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 Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive 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 NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.
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 data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.