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Music is one of the most popular source of entertainment in today's era. Listening to music has become much easier due to the digital era. Few years ago many of the users used to listen to a particular artist or band, some used to love specific types of music. As the world is getting more and more into technology, users have access to various kinds of music on various platforms. Nowadays, the availability of music and music streaming services has been increasing exponentially. The public is listening to all kinds of music ranging from classical, jazz to pop.
Music streaming applications such as spotify, youtube music, amazon music have features to recommend music to the users based on their listening history and preferences. Such features play a vital role in the business of these streaming services. As the time spent on the platform is directly linked to the growth of the streaming services, appropriate recommendations are very important. The music recommendation system by which music provider can predict and suggest appropriate songs based on the characteristic of the music which has been heard by the user over the period of time
Due to the increasing number of songs, artists and kinds of music, it has become difficult to suggest appropriate songs to the user. The challenge of a music recommendation system is to build a system which can understand the users preferences and offer the songs.
In this project we use the KKBOX dataset to build a music recommendation system. This project will walk through some Machine learning techniques that can be applied to recommend songs to users based on their listening patterns.
To predict the chance of a user listening to a song repetitively after the first observable listening event within a particular time.
The dataset used is from Asia’s leading music streaming service, KKBOX. It holds the world’s most comprehensive Asia-Pop music library with over 30 million tracks. In the training data set, information of the first observable listening event for each unique user-song pair within a specific time duration is available. Metadata of each unique user and song pair is also provided. There are three datasets available.
In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
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.
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.
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
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.
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 deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.