Algorithms of the Intelligent Web | Building Intelligent Web Applications

Course Objectives

Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Working in a hands-on lab environment led by our expert instructor, attendees will explore

• 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

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional AI, machine learning, deep learning, data science, programming, Python/R and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

This course is geared for attendees with who wish 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.

Pre-Requisites:  Students 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

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most.

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 perceptrons
• 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

Course Materials

Student Materials: Each participant will receive a digital Student Guide and/or Course Notes, code samples, software tutorials, step-by-step written lab instructions (as applicable), diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.

Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment, or local installation, is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. In some cases we can also help you install this course locally if preferred. Please inquire for details and options.

Every-Course Extras = High-Value & Long-Term Learning Support! All Public Schedule courses include our unique EveryCourse Extras package (Course Recordings, Live Instructor Follow-on Support, Free *Live* Course Refresh Re-Takes, early access to Special Offers, Free Courses & more). Please inquire for details.