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Recommender systems are an integral part of many online systems. From e-commerce to online streaming platforms. Recommender systems employ the past purchase pattern on its user to predict which other products they may be interested in and likely to purchase. Recommending the right products gives a significant advantage to the business. A major portion of the revenue is generated through recommendations.The Collaborative Filtering algorithm is very popular in online streaming platforms and e-commerce sites where the customer interacts with each product (which can be a movie/ song or consumer products) by either liking/ disliking or giving a rating of sorts. One of the requirements to be able to apply collaborative filtering is that sufficient number of products need ratings associated with not them. User interaction is required.
This machine learning project walks you through the implementation of collaborative filtering using memory based technique of distance proximity using cosine distances and nearest neighbours.
Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.
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.
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 spark project, you will use the real-world production logs from NASA Kennedy Space Center WWW server in Florida to perform scalable log analytics with Apache Spark, Python, and Kafka.
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 NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.
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 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.