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Working in a hands-on learning environment, led by our Algorithms of the Intelligent Web expert instructor, students will learn about and explore: teaches the most important approaches to algorithmic web data analysis, enabling you to create your own machine. learning applications that crunch, munge, and wrangle data collected from users, web applications, sensors and website logs. how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs.

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Last Updated

January 6, 2021

Students Enrolled

20

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Course Description

Algorithms of the Intelligent Web teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you’ll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Pythons scikit-learn. This course guides you through algorithms to capture, store, and structure data streams coming from the web. You’ll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning.
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Course Curriculum

    • 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
    • Data, structure, bias, and noise 00:00:00
    • The curse of dimensionality 00:00:00
    • K-means 00:00:00
    • The Gaussian mixture models 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 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
    • 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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