Building Intelligent Web Applications

Building applications for the intelligent web

• An intelligent algorithm in action: 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

Extracting structure from data: clustering and transforming your data

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

Recommending relevant content

• 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

Classification: placing things where they belong

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

Case study: click prediction for online advertising

• 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

Deep learning and neural networks

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

Making the right choice

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

The future of the intelligent web

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

Join an engaging hands-on learning environment, where you’ll learn:

• 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

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work. This is not a basic class.

Before attending this course, you should have:

• Basic to Intermediate IT Skills, with some prior Python exposure if able (attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them)
• Good foundational mathematics or logic skills
• Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Developer, Data Scientist, Architect, and Software Engineer