Want to search images of clothes which have text on them? Then this project talks through how we can classify an image whether it has text on it or not. For this we use state of the model called as inception and try and deepdive into how it works on our dataset

Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

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 contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.

Use cluster analysis to identify the groups of characteristically similar schools in the College Scorecard dataset. Considerations: Clustering Algorithm Data Preparation How will you deal with missing values? Categorical variables? Feature intercorrelations? Feature normalization or scaling? Dimensionality reduction? Hyperparameters How will you set the parameters -- the algorithm's knobs and dials, so to speak -- in order to achieve valid and useful output? Interpretation Is it possible to explain what each cluster represents? Did you retain or prepare a set of features that enables a meaningful interpretation of the clusters? Do the compositions of the clusters seem to make sense? Validation How will you measure the validity of your clustering process? Which metrics will you use and how will you apply them?

CRNNs combine both convolutional and recurrent architectures and is widely used in text detection and optical character recognition (OCR). In this project, we are going to use a CRNN architecture to detect text in sample images. The data we are going to use is TRSynth100k from Kaggle. Given an image containing some text, the goal here is to correctly identify the text using the CRNN architecture. We are going to train the model end-to-end from scratch.

In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

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 machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

We all at some point in time wished to create our own language as a child! But what if certain words always cooccur with another in a corpus? Thus you can make your own model which will understand which word goes with which one, which words are often coming together etc. This all can be done by building a custom embeddings model which we create in this project