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Algorithms of the Intelligent Web is a hands-on Applied Machine Learning & AI course that teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications and website logs.

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Unlimited Duration

Last Updated

March 11, 2021

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Certification

In this course you will learn about: 

· Machine learning essentials, as well as deep learning and neural networks

· How recommendation engines work

· Building applications for the intelligent web

· Extracting structure from data: clustering and transforming your data

· Recommending relevant content

· Classification: placing things where they belong

· Relevant Case Study: click prediction for online advertising

· Making the right Machine Learning choices for your web apps

· The future of the intelligent web

Course Curriculum

    • An intelligent algorithm in action: Google Now 00:00:00
    • The intelligent-algorithm lifecycle 00:00:00
    • Further examples of intelligent algorithms 00:00:00
    • Things that intelligent applications are not 00:00:00
    • Classes of intelligent algorithm 00:00:00
    • Evaluating the performance of intelligent algorithms 00:00:00
    • Important notes about intelligent algorithms 00:00:00
    • Extracting structure from data: clustering and transforming your data 00:00:00
    • Data, structure, bias, and noise 00:00:00
    • The curse of dimensionality 00:00:00
    • K-means 00:00:00
    • The relationship between k-means and GMM 00:00:00
    • Transforming the data axis 00:00:00
    • Setting the scene: an online movie store 00:00:00
    • Distance and similarity 00:00:00
    • How do recommendation engines work? 00:00:00
    • User-based collaborative filtering 00:00:00
    • Model-based recommendation using singular value decomposition 00:00:00
    • The Netflix Prize 00:00:00
    • Evaluating your recommender 00:00:00
    • The need for classification 00:00:00
    • An overview of classifiers algorithms 00:00:00
    • Fraud detection with logistic regression 00:00:00
    • Are your results credible? 00:00:00
    • Classification with very large datasets 00:00:00
    • History and background 00:00:00
    • The exchange 00:00:00
    • What is a bidder? 00:00:00
    • What is a decisioning engine? 00:00:00
    • Click prediction with Vowpal Wabbit 00:00:00
    • Complexities of building a decisioning engine 00:00:00
    • The future of real-time prediction 00:00:00
    • An intuitive approach to deep learning 00:00:00
    • Neural networks 00:00:00
    • The perceptron 00:00:00
    • Multilayer perceptrons 00:00:00
    • backpropagation 00:00:00
    • Going deeper: from multilayer neural networks to deep learning 00:00:00
    • A/B testing 00:00:00
    • Multi-armed bandits 00:00:00
    • Bayesian bandits in the wild 00:00:00
    • A/B vs. the Bayesian bandit 00:00:00
    • Extensions to multi-armed bandits 00:00:00
    • Future applications of the intelligent web 00:00:00
    • Social implications of the intelligent web 00:00:00

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