In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.
In this Kmeans clustering machine learning project, you will perform topic modelling in order to group customer reviews based on recurring patterns.
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 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.
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.
In this deep learning project, you will build a convolutional neural network using MNIST dataset for handwritten digit recognition.
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.