Movie Recommendation - Deepstash

Movie Recommendation

With almost everyone sticking to their television or mobile phone screens to find out the next show to binge-watch or movie to wind up the day, it is interesting to notice how our OTT profiles recommend the right content. These recommendations are based on individual viewers’ history and preferences and are made using ML algorithms. 

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MORE IDEAS FROM 6 ML Projects That Every Beginner Should Try

Wine Quality Prediction

Wine is differentiated based on its flavour, undernotes, smell and colour. The taste of wine is acquired over time, so how do we know if one is good or not in the absence of a wine expert? Machine learning to the rescue. Beginners can use the R programming language to build an ML model to predict the quality of wine with the help of available datasets, using data exploration, regression models and data visualisation. 

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The fake news detection model will introduce programmers to the concept of text classification . This hands-on project trains the deep learning model to detect fake news from a given news compilation. Beginners can use Python for this project. 

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Sorting Tweets On Twitter

This beginner-level ML project allows programmers to create an algorithm that can scrape tweets using a natural language processor. The algorithm can determine tweets matching specific themes, keywords, or individuals. Using a sentiment analysis with a text mining algorithm will thus allow one to filter tweets quickly and seamlessly.

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Handwritten Documents To Digital File.

An easy and fun project for beginners starting out with machine learning would be building an algorithm to convert handwritten documents into digital notes or files. Also known as the offline Handwritten Text Recognition (HTR) system, this algorithm can transcribe the text in scanned images into digital text. To build this algorithm, one would need Python, TensorFlow, NumPy and OpenCV. Building this algorithm would help beginners try their hands in concepts like image recognition, deep learning and neural networks. The project uses logistic regression.

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  • If you are planning to start a career or even shift to this lucrative industry, the best way is to get hands-on experience and develop a project.

Machine learning offers an abundance of data to learn from and run models. Everybody has been talking about its applications and use cases. The machine learning market has grown manifold in the last couple of years. According to Fortune Business Insights 2020, the global ML market is expected to reach a value of $117.19 billion by 2027. In fact, as of April this year, more than 98,000 jobs listed on LinkedIn had ML as a required skill.

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Loans form an integral part of any bank. It is the loan interest from which banks earn most of their profits. Beginners can use Python to build simple ML projects for loan prediction. The project will introduce developers to concepts like logistic regression, decision tree and random forest. 

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RELATED IDEA

A recent study suggests that MOOCs do not disrupt world-class education as initially thought, nor are they accessible to the entire world.

  • Online learners are concentrated in the world's most affluent countries and neighborhoods.
  • Only 6 % of people who enroll in a course actually complete it.
  • These numbers aren't improving.

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Transfer learning is how humans use knowledge of one thing to help understand something else, for example to humans a umbrella can be used to stop rain hitting you, but it also can be used to keep the sun of you. Getting a AI to put these connections together is one of the steps towards general Intelligence, and that's where transfer learning comes in.

Transfer learning looks at the weights, biases or artitecture of the deep learning model and saves them to them transfer to another model,

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"Contagious" by John Berger

Jonah Berger is a marketing professor and world-renowned expert on word of mouth marketing, consumer behavior, and virality.

In Contagious , he shares why certain products, campaigns, and articles catch on and go viral while others don’t.

The book presents a 6-step framework to create brands and messages that are worth sharing.

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