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Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers and beginner data scientists with basic Python experience

Who should attend & recommended skills

  • This course is geared for those who want to know how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs.
  • This course is designed for developers interested in data science and for beginner data scientists.
  • Skill-level: Foundation-level Algorithms of the Intelligent Web skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment led by our Algorithms of the Intelligent Web expert instructor, participants will learn about and explore:
  • 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
  • Building applications for the intelligent web
  • Extracting structure from data: clustering and transforming your data
  • Recommending relevant content
  • Classification: placing things where they belong
  • Case study: click prediction for online advertising
  • Deep learning and neural networks
  • Making the right choice
  • The future of the intelligent web

Course breakdown / modules

  • An intelligent algorithm: Google Now
  • The intelligent-algorithm lifecycle
  • Further examples of intelligent algorithms
  • Things that intelligent applications are not
  • Classes of intelligent algorithm
  • Evaluating the performance of intelligent algorithms
  • Important notes about intelligent algorithms

  • Data, structure, bias, and noise
  • The curse of dimensionality
  • K-means
  • The Gaussian mixture models
  • The relationship between k-means and GMM
  • Transforming the data axis

  • Setting the scene: an online movie store
  • Distance and similarity
  • How do recommendation engines work?
  • User-based collaborative filtering
  • Model-based recommendation using singular value decomposition
  • The Netflix Prize
  • Evaluating your recommender

  • The need for classification
  • An overview of classifiers
  • Fraud detection with logistic regression
  • Are your results credible?
  • Classification with very large datasets

  • History and background
  • The exchange
  • What is a bidder?
  • What is a decisioning engine?
  • Click prediction with Vowpal Wabbit
  • Complexities of building a decisioning engine
  • The future of real-time prediction

  • An intuitive approach to deep learning
  • Neural networks
  • The perceptron
  • Multilayer perceptrons
  • Going deeper: from multilayer neural networks to deep learning

  • A/B testing
  • Multi-armed bandits
  • Bayesian bandits in the wild
  • A/B vs. the Bayesian bandit
  • Extensions to multi-armed bandits

  • Future applications of the intelligent web
  • Social implications of the intelligent web