In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.
In this NLP Project, you will learn how to use the popular topic modelling library Gensim for implementing two state-of-the-art word embedding methods Word2Vec and FastText models.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.
In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.
In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.
Build your own image similarity application using Python to search and find images of products that are similar to any given product. You will implement the K-Nearest Neighbor algorithm to find products with maximum similarity.
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.