In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.
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 project, we will cover in detail the architecture of a transformer used in natural language processing use cases. We will go through the key nlp areas in the pre-transformer stage like bow, word2vec...and then the origin and gradual refinement of transformers. Finally, we will study one of the most popular state of the art transformer models, called BERT and use it for text classification on a large dataset.
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 ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.
Use the Mercari Dataset with dynamic pricing to build a price recommendation algorithm using machine learning in Python to automatically suggest the right product prices.
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 contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.
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.