Vectorization Read More »
The goal of the case study is to learn from the historical data of advertisement clicks using machine learning and create a model to Predict who is going to click on the Advertisement on a website in future based on the user behaviour and user profile.
In this case study, I will show you how to implement a face recognition model using CNN. You can use this template to create an image classification model on any group of images by putting them in a folder and creating a class.
In this case study, I will show how LSTMs can be used to learn the patterns in the stock prices. Using this template you will be able to predict tomorrow’s price of a stock based on the last 10 days prices.
How to use Deep Artificial Neural Networks for Classification use cases in Python
How to implement a Deep Learning ANN for a Regression use case in python
House price prediction using machine learning in python. Based on the famous Boston housing data.